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ISSUE 39 DISCOVER BETTER DESIGNS. FASTER.
FULL STEAM AHEAD:ENGINEERING SIMULATION MAKES WAVES IN THE MARINE INDUSTRY
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139
REMEMBERING STEVE
It is with profound sadness that we report the passing of our President, CEO and co-founder, Peter “Steve” MacDonald who died on Wednesday, September 2, 2015.
In 1980, Steve led a group of three engineers who took the “computer simulation” techniques they had been using in the nuclear industry and applied them to more general engineering problems. The result was “adapco”, the Analysis Design and Application Co., which would later become CD-adapco.
From those humble beginnings — three engineers working in a New York attic — CD-adapco flourished over the next 35 years under Steve’s inspirational leadership, and today employs over 900 people in 40 different offices around the world.
Throughout his leadership, Steve passionately pursued his vision of “no-compromise” engineering simulation applied to real world products and processes. In fulfilling that vision, Steve’s primary objective was to achieve his customer’s goals and to push the capabilities of CD-adapco’s software products beyond the merely possible. Steve regarded his employees as CD-adapco’s most valuable asset, and always worked hard to recruit the best and brightest engineers and software developers.
Steve will be remembered as a pioneering engineer, a visionary and a charismatic leader. It is almost impossible to calculate the full extent of his legacy. CD-adapco’s software tools have been used to improve products and processes that touch every part of our lives. The design of almost every automobile on the road today has been
in�uenced by STAR-CCM+ or STAR-CD. There are people who would not be alive today if it were not for CD-adapco’s software. Not only is STAR-CCM+ used to increase the safety of everything from airplanes to nuclear reactors, but it has also been used to improve a range of healthcare outcomes, for example, by aiding the design of improved infant incubators.
A devoted husband, father and grandfather, Steve will be sorely missed.
Although the company is mourning his passing, Steve’s legacy is alive in a healthy organization of talented and motivated individuals who are carrying forward his vision and continuing his customer-�rst business model.
Sharron L. MacDonald succeeds Steve MacDonald as the Interim President and CEO of CD-adapco worldwide.
For more information on the history of CD-adapco, and Steve MacDonald’s role in the company, please read the “CD-adapco Origins” story in Dynamics 38: www.cd-adapco.com/dynamics38 or www.cd-adapco.com/origins
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58-63AEROSPACEQUIET, RELIABLE & EFFICIENT!
64-69ELECTRONICSMORE POWER TO YOU!
34-41OIL & GASOPTIMIZATION OF AN OFFSHORE PLATFORM ORIENTATION
32-33MARINEDR. MESH TALKS EHP
CONTENTS
EDITORIAL Dynamics welcomes editorial from all users of CD-adapco software or services. To submit an article: Email: [email protected] Telephone: +44 (0)20 7471 6200
EDITOR Anna-Maria Aurich - [email protected]
ASSOCIATE EDITORS Prashanth Shankara - [email protected] Eppel - [email protected] Sahar Fazli - [email protected] DESIGN & ART DIRECTION Ian Young - [email protected]
PHOTOGRAPHYFranco Consales - [email protected]
4-5INTRODUCTIONDEJAN RADOSAVLJEVIC
70-75ENERGY & POWERAIR POLLUTION CONTROL
6-7FROM THE BLOGA QUANTUM LEAP FORWARD
8-9FROM THE BLOG STAR-CCM+ NEW COLORMAPS
10-11FROM THE BLOG SUPERCHARGE YOUR PRODUCTIVITY
12-13FROM THE BLOG CPI LOOKS TO THE FUTURE
14-15FROM THE BLOG ALL FIRED UP ABOUT DARS
DYNAMICS 39
28-31MARINEENERGY SAVING DEVICES
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PRESS CONTACT US: Todd Mavreles - [email protected] Europe: Julia Martin - [email protected] ADVERTISING SALES Geri Jackman - [email protected] EVENTS US: Lenny O’Donnell - [email protected] Europe: Sandra Maureder - [email protected]
SUBSCRIPTIONS & DIGITAL EDITIONS Dynamics is published twice a year, and distributed internationally. All recent editions of Dynamics, Special Reports & Digital Reports are available online: www.cd-adapco.com/magazine We also produce our monthly e-dynamics newsletter which is available on subscription. To subscribe or unsubscribe to Dynamics and e-dynamics, please send an email to [email protected]
48-51GROUND TRANSPORTATIONSIMULATING REAL-WORLD AERODYNAMICS FOR CARS
76-80LIFE SCIENCESINTERVIEW: SIMULATE, COLLABORATE & REGULATE
DYNAMICS 39
82-86MANUFACTURINGOPTIMIZING COMPONENT DESIGN
52-56GROUND TRANSPORTATIONNISMO TAKES THE LEAD WITH STAR-CCM+
18-21MARINENUMERICAL TOWING TANKS
22-27MARINECONFLICTING OBJECTIVES IN SHIP DESIGN
42-46GROUND TRANSPORTATIONCOME RAIN OR SHINE
16FROM THE BLOG THE TRUE COST OF PARALLEL COMPUTING
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INTRODUCTION DEJAN RADOSAVLJEVIC
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DEJAN RADOSAVLJEVIC INTRODUCTION
INTRODUCTION: CFD IS MAKING WAVES IN THE MARINE INDUSTRY
As we celebrate 35 years of the birth of CD-adapco, it is interesting to re�ect on the history of CFD within the marine industry, which is much shorter in comparison.
That may sound surprising considering marine as one of the �rst means of transportation in human history, however, that is probably also the main reason it took so long to take note of CFD. Throughout history, the main focus of building �oating objects, be it canoes, dinghies, boats, yachts, ships and so on was to make sure they are safe on water and retain structural integrity under any conditions, i.e. do not break and sink. Learning initially through trial and error, and as engineering developed through more scienti�c methods, all this accumulated knowledge was with time embedded in classi�cation rules, governing structural integrity of ships in commercial use. This combination of experience and rules, while indeed going a long way towards making ships extremely dif�cult to sink, did little to eliminate a myriad of smaller-scale issues: vibrations, cavitation on propellers and rudders, erosion of appendages, poor performance, poor controllability, and so on.
During my 20 years of working for Lloyd’s Register Technical Investigation Department (LR TID) I was (un)fortunate enough to encounter numerous examples of such issues. Due to my
CFD background prior to joining LR TID, I started applying CFD simulations to help us understand the reasons behind each failure. Apart from giving us a much better insight into the reasons for failures, which made it possible not only to devise remedial measures but also to test them on the computer prior to applying on a ship, it made one thing glaringly obvious: If you apply CFD at the design stage, you could “design-out” most, if not all future failures. However, at that time, late ‘90s and early ‘00s, there were no incentives strong enough for the marine industry to change their established way of designing ships and they continued with traditional methods for some more years.
In the background I was enjoying a step-change in “automated” meshing with the appearance of trimmed cell meshing in STAR-CD® (or pro-am as it was known in the late 90s), which allowed a signi�cant reduction in the time needed to do meshing of complex geometries and helped provide results with ever increasing accuracy. Good comparisons against full scale observations, validations against experiments and full scale measurements started to multiply. Industry �nally started to take note. Leading players within the marine industry started to take CFD much more seriously. But it was really not until the combined effects of oil prices shooting through the roof and climate change
forcing implementation of some stringent emissions regulations that they were fully exposed to CFD’s tremendous power. Yes, increase in computing power helped as well, and it was fortuitously good timing, but computing power did not prove an obstacle to the aerospace and automotive industries which took full advantage of CFD a good ten years before the marine industry.
Today, CFD within the marine industry is well recognized and its application to a wide range of design problems is constantly growing, as selected articles in this issue of Dynamics show. Similar to other transportation sectors, marine companies have recognized the tremendous power that CFD gives them to innovate through applying sophisticated multi-physics simulation capabilities of STAR-CCM+® to carry out MDX, or to add HEEDS for full automatic optimization, or, in the case of marine, apply EHP, a tool tailored speci�cally for marine industry clients.
The future for the marine industry �nally looks less stormy.
DEJAN RADOSAVLJEVICDirector, MarineCD-adapco
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FROM THE BLOG A QUANTUM LEAP FORWARD
JOEL DAVISONProduct Manager STAR-CCM+CD-adapco
FIGURE 1: A Boeing 787 Dreamliner with highly �exible wings
Growing up (30 years or so ago now), shopping for weekly groceries was nothing like it is today. I was born and raised in a small village outside London and getting food for the week meant visiting many different shops, the butchers for our meat, green-grocers for vegetables and so on. Now I have my own family and I do all my shopping in one place: the supermarket. At this one large store, I can get my vegetables, meat, household items and even electronics and clothing. Unless I decide to buy a new HD TV on the spot, this bene�ts me in many ways; My costs are reduced as I don’t have to
A QUANTUM LEAP FORWARD: COMPUTATIONAL SOLID MECHANICS (CSM) IN STAR-CCM+
burn fuel driving around town to different shops, and the time it takes me to shop is far less.
This evolution of the retail experience is indicative of a wider trend of being able to perform multiple functions from a single source. Just look at the smartphone which provides a telephone, camera, web browser and even counts your steps as you are shopping at the supermarket. So what has this got to do with CAE software? Well, the important point I’m trying to make is that integration is revolutionizing the way we do things! If you have a tool that performs multiple functions from the same familiar environment, it opens up the possibility to not only do more, but do it better and faster as well.
As we have developed STAR-CCM+®, we have always worked to provide a tool based on the philosophy that integration allows the engineering analyst to cut down simulation time and cost while expanding application scope. With v10.04, we make
a huge leap forward in these aims, with the release of a �nite element solid stress solver to provide computational solid mechanics capabilities from the same integrated user interface you are already very familiar with.
So why did we develop this solid mechanics capability in STAR-CCM+? You don’t have to look very far to see real-world examples where the interaction between structures and �uids is critical. A great example is the Boeing 787 Dreamliner whose wings �ex much more than conventional aircraft due to their composite construction. By understanding the in�uence of the air�ow on the wing, Boeing was able to design wings that were not only aerodynamically but also structurally ef�cient leading to signi�cantly reduced fuel consumption and costs.
Our users have been interested in performing FSI analysis for many years but often the effort of learning and using two different tools as well as getting them
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A QUANTUM LEAP FORWARD FROM THE BLOG
to communicate effectively has been time, and accordingly cost prohibitive. For this reason, we felt that providing capabilities for solving and coupling both �uid and solid mechanics problems in a single interface would enable for our users to expand their simulation capabilities into applications that were previously out of reach.
You might reasonably ask why we are moving to a �nite element approach when the rest of STAR-CCM+ is built around the �nite volume technique? The change comes from a desire to provide the best and most appropriate tools for the physics being studied from within a unique and easy to use interface. To ensure this is realized, CD-adapco has invested heavily to deliver an entirely new framework that can sustain future growth and enhancement to the �nite element solver. Now this work is complete, for the �rst time, you have access to industry standard �ow and structural analysis technology without having to swap between different tools and learn different interfaces.
You may not know this but the CCM part of STAR-CCM+ stands for Computational Continuum Mechanics, named as such to reflect the vision
for providing a truly multidisciplinary analysis tool. With the new release of STAR-CCM+ the name is more appropriate than ever before.
FIGURE 2: Stress analysis of a marine propeller in STAR-CCM+
FIGURE 3: CSM analysis in STAR-CCM+ on a platform showing stress on the component
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FROM THE BLOG STAR-CCM+ NEW COLORMAPS
We know a good scientific visualization when we see one – colormaps are a big part of this and when used effectively, they can make all the difference. The ability to create your own colormap within STAR-CCM+® has been around for a long time and around this time last year, we delivered a full featured interactive colormap editor with the latest release at the time.
Still, what makes for a good colormap is not common knowledge. Somewhat surprisingly, the most frequently used colormap for scientific visualization, the rainbow colormap, is regarded as one of the least effective. If this is news to you, guess what: Rogowitz & Treinish [1] were calling attention to the shortcomings of the rainbow colormap twenty years ago. Since then, several well considered, provocatively titled articles like “How the Rainbow Color Map Misleads”, “Rainbow Color Map (Still) Considered Harmful” and even “Dear NASA: No More Rainbow Color Scales, Please” have further exposed the limitations of the rainbow colormap. So, now that we know the rainbow colormap shouldn’t be our first choice, what alternatives do we have?
STAR-CCM+ PREVIEW: NEW COLORMAPS & CUSTOMIZATION
Drawing from very well considered colormap requirements summarized by Moreland [5], we’ve put together 22 new colormaps and added them into STAR-CCM+, ready to use. These new colormaps can be grouped into four categories: High-Impact, Perceptual, Diverging and Specialty. One of the primary concerns for many of us, and equally to authors in this area of research, is that colormaps should be visually pleasing. We’ve balanced contradictory color selection best practices against aesthetics, delivering several High-Impact colormaps that are minimally distorted by lighting and are well suited to a wide range of applications. The Perceptual green-mauve, orchid-green, tropical and water colormaps are the easiest to interpret and are of particular bene�t to individuals with color blindness such as deuteranopia, protanopia and tritanopia. Diverging colormaps highlight either large differences (purple-red and purple-red basic) with an unsaturated color at the center position of the colormap, or small differences (red-blue and red green) with nearly unsaturated colors placed at the ends of the colormap. Lastly, Specialty maps like the land elevation, land-sea elevation, casting and thermal ramps are designed for speci�c applications. We anticipate that these new colormaps will streamline your work�ows by removing the need to create custom colormaps of your own. We’ve even made it possible for you to specify your own default colormap through our Tools > Options > Visualization settings (shown below) for everyday use.
The next topic for today: customization. As you gain familiarity with STAR-CCM+, it’s likely that you’ve authored a Java macro (or two) to save you time on daily, routine, specialized tasks. But as new projects come in, it’s easy to forget “where” these various Java macros are and “what” they actually do. Even if your scripting efforts are well organized, you still need to be able to �nd and play these “time-savers”. With the new release of STAR-CCM+ you will now be able to run any one from up to ten Java macros, directly from any toolbar. You can even specify an icon and a hover tooltip to remind you what your macro does.
During the course of STAR-CCM+ development, I wrote two Java macros that I came to rely on heavily. The �rst cycles through all of the candidate colormaps for a chosen scalar displayer, saving an image for each colormap to a speci�ed folder. This lets us make comparisons between many colormaps we had under consideration and substantially streamlined our efforts to arrive at the �nal 22 colormaps that we’ve included.
The second macro was created to provide enhanced �exibility for diverging colormaps. Our diverging maps associate a critical scalar value with the center position of the colormap. To correctly visualize +/- differences, the scalar minimum and maximum extents need to be equidistant from a median scalar value. To see how this can be used in practice, let us look at the radial velocity component in the vicinity of a mixing impeller. In the leftmost illustration of
MATTHEW GODOProduct Manager STAR-CCM+CD-adapco
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STAR-CCM+ NEW COLORMAPS FROM THE BLOG
FIGURE 1: Examples of the four colormap categories in STAR-CCM+
1. B.E. Rogowitz & L.A. Treinish: “Why Should Engineers and Scientists be Worried About Color?”, IBM Corporation (1995), Retrieved from http://researchweb.watson.ibm.com/people/l/lloydt/color/color.HTM2. R. Kosara: “How the Rainbow Color Map Misleads” (2013), Retrieved from http://eagereyes.org/basics/rainbow-color-map3. D. Borland et al.: “Rainbow Color Map (Still) Considered Harmful”, IEEE Computer Graphics and Applications, 27 (2007), p 14-174. D. Skau: “Dear NASA: No More Rainbow Color Scales, Please” (2012), Retrieved from http://blog.visual.ly/rainbow-color-scales/5. K. Moreland: “Diverging Color Maps for Scienti�c Visualization”, Retrieved from http://www.sandia.gov/~kmorel/documents/ColorMaps/ColorMapsExpanded.pdf
Figure 1, a divergent map effectively shows where �uid is being pushed outward (in red) away from the shaft, or inward (in blue) towards the shaft. The scalar minimum and maximum extents (+/- 0.80) are set equidistant from the critical radial velocity component (equal to 0). However, variations in the negative radial velocity are dif�cult to see. In the center illustration, we’ve changed the minimum extent to pick up more detail but the white color in the middle of colorbar is no longer associated with the critical radial component. The larger blue region incorrectly implies that there is more �uid moving toward the impeller shaft than there really
is. To �x this, we run the second macro, right from our toolbar, which shifts all the control points in the colormap to account for the offset of the critical radial component using the modi�ed scalar extents (-0.40 to +0.80). Once again, our critical scalar lines up with the neutral white color. The end result at right shows enhanced detail for the variation of the radial velocity component.
With every STAR-CCM+ release, we pay close attention to all aspects of your productivity. The newly added colormaps should help you to quickly create high impact and effective scienti�c
visualizations of your results and you can certainly use them as a starting point. I am not sure how big a hint I am allowed to drop, but my suggestion is that if you want to win the annual calendar contest by CD-adapco, you should investigate using alternative colormaps in your submission. Finally, we need to appreciate that a Java macro can be capable of saving us a tremendous amount of time with a little investment up front. Making your macros accessible has been a long standing feature request that we are pleased to be able to deliver this time around.
FIGURE 2: Screenshot from STAR-CCM+ showing the new colormap options
FIGURE 3: Screenshot from STAR-CCM+ showing macros accessible via the default toolbar
FIGURE 4: Example of enhanced �exibility for diverging colormaps via macros on a mixing impeller
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FROM THE BLOG SUPERCHARGE YOUR PRODUCTIVITY
DAVID MANNProduct Manager STAR-CCM+CD-adapco
As a product manager for STAR-CCM+®, I spend a big chunk of my time traveling the globe. I am also based in a part of the world that is short of runway space, so I spend considerable amounts of time going round in circles, feeling stacked in some kind of airborne to do list. During one such recent occurrence, whilst wondering if air traf�c control were having a collective tea break, or had possibly gone home, I began to ponder the negative effect this must have on the
productivity of the hundreds of people circling with me. For I was not alone in my frustration, in addition to my fellow passengers, out of the window I could see dozens of other planes pirouetting in some kind of badly choreographed Swan Lake. How much more productive we could all be if only there was a way to get more directly to where we needed to be… teleportation, hmmmm…
Whilst teleportation may not be feasible in reality, and likely would lead to an increase in claims for lost luggage, it is certainly something we can use in simulation. In the upcoming release of STAR-CCM+, we have implemented a feature to do exactly that. The new equilibrium motion type for DFBI bodies is used for simulating the motion of moving bodies that tend towards a steady state equilibrium position. With this feature the body is incrementally “teleported” to the
current best estimate of the equilibrium position rather than allowing it to oscillate freely about it.One of the main application areas to bene�t from this new feature is that of marine “sink and trim” simulations. The marine industry, in common with most others, has increasingly come to rely on simulation where traditional methods such as tow tank tests can no longer be used in isolation due to shorter and shorter design cycles. Simulation must therefore be used in the most ef�cient manner possible if engineers are to gain the maximum value from it.
For those of you not familiar with marine tow tank tests and related simulations, in a “sink and trim” simulation, we wish to �nd the depth to which the vessel sinks in the water due to the balance of buoyancy forces and weight, together with the pitch, yaw and roll angular
STAR-CCM+ PREVIEW: SUPERCHARGE YOUR PRODUCTIVITY WITH EQUILIBRIUM DFBI BODY MOTION
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SUPERCHARGE YOUR PRODUCTIVITY FROM THE BLOG
FIGURE 1: Simulation of �ow and vessel motion using DFBI capability of STAR-CCM+
positions the vessel assumes given the �uid moments acting on it. The �gure below shows a typical Kelvin wave pattern around a vessel in a “sink and trim” simulation.
This feels like it should be a steady state simulation, but traditionally such simulations have been carried out as transient runs. The vessel is allowed to find its position only after bobbing up and down seemingly endlessly about the final resting position. Typically, however, we are not interested in how the vessel arrived at its final position, but just what that final position is, and the transient simulation is just a means to an end, albeit a computationally expensive one.The new Equilibrium DFBI body motion option short circuits this whole process, substantially reducing the time taken to find the equilibrium
position of moving bodies. The method uses a pseudo-steady approach to incrementally displace the DFBI body to the current best estimate of its equilibrium position given the forces and moments on it. The position of the body is then maintained whilst the flow stabilizes around the new position. This process is then repeated until after a few updates the body settles in its final equilibrium position, in a fraction of the overall time taken by the free motion transient approach.This approach is applicable to cases where a stable equilibrium exists, and forces and moments are a function of position.
So to an example…The graph in the �gure above shows the average vertical position of a vessel in a “sink and trim” simulation. It highlights the typical bene�ts of the new method compared to a free motion transient
approach. In the traditional free motion simulation (blue) we can see that the oscillations in the vessel position take a considerable time to damp out before the �nal position is determined. Using equilibrium DFBI body motion option (red), however, after a small number of updates, and much reduced time, the vessel �nds the same equilibrium position.In performance tests on a standard marine test case (KCS hull) with 1.35M cells on 11 cores, the new equilibrium DFBI motion showed an approximately 7x speed up over the traditional free motion transient approach, reducing a 34.2 hr simulation down to 4.9 hrs.Equilibrium DFBI motion is only one example amongst many in STAR-CCM+ aimed at improving your productivity as engineers by reducing the time it takes you to solve problems using simulation.Stay tuned to the CD-adapco news for all the latest updates.
FIGURE 2: Convergence of sinkage in a simulation using free motion DFBI (blue line) and equilibrium DFBI (red line) features of STAR-CCM+
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ASHKAN DAVOODIAccount ManagerCD-adapco
FROM THE BLOG CPI LOOKS TO THE FUTURE
Recently, I sat down with Alex Smith from the Centre for Process Innovation (CPI), a UK-based technology innovation center that uses knowledge in science and engineering combined with state-of-the-art facilities to enable their clients to prototype and scale up the next generation of products and processes. Alex is a recent recruit who is just approaching his �rst work anniversary with CPI. He is a Senior Process Engineer within their Industrial Biotechnology and Bio-re�ning (IB&B) unit and it hasn’t taken him very long to get his hands dirty with simulation work. He is currently balancing his time mostly between developing their CFD capability, both in model development and training of future users,
CPI LOOKS TO THE FUTURE
and working on some more “standard” process engineering tasks such as plant improvement and troubleshooting exercises.
This sounds like a very sensible and sustainable approach to developing a simulation capability, particularly in a company that is investing in CFD simulation for the �rst time. I asked Alex why CPI had decided to invest in a CFD simulation capability. “As a company in our position, assisting with the development of exciting and often unusual new processes,” he said, “it is important that we have access to cutting-edge tools and techniques to help us get the best results for our customers. With that in mind, the company placed a focus on improving our modeling and simulations capability in general and the introduction of CFD modeling is just one part of this ongoing capability development.”
PHILOSOPHY OF SIMULATIONWhen asked about CPI’s philosophy of simulation, Alex was at pains to point out that CFD is a tool that builds upon traditional expertise, and that it supports but does not replace it: “When selecting
conditions for trial runs, we primarily rely on the experience and judgment of the technical and operations teams. A validated model for a piece of equipment allows us an additional insight into the likely outcomes of a trial before it is run, and gives those experienced people extra tools and information to help them to select the most appropriate trials to run.”
In a similar vein, CPI’s CFD simulations are being used to improve physical testing, not replace it: “Computer-based simulations can’t replace real-life testing. The purpose of the model is to improve the focus of the live testing in order to make the process more ef�cient, by giving an impression of how the equipment will perform before testing begins. This way the live testing can begin from a more appropriate starting point and less time will be spent running unnecessary or inappropriate trials on the equipment.”
FUTURE DEVELOPMENTAlthough this CFD capability is still rather new to CPI, Alex and the team have a clear vision for the future development of their CFD capabilities across the business: “At CPI, the �rst application for
FIGURE 1: Alex Smith, Senior Process Engineer at CPI
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CPI LOOKS TO THE FUTURE FROM THE BLOG
CFD modeling is to generate validated models of our existing equipment for the purposes of improving our understanding of the equipment we currently operate, and therefore allowing us to simulate other scales outside of the available plant scales here on site. Initially, the focus is to develop models which can accurately predict mixing behavior in our stirred tank reactors, and in the future we will also look into Oxygen transfer and other key process attributes.”
Eventually, CPI aims to expand the use of CFD modeling outside of IB&B, to the other business units within the company. The business units of CPI all operate in different �elds so it is important that the simulation software they use is adaptable and can cover a wide range of conditions and problems. Alex said: “Within IB&B we aim in the long term to use CFD modeling as a design validation tool for new and novel reactor designs, whilst also using the package for troubleshooting and general plant performance improvements.”
Alex pointed out that one of the most interesting features of working in a company like CPI is that you never really know what’s coming next. He added: “The objective of the company is to help people and companies to develop their processes from laboratory to production scale, so the challenge really is to be able to constantly adapt to new processes and ideas which are coming out of universities and SME’s.”
WHY STAR-CCM+®?Before choosing a CFD provider, Alex and the team at CPI went through a fairly extensive software selection process that �nally resulted in them choosing STAR-CCM+. Alex said: ”There are a few
features in STAR-CCM+ which we �nd particularly bene�cial, for example live monitoring of a solution as it develops can be a big time saver as it allows us to quickly see whether a model is on track or not without having to wait for the run to �nish.” Users at CPI also have been particularly impressed by the ease of use of the package in general: “The all-in-one window approach keeps things simple and tidy and is very intuitive to use,” Alex said.The fast pace of product development and the willingness to develop the program based on customer demand (IdeaStorm) is a particularly strong bene�t for CPI. Alex commented: “We can see that the capabilities of the software are constantly improving and looking back over previous releases, more often than not each new release comes with new features which we will �nd useful, rather than just super�cial improvements.” He also stated that having a Dedicated Support Engineer
(DSE) is particularly bene�cial to them: “We can be con�dent that when we do need to rely on technical support, our engineer will have a good understanding of our needs.”
When I asked Alex to comment on what sets STAR-CCM+ apart for CPI, he didn’t hesitate to say: “For us it is not necessarily the physics models, it is more the ability to adjust those models on the �y, whilst monitoring the effect of those adjustments on the solution as you go. This way whilst a model is running, the physics conditions and model selection can be re�ned, and the effects of those re�nements can be visualized instantly. Combined with the very strong post-processing capability of STAR-CCM+, this allows us to develop models which closely mimic reality relatively quickly, and without the need for a time-consuming iterative process to re�ne mesh and physics settings.”
FIGURE 2: Industrial Biotechnology and Bio-re�ning (IB&B) at CPI
FIGURE 3: Predicted 3D �ow pattern around impellers of a mixing tank using STAR-CCM+
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FROM THE THE BLOG ALL FIRED UP ABOUT DARS
KARIN FROJDPrincipal Development Engineer DARSCD-adapco
FIGURE 1: Turbulent �ame simulation using STAR-CCM+
Prometheus (which literally means “forethought”), was a titan whom Zeus had defeated. He was given a task to go to earth and make its creatures. He crafted human beings and gave them arts and culture. But they were suffering and terribly shivering in the cold. So against Zeus’ direct wishes, he also gave �re to mankind. Little did humans know that this gift meant literally they would have to “play with �re” in order to have control over the wild rage, as wise as they were, humans started to investigate it in more
GET ALL FIRED UP ABOUT DARS!
scienti�c ways. But they found it wasn’t very easy. In fact it was so complicated, even schoolbook combustion examples would have stiff and dif�cult to solve ODE systems…
Sometimes I wonder why on earth I chose to work in reacting flow. Prometheus may have wanted us to play with fire, but it is just so complicated! Not only are reacting mixtures stiff ODE systems because of the very short timescales involved in the chemistry, the combustion in in-cylinder combustion engines or industrial flames contain 1,000s of species interacting through 10,000s of reactions. Imagine for example having to resolve the turbulent flame shown in the image above, using a detailed CFD grid with full chemistry and including proper turbulence-chemistry interaction. This would be extremely CPU expensive and thus impractical.
So what do intelligent humans do when solving a problem takes too much time and requires too many resources? They make simpli�cations to �nd the answer ef�ciently and there are a lot of possible ways to use simpli�cations for simulation of reacting �ows:1. Reducing chemistry complexity, using a
global single step or very small chemical schemes or using online reduction,
2. Reducing local resolution by calculating, for example, representative �amelets for the whole domain,
3. Reducing generality, by pre-tabulating chemistry,
4. Any combination of the abovementioned methods.
I think the answer to why I continue to be passionate about reactive �ows lies in the complexity of the subject itself. It is fascinating to work in an ever changing �eld where research is very active and you get to see constant progress for industrial applications. We are quickly advancing
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this �eld, and this is demonstrated in the latest version of DARS.
In DARS™ we have chosen to focus on tackling the third item in the list above: pre-tabulating chemistry using libraries. This offers an effective way of nailing down all the information of the detailed chemistry in a convenient and CPU ef�cient format. The generality reduction is handled by making it easy to generate and use new libraries for a new fuel, a fuel blend or a parameter range, making it possible to explore new designs faster and earlier in the design cycle. DARS now supports this by native export of libraries for the common in-cylinder combustion models in STAR-CD® (ECFM-3Z, ECFM-CleH and PVM-MF) and the STAR-CCM+® combustion model FGM. For in-cylinder combustion, dual fuel is supported, enabling simulation of dual fuel engines. In addition, you can enhance your understanding of soot formation and consumption by generating soot libraries for STAR-CD as well as for STAR-CCM+.
What is really cool is that a completely new GUI module has been created for library generation, which makes it possible to set up a full library calculation in less than ten (!) minutes.
After the setup is finished, you pass the calculation to any number of cores and let it run until the library is finished. The massive parallelization makes is possible for you to create new libraries fast, speeding up your virtual design work. The calculations can take from as little as ~15 minutes up to several days depending on number of cores, type of library, grid density, chemistry and whether single or dual fuel is used. You can run the calculations directly from the GUI, or execute them through a built-in command line functionality and an automatically generated script. This gives you the power to modify input data, make reruns, calculate the library on clusters and automate these calculations through scripting. All this makes your simulation workflow smoother and faster. When the library creation is done, just grab the library files and import them in your STAR-CD/ STAR-CCM+ calculation.
By creating a new library, you can capture the combustion of new fuels or fuel qualities, or the same fuel at a different operating condition. Or simply re�ne the accuracy of your calculations by creating a library with a more re�ned grid. For
example, the quality of a natural gas fuel has a large impact on the ignition delay time, see Figure 3. The ignition delay time differs more than 20%, corresponding to ~1 crank angle degree an in-cylinder combustion engine, and the point of shortest ignition delay shifts in equivalence ratio. This is an effect that may change the end-gas knock behavior in a natural gas engine, and it can only be captured by complex chemistry, as it is the amount of higher hydrocarbons that deduce the ignition delay time. To capture this effect, different libraries for varying fuel quality can be produced, and hence you can virtually test many natural gas qualities in your engine.
Thinking about it, chemical engineers really are fulfilling the forethought of Prometheus! Through simulation, we are slowly starting to understand the wild rage of combustion and chemical kinetics and we are able to discover better designs faster. I may not be able to see the future but I am certainly excited to be part of it!
So please, go ahead and download DARS from the Steve Portal today, to explore the new possibilities in library generation!
FIGURE 2: Library generation through GUI of DARS FIGURE 3: Variation of ignition delay time for different higher hydrocarbon percentage in natural gas fuel
"What is really cool is that a completely new GUI module has been created for library generation, which makes it possible to set up a full library calculation in less than ten minutes!"
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FROM THE BLOG THE TRUE COST OF PARALLEL COMPUTING
STEPHEN FERGUSON Director Marketing ContentCD-adapco
CD-adpaco’s power license allows you to access as many cores as you like for a single �xed fee.
To paraphrase Oscar Wilde: “To break one world record in a month is unfortunate, to break two in quick succession looks like carelessness.”
Just weeks after announcing that we had broken a “world record” by scaling STAR-CCM+® across 55,000 cores on the 1.045 PetaFLOP Hermit cluster, we are pleased to announce that we have smashed it already, by scaling up to 102,000 cores on NCSA’s Blue Waters supercomputer, which included running a 1 billion cell aerodynamics simulation. Blue Waters is one of the most powerful supercomputers in the world*, and is the fastest supercomputer on a university campus.
Of course, lots of CFD vendors have claimed a “world record” from time to time. And I am sure someone will break it again soon. But what does any of this mean in the real world? How does it help you as an engineer?
Obviously it is a good thing that vendors like CD-adapco (and our competitors) are worrying about the scalability of simulation software across massive numbers of processors. Even though this technology might seem out of reach right now, the unwavering power of Moore’s Law will ensure that computing, even on this seemingly massive scale, will be commonplace much
sooner than you think (trust me here, I am old enough to remember when two processors seemed like a lot).
However, when reading boastful claims such as these, the question that you really need to ask yourself, is “Even if I had access to thousands of cores, could I afford the CAE licenses required to run a simulation on them?”
The answer - with one important exception - is that you couldn’t. You couldn’t afford to run on 100,000 cores, 10,000 cores, or even 1,000 cores (depending on how large the company you work for is).
This is because most CAE vendors (and CFD vendors in particular) still base their licensing model around the broken paradigm of “the more you use, the more you lose,” charging you per core instead of per simulation. There is an almost linear** relationship between the cost of your license, and the maximum number of cores that you are allowed to utilize in your simulation. The truth is, that for most CFD software, price scales much better than code performance.
The exception to this rule is CD-adapco. Since we �rst introduced our “Power Session” license over �ve years ago, the licensing cost of running a STAR-CCM+ simulation on Blue Waters or Hermit, would be no different than running on your local cluster***. Using 100,000 cores costs exactly the same as running on ten cores (say).
STAR-CCM+ POWER LICENSINGThis is unique among CFD vendors, and offers a huge advantage to our users. Unlike the users of any other commercial
CFD code, you are free to deploy your simulations on as many processors as you can lay your hands on, without having to worry about arti�cial licensing limits imposed by your vendor and your �nance department.
This is particularly important as we enter the world in which multidisciplinary simulation is the norm. Capturing of all the physics that de�ne your problem usually results in higher cell counts. We don’t want you to compromise your modeling choices, because you can’t afford to run simulations on the computing resources that you’ve already paid for.
I’ll be writing more about our licensing in the future, including how Power-on-Demand, and Power Tokens offer you even more flexibility in the way that you deploy your computational and licensing resources.
* It is difficult to say where it ranks exactly, because NCSA choose not to participate in ranking schemes such as TOP500.
** It is true that the relationship between number of cores and total license cost isn’t exactly linear, as your kind sales rep will probably cut you a deal that means that you’ll pay slightly less per core for a huge number of cores. But you’d still pay orders of magnitude more for 10,000 cores than you would for say ten.
*** You might need to have a word with your boss about the electricity bill you’d incur in the process.
WORLDRECORDS ANDTHE TRUE COSTOF PARALLEL COMPUTING
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3 MEN IN AN ATTIC GROUND TRANSPORTATION3 MEN IN AN ATTIC GROUND TRANSPORTATION
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FLOW − THERMAL − STRESS − EMAG − ELECTROCHEMISTRY − CASTING − OPTIMIZATIONREACTING CHEMISTRY − VIBRO-ACOUSTICS − MULTIDISCIPLINARY CO-SIMULATION
DISCOVER BETTER DESIGNS. FASTER.
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MARINE NUMERICAL TOWING TANKS
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Since the �rst commercial ship basin was commissioned in 1883, towing tanks have provided naval architects with a reliable method of predicting the performance of a ship at sea. Towing tanks are used for both resistance and propulsion tests, with towed and self-propelled ship models used to determine how much power the engine will have to provide to achieve the speed laid down in the contract between shipyard and ship owner.
The performance of a vessel depends on the hydrodynamic interaction between the hull, its propulsion system and its rudder, which all combine to interact with the environmental conditions. The �ow past the hull in�uences the �ow past the rudder, which in turn affects the quality of �ow “seen” by the propellor. While it is certainly possible to obtain useful design information from experiments (or simulations) that investigate these components individually, in order to predict the at-sea performance of a vessel with a high degree of accuracy, it is necessary to include all three components in a single model. This is particularly important with the current demand for energy ef�cient “green ships” which is driven by a combination of legislation and economic necessity. Energy savings of a
few percent can signi�cantly in�uence the operational viability of a vessel.
However, the cost and effort of producing a model and testing it, means that towing tanks are usually deployed relatively late in the design cycle, verifying and �ne tuning an established design, rather than providing engineering data that could be used to drive the design into different, better, directions. In addition, any novel solution tested at model scale has increased uncertainty of actual performance at ship scale due to de�ciencies of the scaling process.
Computational Fluid Dynamics (or CFD) has long been touted as a credible alternative to tank testing, providing a “numerical” model basin that could, at least in principle, be deployed much earlier in the design process, providing naval architects with a stream of engineering data that could be used to in�uence and improve the design. CFD also carries the distinct advantage of result accuracy independent of the scale at which they are calculated.However, up until recently, that prospect has been limited by a number of challenges inherent in the CFD simulation process. In this article we consider how advances
in CFD and hardware technology have addressed those concerns, and consider whether fully featured numerical towing tanks are �nally now a practical proposition.
CHALLENGE 1: MESHINGCFD simulations solve the fundamental equations of �uid dynamics, through a process known as “discretization” in which a volume occupied by the �uids (both water and air) surrounding the vessel is subdivided into a number of much smaller control volumes (known as computational cells). Depending on the software used, these control volumes can be tetrahedra (four faced pyramids), hexahedra (six faced bricks) or polyhedra (control volumes with an arbitrary number of faces).
Constructing a computational mesh is one of the most important parts in conducting a CFD simulation, and always represents a compromise between accuracy and computational cost. In practical terms, a “�ne mesh” that is constructed from a large number of small computational cells provides a more accurate prediction than a “coarse mesh” of larger cells. However, a greater number of cells results in a larger computational
NUMERICAL TOWING TANKS, A PRACTICAL REALITY?STEPHEN FERGUSONCD-adapco
The performance of a vessel depends on the hydrodynamic interaction between the hull, its propulsion system and its rudder, which all combine to interact with the environmental conditions.
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cost, requiring more computer resources and longer simulation times compared with a coarser mesh. Since the computer resources available for a given simulation are �nite and, in order to be useful, simulation results must be provided within a reasonable time-scale, CFD engineers have to choose how they spend their cells wisely, deploying smaller cells in areas of high rate of change close to the vessel and its wake, transitioning to larger cells further away.
Historically, providing a computational mesh that is �ne enough to capture the hull, rudder and propeller in a single simulation has been challenging, and engineers have often been forced to consider the components in separate simulations (and accounting for their interactions using boundary conditions).
However, recent developments in automatic meshing technology (that
provide a high quality grid with minimal manual interaction from the engineer), computer hardware (which provides lower cost computational resources) and licensing (which reduces the cost of running simulations across multiple processors) has made self-propulsion and maneuvering tests a practical proposition.
CHALLENGE 2: WAVE AND WATER PHYSICSIn order to accurately predict the performance of a vessel, the numerical simulation has to correctly predict for both the in�uence of the vessel on the surrounding sea (wake predictions) as well as the increase in resistance caused by waves.
This represents a much greater challenge than the type of “single �uid” simulations that can be used to
investigate an aircraft, land-vehicle, or fully submerged vessel.
Many CFD tools deploy a “Volume of Fluid” approach that assigns a value of “1” to cells that contain water, and a value of “0” to cells that contain air. In cells marked “1” the physical properties of water are used, in the cells marked “0” the properties of air are used.
STAR-CCM+® deploys a “High Resolution Interface Capture” scheme to accurately capture the position of the free surface between water and air; this is necessary to prevent the free surface from diffusing (with cells that have a value that is somewhere between “1” and “0”). This method ensures that the interaction between the vessel and the free-surface can be accurately captured. STAR-CCM+ also provides a range of built-in higher-order wave models that
STAR-CCM+ is capable of capturing the full complexity of a vessel (including the propulsion system, rudders, and all appendages) without simpli�cation.
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can be used to test the vessel under realistic sea states.
Additionally, STAR-CCM+ also includes an extensively validated cavitation model that can be used to predict and manage the phase changes caused by the propeller.
CHALLENGE 3: VESSEL MOTIONUnlike the simulation of an aircraft or road-vehicle, which in ideal circumstances moves forward in a single direction, the forward progress of a ship is heavily in�uenced by the surrounding sea-state. Even in still water, establishing the dynamic position of the ship in relation to the sea surface (“sink and trim”) is critical to providing accurate resistance predictions. In rough seas, the full motion of the vessel in six-degrees-of-freedom must be correctly accounted for, as the vessel pitches, rolls and heaves in response to oncoming waves.
STAR-CCM+ accounts for 6DOF vessel motion in an automatic manner. The “Dynamic Fluid- Body Interaction” model integrates the forces acting on the vessel at every time step, and adjusts its position (in all-six-degrees-of-freedom) accordingly.
“Adjusts its position” means moving the computational mesh, which historically has been a dif ficult proposition, and various methods have been used to account for this motion. For relatively small movements, the vertices of cells in the mesh can be adjusted on a step -by-step basis. However, for large movements, this becomes impractical as individual cells become highly distorted, leading to inaccuracies in, or failure of, the simulation.
STAR-CCM+, uniquely among commercial CFD codes, solves this problem using “overset” or “chimera” meshes, in which the mesh around the vessel is independent of the mesh used to represent the sea. This allows the simulated ship to move as much as necessary. Furthermore, it can be used to model the interaction between multiple vessels or objects, such as one ship moving independently in the wake of another, or the collision of two vessels. Also, with overset mesh, the rotation of the propeller and rudder motion, in addition to propeller pitching, can all be modeled in relation to the ship motion, leading to robust, accurate self-propulsion and maneuvering analysis.
OUTLOOKHaving addressed the three main challenges to replicating the performance tests, CFD is now able to provide a useful tool to augment, if not replace, towing tank testing. Comparisons between STAR-CCM+ and tow-tank simulations have demonstrated a high degree of correlation between the two methods (typically within a few percentage points [1],[2]). Furthermore, CFD simulations also have the advantage that they can easily be deployed at full-scale if desired, reducing the uncertainty inherent in model scaling.
Although it is unlikely that any large vessel will be designed in the foreseeable future without some aid from towing tanks, CFD is now routinely being used as part of the design process by shipbuilders and naval architects across the world. Used effectively, CFD simulation can be used to reduce the amount and cost of physical towing tank tests by providing a more re�ned and optimized design that requires
fewer modi�cations in order to meet contractual obligations.
It is also true that in certain parts of the industry, such as in the design of the high-performance vessels that compete in the America’s Cup, towing tanks have been dispensed of entirely in favor of CFD. The winning yacht in the 37th America’s Cup was designed using STAR-CCM+, as will be yachts raced by Ben Ainslie Racing and Luna Rossa in the next America’s Cup.
What of the future? Unlike towing tanks, once you have developed a robust process for simulating the performance of a vessel, it is relatively easy to automate it. This opens the door to both “automated design exploration,” where the proposed vessel is subjected to a wide range of potential operating scenarios, and “optimization,” where the design of the vessel is automatically adjusted to account for de�ciencies in the performance identi�ed in previous simulations.
Widespread adoption of this approach will not only lead to more innovative and ef�cient ship designs (which can be developed at lower cost), but also more robust vessels that have been numerically tested against a much wider range of real-world operating conditions than could ever be considered using a towing tank alone.
REFERENCES[1] http://www.cd-adapco.com/presentation/maneuvering-predictions-early-design-phase-using-cfd-generated-pmm-data
[2] http://www.dansis.dk/�larkiv/pdf-�ler/2009/2/skibsdesign_force.pdf
STAR-CCM+ also includes a number of wave models that can be used to test the performance of a vessel under a range of realistic sea states.
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Dr. Richard Korpus is Chief Scientist for the American Bureau of Shipping where he is responsible for integration and quality control of CFD services world-wide. Since joining
ABS in 2013 Richard has matured CFD into an essential part of ABS’ technology offerings, and developed new client services to ensure ABS remains ahead of the competition. CFD is now used over a wide range of marine and offshore applications to support customer requests to increase operating ef�ciency, enhance environmental performance, and improve safety. Prior to joining ABS Richard was Principal Scientist at Applied Fluid Technologies (AFT), a company he founded in 2000 to provide ef�cient solutions to complex aerodynamic and hydrodynamic design problems. He has been involved with CFD development and application for more than 25 years, and has served clients in the naval, maritime, oil and gas, nuclear, automotive, chemical, aerospace, and racing business sectors. Dr. Korpus earned his PhD. in CFD and Naval Architecture from the University of Michigan, and also holds multiple degrees in aerospace engineering.
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RICHARD KORPUSAmerican Bureau of Shipping (ABS)
SAHAR FAZLICD-adapco
INTRODUCTION“ABS is �rst and foremost a safety company,” says Richard Korpus, “but safety can arise on many different fronts. It can refer to the safety of a high-valued asset, for the people who work on that asset, for the environment where the asset operates, or even for the �nancial security of the owners and operators of that asset.”
Dr. Korpus is Chief Scientist, Computational Fluid Dynamics (CFD) for the American Bureau of Shipping, a leading provider of maritime and offshore classi�cation services. In this role Korpus supports the Chief Technology Of�cer (CTO) and underlying organizations through a focus on developing and applying CFD technology. He believes CFD has the potential to change how some of the most challenging problems in marine and offshore classi�cation are solved in the future. “This organization has a reputation as an industry leader, and we’re using CFD to extend that reputation by offering state-of-the-art services, new to the classi�cation business,” says Korpus. In this article, the reader is introduced as to how CFD is changing ABS’ marine technology business by providing designers, owners and operators a means to improve vessel fuel
CONFLICTING OBJECTIVES IN SHIP DESIGN: ENVIRONMENTAL & SAFETY REGULATIONS CONSPIRE TO COMPLICATE THE MARINE CLASSIFICATION BUSINESS
ef�ciency, lower environmental impact, and maintain the highest level of safety.
BACKGROUND: CFD SUPPORTS A PROACTIVE BUSINESS MODEL Shipping is the lifeblood of the world economy carrying 90% of international trades worldwide. A variety of organizations, including the International Marine Organization (IMO), national coast guards, and regional port authorities, impose regulations to ensure the safety of cargos, people, and the environment. These regulations change regularly, and a classi�cation society needs to react quickly. When combined with ship owners’ continued motivation to minimize operating cost, it is becoming essential that every sector of the marine industry �nd ef�cient design strategies to satisfy environmental and safety regulations. The net effect is an enhanced competitiveness where innovative solutions are essential to survival. Examples of new challenges include: optimizing hull resistance and propulsive power; deployment of biodegradable oils to lower the risk of water contamination; development of Energy Saving Devices (ESDs); and methods to “scrub” engine emissions. Each of these innovations comes with its own business and
technical challenges, and ABS has chosen to respond proactively by investigating solutions before their clients encounter dif�culties. CFD is an essential part of that process.
One timely example is the increasingly demanding environmental regulation known as the Energy Ef�ciency Design Index (EEDI). The index is a means to enforce reductions in greenhouse gas emissions, but in conjunction with owners’ desires to minimize fuel consumption may push designers to install less powerful propulsion engines. Since total installed power is an important variable for the safety of ships in bad weather, the requirements for low emissions and safe power margin could come into con�ict. ABS is taking a proactive role in helping to avoid such con�icts by using CFD to quantify minimum safe levels of power.
Being proactive (as opposed to reactive) requires an engineering approach built on pre-established CFD best practices to minimize response time. Best practices typically focus on a single class of CFD application, but at ABS these are motivated by the more practical business objectives of class customers. Practices exist to guide development of ships and platforms to be more environmentally-
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friendly, safer, fuel ef�cient, and cost-effective. Typical CFD-related service offerings include:• Guide hull and propeller design to
minimize operating cost, • Ensure safe power margins are
maintained as installed power is decreased,
• Ensure adequate maneuvering and dynamic stability margins are maintained,
• Assist propeller shaft and stern tube design to avoid bearing damage,
• Assist selection and improvement of Energy Saving Devices (ESDs),
• Provide structural load estimates due to sloshing liquid cargoes,
• Provide structural load estimates imposed by extreme wind and wave events,
• Guide cargo distribution to minimize motion, structural loading, or slamming in a seaway,
• Advise operators about the most fuel ef�cient cargo distribution and operating trims,
• Develop procedures to minimize boil-off of Lique�ed Natural Gas (LNG) cargos,
• Guide redesign to accommodate the trend towards slow steaming.
Best practices help ABS customers and prospective clients look ahead before committing to a particular design. They allow assessment of a design’s performance, or its compliance with environmental and safety regulations (such as EEDI), at an early stage of a project. An additional advantage is that best practices homogenize the quality of ABS’ CFD products and services. Even though they have been using CFD (including STAR-CCM+®) for many years, best practices ensure that ABS engineers from different offices,
different levels of CFD experience, or with different customer requirements all deliver the expected level of accuracy in a predictable period of time. Consistent quality of results is guaranteed without spending extra man-hours repeating grid ref inement, time step, or turbulence modeling studies.
An example is provided in the next section where best practices for propeller optimization are demonstrated.
DESIGN OPTIMIZATION In order to improve a ship’s operating ef�ciency it is necessary to simultaneously address its hull resistance, propulsive ef�ciency, and engine performance. Each affects the other, and the process is even more challenging when multiple optimization objectives are contradictory in nature. For instance, reducing the main engine size can improve overall ef�ciency in terms of lower fuel consumption and greenhouse gas emissions, but con�icts with safety-
FIGURE 1: Detailed geometry for STAR-CCM+ simulation of ship self-propulsion
FIGURE 2: CFD predictions of wake: scale effect (left) and propeller effect (right)
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oriented requirements for reserve power. Without adequate reserve power a vessel might have maneuvering problems as wind and wave loads increase in bad weather. In such a case optimization requires a subtle balance between economy and safety - or at the very least inequality constraints to ensure minimal acceptable values for each objective.
LONGEST STANDING CHALLENGE: PROPELLER DESIGN BY FULL SCALE SIMULATIONPropeller design is one of the most important factors affecting operating ef�ciency, and yet it has been performed more or less the same way for decades. The problem is indeed dif�cult because the propeller operates in a hull viscous wake that varies both spatially and temporally. Traditionally a model test is performed without the propeller present. The wake is measured and then extrapolated to full-scale. The result is averaged circumferentially at each radius to provide a steady in�ow, and the propeller designed for that condition.
But with modern CFD and optimization, it is no longer necessary to tolerate the inaccuracies of extrapolation or steady in�ow assumptions. The propeller can be designed or optimized at full scale, in situ behind the ship, even when the wake is unsteady and varies in three dimensions. A design developed using full-scale, unsteady CFD will be more ef�cient due to accurately accounting for propeller/hull interaction, and can be made to produce less unsteady force (vibration), off-axis loading, and cavitation.
Figure 1 shows a typical self-propelled ship simulation and Figure 2 the types of propeller in�ow (hull wake) that results from different modelling assumptions. Figure 2 demonstrates the severity of inaccuracies that are expected when using un-propelled or model-scale test data to predict actual propeller in�ow. Note that the model scale wake looks nothing like the full-scale equivalent. Similarly, the normal wake (without propeller) looks nothing like the effective wake (with propeller). A model test provides data like that shown in the upper left �gure, but the operating condition for
which a propeller should be designed is like that in the lower right.
To demonstrate the advantages of design by CFD, engineers at ABS leveraged STAR-CCM+‘s sliding mesh and overset grid techniques to simulate full-scale propellers rotating in the actual full-scale unsteady hull wake. CD-adapco’s HEEDS optimization package was employed to search through design space, and a variety of parameterizations tested including radial distributions of pitch, chord, rake, and skew. HEED’s SHERPA algorithm is employed to �nd the design with minimal shaft power at a prescribed thrust. The individual software elements are shown in the schematic of Figure 3. For a real-world design, the story is more complicated because of the phenomenon of cavitation. If pressure falls below the thermodynamic boiling point, water evaporates to vapor. With low enough pressure (such as might be found on a propeller blade at high lift coef�cients) this can happen at any temperature. When pressure again increases the process reverses and vapor condenses,
FIGURE 3: HEEDS’ automated process for propeller design optimization
For more than 100 years ship designers have built ships using the evolutionary approach – one small improvement per design generation. Within the last few years CFD has provided a groundbreaking technology to enable the revolutionary approach – true optimization for every design generation.
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sometimes violently. The more violent condensations can actually erode away a solid metal blade. It is also notable that not all “good” designs are created equal. Two blades with equal total lift and drag might exhibit different levels of cavitation depending on the local distributions of pressure. Figure 4 shows an example of cavitation and cavitation damage. ABS design optimizations avoid this problem by checking minimum blade surface pressure for every design and passing the results back to HEEDS for providing an inequality constraint. Excessive cavitation is avoided by not allowing minimum blade pressure to get any less than that of an acceptable baseline design.
DEMONSTRATION: SINGLE OBJECTIVE APPROACH The method is demonstrated for a twin-screw LNG carrier at a single speed and cargo load. The hull is left unchanged, and propeller parameterized for varying radial distributions of pitch and chord. Once the base design is solved, HEEDS’ SHERPA algorithm uses a combination of population-based and gradient-based optimization methods to explore the whole design space. Each design is tested at multiple shaft speeds, and the objective function (shaft horsepower) is chosen for the speed which delivers the prescribed thrust. Minimum blade surface pressure is found over one complete revolution at the thrust balance point, and the result returned to HEEDS to provide the cavitation inequality constraint.
The level of improvement possible is dependent on how a design is parameterized and on how many design evaluations are permitted. In the present
example the radial distributions of pitch and chord are de�ned by just �ve parameters and SHERPA is allowed just 150 design evaluations. The baseline propeller was taken from a high-end designer who had already optimized the unit using existing analysis technology. Results are summarized in the HEEDS output shown in Figure 5. Even for this relatively restrictive example, ABS engineers found power reductions around 2.0%, which for larger ships corresponds to as much as $500,000 per year savings. But as Dr. Korpus points out, “The point is even more fundamental than the huge cost savings. For more than 100 years ship designers have built ships using the evolutionary approach – one small improvement per design generation. Within the last few years CFD has provided a groundbreaking technology to enable the revolutionary approach – true optimization for every design generation.”
THE CASE FOR MULTI-OBJECTIVE OPTIMIZATION The single-objective approach provides an effective philosophy to identify substantial fuel savings, but does not account for the above mentioned issue of reserve engine capacity for maneuvering in extreme weather. Ship engines spend most of their life operating at a power less than their Maximum Continuous Rating (MCR). But if the propeller and engine are optimized simultaneously, a power plant will be selected with just enough power to satisfy the design condition. Normal operations will require 100% MCR and nothing is left for bad weather. Conventional design wisdom applies a 15% “sea margin” to cover such contingencies, and one might
be tempted to just add that margin after the optimization is complete. But in reality the required margin is a function of the other design variables, so a multi-objective approach is required. Ideally, a designer should be provided with a range of designs (the so-called Pareto frontier) that prioritize the objectives of fuel saving and safe maneuvering independently.
Unfortunately, simulations of self-propelled ship maneuvering are still very time-consuming. Even a single maneuver at a single speed in a single wind and sea condition requires many days of computer run time. It is impractical (at this point in time) to incorporate heavy weather maneuvering into a multi-objective optimization. In lieu of this, it is crucial to have a precise understanding of the minimum power margin required in adverse conditions, and also for how that minimum is affected by the other optimized variables. To provide this knowledge the CFD group at ABS conducts maneuvering simulations in various sea conditions and power settings. Typical rudder motions are applied and STAR-CCM+’s DFBI capability is used to predict the ship’s trajectory. A given level of power is considered safe if the vessel can turn and accelerate under the prescribed rudder motion. The goal is to build a database of acceptable sea margins that can be applied until the multi-objective approach becomes more viable.
A PRACTICAL ALTERNATIVE TO MULTI-OBJECTIVEDeveloping this database requires a huge number of simulations. A variety of different ship types and sizes need to be
FIGURE 4: Tip vortex and blade back cavitation (left), and cavitation damage (right)
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tested over a range of weather conditions. In each case a range of power settings have to be applied to identify the point at which a vessel can no longer maneuver in the prescribed weather condition. The approach is demonstrated using a generic Very Large Crude Carrier (VLCC) trying to turn in 5.5 meter beam seas and 37 knots of side wind. Figure 6 shows the overset grid in a large background earth-�xed domain with a total of 7M trimmed hexahedral cells. The simulation starts with the ship at low speed and straight rudder to build fully-developed Kelvin and viscous wakes. The vessel is free to move in six degrees-of-freedom so the effects of added resistance and lost propulsive ef�ciency are included. Once the wakes are developed (and propeller forces stabilized), the rudder is put over 20 degrees and the power increased to full.
Simulations are conducted at different power levels under both full load and ballast draft conditions. If the prescribed maximum power is acceptable, the vessel accelerates under the in�uence of its own propulsive and rudder forces. At power levels below some point the ship can no longer overcome forces and moments imposed by the wind and waves, and just blows sideways. Figure 7 shows an example where the power is suf�cient for a complete turn, whereas Figure 8 shows the trajectory from a vessel with lower maximum power. Note that in Figure 8 the vessel is seen to drift three-quarters of a boat length to leeward before starting to recover distance back to windward. The small high speed oscillations superimposed on top of the curves are due to vessel motions over individual waves. It is interesting that even though the turn rate (yaw angle versus time) becomes steady about half way through the simulation, the vessel is only just managing to halt its slide to leeward near the end of the simulation. The maximum power used for this second example might be considered close to the minimum safe amount.
SUMMARYCFD has become a practical tool in almost every sense. This is true not just from the technical point of view, but from the business point of view as well. It enables a proactive approach to solving client problems, and provides the means to revolutionize a maritime industry that is traditionally evolutionary in nature. Design optimization is �nally becoming a reality, and even though some problems may still be time-consuming (e.g. maneuvering in a seaway), CFD can be expected to play an increasingly prominent role in the marine and offshore business sectors.
FIGURE 5: HEEDS summary of propulsive performance for 150 sample designs
FIGURE 6: Simulation of a maneuvering VLCC: earth-�xed grid (left) and overset details (right)
FIGURE 7: Velocities at depth of shaft during steady turning
FIGURE 8: Typical maneuvering trajectory (left) and yaw angle (right)
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MARINE ENERGY SAVING DEVICES
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ALEJANDRO CALDAS, CONSTANTINOS ZEGOS & CHRIS CRADDOCKLloyd’s Register
INTRODUCING ENERGY SAVING DEVICESIn order to reduce fuel costs and comply with increasingly stringent environmental regulation on emissions of air pollutants such as SOx, NOx, and CO2, ship owners and operators are constantly looking for innovative, energy-ef�cient, and cost-effective solutions.One popular solution is to employ Energy Saving Devices (ESD) in order to improve the hydrodynamic performance of vessels through active or passive �ow control. Such devices fall into two categories:1. Those that aim at reducing the
resistance of the vessel;2. Those that aim at improving the
propulsion performance. This can be achieved through improvements of the propulsion system itself, for example with Propeller Boss Cap Fins (PBCF), or through the use of systems that improve the hull-propulsion interaction, such as pre-ducts.
This article focuses on the second group
ENERGY SAVING DEVICES:A COST-EFFECTIVE ANDENERGY-EFFICIENT SOLUTION FOR THE MARINE INDUSTRY
of devices. Although the advantages of applying those technologies are clear, a few challenges need to be overcome in the design process.
THE CASE FOR CFDThe two main challenges with designing ESD’s are as follows:1. Since the performance of ESDs
is strongly linked to the Reynolds number (Re), their design cannot be based on scaled models in a ship model basin, where the difference in Re is usually two orders of magnitude.
2. ESD design should be robust, i.e. improving the performance over the operating profile of the ship and not only for one condition. This forces designers to investigate a large number of operating points in order to ensure that the device is effective across the operating profile.
The use of Computational Fluid
Dynamics (CFD) applied to the design and assessment of these devices is vital since it allows the computations to be carried out at full scale (same Re). In addition, a wide range of solutions can be investigated without having to construct any physical model of the device.
CASE STUDYA case study of a 60,000 DWT bulk carrier was carried out. Three types of ESDs were tested using STAR-CCM+®, namely pre-ducts, twisted rudders and PBCF. Those devices improve the overall performance of the vessel by interacting with the propulsion system, thereby reducing the amount of rotational losses. The effect of each ESD on the propulsion performance and on the hull-propulsion interaction was calculated for six operating points, including three draughts (ballast, design and scantling), and two speeds per draught.
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PRE-DUCTSIn order to �nd the best pre-duct design for the 60,000 DWT bulk carrier, the duct geometry was fully parameterized using the CAD tools in STAR-CCM+. A total of seven parameters were used to de�ne the duct: diameter, relative position (two constraints), contraction angle, length, thickness, and pro�le shape. As can be seen from the streamlines shown in Figures 1 (scantling draught) and 2 (ballast draught) for an initial design, the performance of the duct was found to be relatively sensitive to the hull draught. In scantling draught, the duct is not aligned with the �ow, resulting in a turbulent wake and loss of rotational energy. This can lead to a bad performance of the propeller and even cavitation. In ballast draught, however, the �ow remains aligned to the duct, resulting in a better performance of the propeller. The effect of the duct upon the propulsion is quanti�ed in Figure 3 for different propeller shaft speeds around the equilibrium point (where the propeller thrust is equal to the hull drag). It appears that the
duct has an impact over the hull drag but also over the propeller performance. In this case, a slight reduction of the hull drag and an increase of the propeller thrust for the same rotational speed occurs. The shift of the equilibrium point shows a decrease in rotational speed and hence a reduction of the engine power delivered.
TWISTED RUDDERSimilar calculations were carried out for the twisted rudder with costa bulb, whose design is shown in Figure 4. The rudder geometry was also parameterized using the CAD tools in STAR-CCM+ and tested over the operating pro�le previously de�ned. The design parameters included the rudder pro�le length, thickness and leading edge camber distribution, bulb diameter and length. The effect of the rudder geometry on the propulsion performance is illustrated in Figure 5 and quanti�ed in Figure 6. The later indicates an increase of both the propeller thrust and the hull drag (rudder drag included). However, the overall effect
is a reduction of the delivered power as the equilibrium point is at lower rotational speed when compared to the original case. In addition, the performance of the device was found to be relatively stable over the operating pro�le, as can be seen in Figure 10.
PROPELLER BOSS CAP FINSSimilar calculations were carried out for the PBCF, whose design is shown in Figure 7. The PBCF geometry was also parameterized in STAR-CCM+ and tested over the operating pro�le previously de�ned. The parameters included the pro�le length, thickness and camber distribution, number of blades and relative position to the propeller. The effect of the PBCF geometry on the propulsion performance is illustrated in Figure 8 and quantified in Figure 9. The latter indicates an increase in both the propeller thrust and the hull drag. However, as with the twisted rudder design, the overall effect is a reduction of the delivered power as the equilibrium point is at
FIGURE 3: Using the pre-duct - Comparison of drag and thrust versus rotation rate for the original and modi�ed designs in design draught
FIGURE 4: Geometry and trimmed mesh of the twisted rudder with costa bulb
FIGURE 5: Flow behavior on the propeller and rudder when using a twisted rudder with costa bulb
FIGURE 6: Using the twisted rudder - Comparison of drag and thrust versus rotation rate for the original and modi�ed designs in design draught
FIGURE 1: Flow behavior around the duct in scantling draught (13.3 m draught)
FIGURE 2: Flow behavior around the duct in ballast draught (6.73 m draught)
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FIGURE 7: Geometry and trimmed mesh of the propeller boss cap �ns
FIGURE 8: Flow behavior around the propeller and rudder when using propeller boss cap �ns
FIGURE 9: Using the propeller boss cap �ns - Comparison of drag and thrust versus rotational speed for the original and modi�ed designs in design draught
FIGURE 10: This graph shows the percentage of time allocated to each operating point and the overall savings achieved at each of these points using the three types of ESD.
lower rotational speed when compared to the original case.
CONCLUSIONThe aim of this study was to analyze the in�uence of three tailored ESDs on the performance of a new 60,000 DWT bulk carrier in order to select the best option to be �tted to the vessel. The overall savings per condition, as well as the percentage of time spent on each operational condition are summarized in Figure 10. It was found that the twisted rudder with costa bulb was not only the ESD with the most consistent performance, but also the modi�ed design that led to the highest overall power reduction. The analysis also showed that the performance of the pre-duct ESD was the most sensitive to operating conditions. This suggests that this device could give good results for speci�c conditions but for wider operating pro�les such as the one presented the applicability is reduced. Consequently, it would appear that the pre-duct would be best suited for vessels that sail within relatively small draught and speed ranges.
e use of CFD applied to the design and assessment of ESDs is vital since it allows the computations to be carried out at full scale (same Reynolds number). In addition, a wide range of solutions can be investigated without having to construct any physical model of the device.
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TIME = MONEY
MARINE DR. MESH TALKS EHP
As you may have noticed, these days, I’m all about showcasing how to use our automated work�ow to get your simulation work done faster. Yes, I can admit it now: I used to be a micromanager. You know… that guy who insists on telling you what to do, when to do it and exactly how to do it. These days, I no longer feel I need to actively participate in every step of the meshing and simulation process. Instead, I am preaching work�ow automation to reduce the learning curve and embed best practices. And I’m preaching it to Dr. Design (that guy who doesn’t like change)! The other day, he came running into my corner of�ce: “I need to quickly compute EHP (Effective Horse Power) for a new hull design and compare results to towing tank experiments.” I didn’t feel the urge to guide him through a detailed setup and meshing process. I simply pointed him towards the �nish line: “Use EHP to compute EHP!” Estimating Hull Performance (EHP) is one of our Virtual Product Development (VPD) add-ons speci�cally developed to provide naval architects with a streamlined GUI-driven process to simulate hull motion in calm water. Like all tools in the VPD series, EHP enables the user to simply start with talking about the design, the physics and the test conditions and then let the add-on do the CFD technical conversion, automatically and consistently. Let me explain to you how easy it is to import your hull design and get started with estimating its performance with STAR-CCM+®.
STEP 1: IMPORT YOUR HULL!Currently, EHP is delivered via a .nbm �le, installed in STAR-CCM+ via the Plug-in Manager. In future releases, this step will no longer be required. Once installed, EHP will accept many different types of geometry, both CAD-based and tessellated, and it will accept fully enclosed hulls (with or without superstructure) or open half hulls. Set the “Import Units” to match your import �le. If your geometry is in model scale, insert the model “Scale Factor”. Once your geometry is ready, click on “Import Geometry.” Your hull will be plotted for you in the default orientation. Click the radio button next to the image that matches your orientation and click “Re-Orient Geometry” and go forward.
STEP 2: POSITION YOUR HULL!EHP will plot a graph scene where the goal for the user is to align the aft of the hull perpendicular to the origin of the plotted coordinate system. The buttons in the interface allow the user to move the hull in increments of the center distance value. You can also use the “Top” and “Front” buttons to move the hull laterally and visualize sinkage.EHP will monitor for reasonable values of reference length and will warn you if these values are outside of normal ranges. The RefLength text box will be red when the values do not make sense and you should continue to position the aft perpendicular if this value is still red. Once the positioning is complete, click “Forward.”
DR. MESH TALKS EHP
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DR. MESH TALKS EHP MARINE
STEP 3: PREPARE YOUR HULL!The next step is to check the hull to see if it can be “surface-meshed” and therefore can enable a volume mesh for the analysis. EHP can detect automatically if the hull is a full hull or an open half-hull and will run some routines to automatically prepare or �x the hull surface. To start, simply click “Check Hull.” If there are no problems, EHP will report “No Errors Found!” and there is nothing more for you to do! If errors are found, you will be given an option to enter manual surface repair. After EHP reports that no errors are found, you can optionally set your “Initial Trim” and also “Clip Superstructure” from the top of your hull geometry. EHP will calculate weight by initial sinkage, so the superstructure is not needed and will slow the calculation down if included. Click “Forward.”
STEP 4: DEFINE YOUR HULL AND OUTPUT!The next step is to de�ne the hull parameters. This includes de�ning the body of water in which the hull will perform. There are inputs for the temperature and whether the body of water is fresh or seawater. EHP will automatically calculate typical values for these inputs. You can customize these values if desired. At this point, you will notice some �elds in red. These need to be calculated �rst by EHP. Click the “Calculate Approximate Values” button and EHP will do a quick calculation for the “Displacement”, and the key coef�cient values. After this is complete, you can again override these values if desired. Click “Forward” to de�ne your output and how you want to view it. All checked plots and scenes will be included in the PPT. You can optionally output all plot data in CSV format to later plot/post-process in Excel. "Click "Forward" and go to the next step.
STEP 5: GET RESULTS!Finally, let’s set up the virtual test parameters and get results. By default, EHP is set up to provide a batch script to submit the design to your computing environment. EHP is very ef�cient in solving your case, but will still take 8-16 cores to compute for a single day turnaround for a full speed testing range. A default velocity range is provided, but if you need non-uniform velocities, just click “Adjust Velocities”. Initial trim and sinkage can be preserved by unchecking “Simulate Trim” and “Simulate Sinkage.” Otherwise, STAR-CCM+ will calculate them based on the weight from initial sinkage, and the center of buoyancy. Meshing is as easy as using the slider. If you are an advanced STAR-CCM+ user, you can select “Do Not Generate Mesh” and EHP will only set up the physics and boundary conditions. You can then manually set all meshing parameters and save the sim �le yourself. Finally click “Setup” to generate the volume mesh. The batch �le will be created already, just close EHP, save the .sim �le, and follow the directions in the batch script.
THAT’S IT!Dr. Design managed to get the results he was looking for by the next day. I never thought this day would come, but I overheard him preach about streamlined and automated processes to a young simulation engineer the next day. My job is done!
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OIL & GAS OPTIMIZATION OF AN OFFSHORE PLATFORM ORIENTATION
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OPTIMIZATION OF AN OFFSHORE PLATFORM ORIENTATION OIL & GAS
INTRODUCTIONTechnical safety in the oil & gas industry is of paramount importance. With most Tension Leg Platforms (TLP) being geographically remote, costing upwards of $3.5 billion, containing a multitude of process and operational hazards, and crowding personnel onboard, it is crucial to minimize the risks to people and assets. This can be achieved through the process of Inherently Safe Design (ISD), in which technical safety has direct in�uence on the design, from concept through to commissioning. The platform orientation is one design aspect that can play a signi�cant role in the ISD process, limiting the adverse effects should an incident occur. Traditionally, the platform orientation has been determined by engineering judgment, heavily weighted by past experiences. While this approach initially appears to be cost- and time-effective, it has the potential to lead to a non-ideal design solution which could cause safety and operational issues to go unaddressed and increased costs in later design stages.
This article will discuss how the orientation and layout of an offshore platform can have a signi�cant impact in developing a better and more informed design, keeping with the ISD principles. A case study will be discussed where STAR-CCM+® was integrated with additional analysis tools to optimize the orientation of a �xed offshore platform. It will demonstrate a technique to �nd the optimum platform orientation, i.e. the platform orientation which results in the best design compromise between speci�ed parameters.
OPTIMIZATION PARAMETERSThe parameters considered for the optimization study were as follows:• The natural ventilation (wind), which can
reduce the potential accumulation of toxic and �ammable gases as well as provide indications of potential vapor cloud explosion consequences.
• The helideck impairment, which can impact helicopter operations due to hot turbine exhaust gases, affecting both general operations and potential emergency operations.
• The wind chill, which can affect the ability for personnel to work on the platform. This is particularly important in cold climates and extreme weather areas where working conditions can in�uence the number of personnel required for operation.
• The lifeboat drift-off direction, which can impact the safety of the crew in an emergency situation.
• The hydrodynamic drag, which can affect tendon fatigue life, hull integrity, and structural design requirements.
FIGURE 1: The aim of this study was to �nd the optimum theta, angle between True North and Platform North, based on a set of parameters.
GERARD REYNOLDS & ANDREW STASZAKAtkins
A FUNCTIONAL METHOD FOR THE OPTIMIZATION OF A TENSION LEG OFFSHORE PLATFORM ORIENTATION UTILIZING CFD
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NATURAL VENTILATION (WIND)Guidance for ventilation rates is contained in the Institute of Petroleum (IP) 15 document. In the event of an unintended hydrocarbon release, higher ventilation rates typically translate into the formation of smaller �ammable gas clouds. This parameter is therefore intended to be maximized.
EXHAUSTThe Civil Aviation Protocol (CAP) 437 dictates that restrictions be put in place to the helicopter operations if there is a temperature increase of 2 ºC above ambient within the operational zone above the helideck. Temperature rise is used to de�ne potential impairment to operations, in some cases this may limit operations altogether or require adjustments to payload weight, approach paths, etc. For many offshore facilities, particularly in extreme weather areas, helicopters are used as the primary means of transportation and evacuation during an emergency. Thus, it is imperative that the helideck remains available through as many expected weather conditions as possible. Additionally, platforms look to minimize exhaust impacts to drilling, crane, and elevated deck operations. The helideck impairment from exhaust fumes is therefore intended to be minimized.
WIND CHILLWind chill is quanti�ed by the perceived decrease in temperature felt by the body on exposed skin and is regulated by NORSOK S-002. Wind chill can impact the number of personnel required to operate a facility. In some cases, environmental effects such as wind chill have been known to increase the potential for operator error. In order to provide personnel with acceptable working conditions and maximize safety, wind chill effects are intended to be minimized. It is important to note that this can be counter to increasing ventilation for the reduction of �ammable clouds during an unintended release of hydrocarbons. One intent of the optimization approach is to �nd a balance between these two potentially competing goals.
LIFEBOAT DRIFT-OFFIf a lifeboat is deployed during an emergency, it is imperative to maximize the potential survival of the craft by limiting exposure to potential hazards. A lifeboat deployment may also suffer from loss of power, thus left to environmental
ventilation for the reduction of �ammable
FIGURE 2: For a given hydrocarbon leak rate, increasing the ventilation rates aids in dispersing the �ammable gas cloud, typically producing smaller explosions in case of ignition, and less probability of fatality and damage to the structure.
FIGURE 3: The offshore platform is powered by burning some of the gases it produces. The exhaust outlets need to be positioned in such a way that the exhaust fumes minimize potential impairment to the helideck operational zone throughout the year.
FIGURE 4: Wind chill index map showing the danger of frostbite to personnel
Helideck Operational Zone
Exhaust Outlets
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effects to reach safety. To maximize the potential for survival, the lifeboat should drift safely away from the platform, assisted by the current. Adverse drift-off, the length of time to reach a safe area, and potential drift back into the facility is intended to be minimized.
TENDON STRESSTLP platforms are typically used in water depths reaching up to 7,000 ft. To be cost-ef�cient and comply with the American Petroleum Institute (API) Recommended Practice (RP) 2T, the stress in the tendons resulting from maintaining the platform in place despite wave impact and drag loading from the current needs to be minimized. Tendon requirements can lead to weight and structural design limitations, as well as require unnecessary buoyancy complications during operations.
WHY USE CFD?Good judgement is fundamental in solving any engineering problem. However, numerical simulations can help in making a good design even better. In his book Expert Political Judgment: How Good is It? How Can We Know?, social scientist Philip Tetlock shows how solutions derived from formal models such as CFD consistently outperform decisions based solely on
expert judgement. Today, with powerful Multidisciplinary Design Exploration (MDX) and Multidisciplinary Design Optimization (MDO) tools such as HEEDS, it has never been easier to make a design reach its best potential.In the oil & gas industry however, decisions relating to the platform orientation are still typically made solely based on previous experience and qualitative judgment, which can lead to unintentional biases. This study aims at improving the accuracy of experts’ predictions through the use of numerical
tools in order to meet the following design objectives:• Maximize ventilation• Minimize helideck impairment from
exhaust• Minimize wind chill effects• Minimize tendon stress• Minimize adverse lifeboat drift-off
Of course, using formal models doesn’t come without limitations. There are a few challenges associated with using CFD to resolve issues related to offshore platforms:
FIGURE 5: In case of emergency, lifeboats should drift away from the platform rather than into or underneath the platform.
FIGURE 6: With water depths reaching up to 7,000 ft, the tendon fatigue resulting from wave impact and drag loading is a signi�cant cost factor. Here, the platform depth is compared to the height of the Burj Khalifa, which is the tallest building in the world.
FIGURE 7: Social scientist Philip Tetlock [1] compared 20,000 predictions made by experts in their �elds with the predictions given by formal models, such as CFD. Every time, the formal models outperformed expert judgment.
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• Firstly, from a technical point of view, offshore platforms are very large and have extremely complex geometries. This makes it dif�cult, if not impossible, to explicitly resolve all objects within the available time frame.
• Secondly, from a project management point of view, projects are strongly schedule-driven: stakeholders want their platform to start running as early as possible since each day of delay will cost upwards of $10 million in deferred revenue.
• In addition, the platform orientation
is one of the �rst design aspects to be decided. However, in very early design stages, information is scarce. Many uncertainties need to be dealt with regarding the location of the equipment, etc.
• Finally, the budget allocation for Health & Safety is usually around 1% of the total project cost, which greatly limits the amount of in�uence technical safety bears on the �nal design.
The physics parameters used in STAR-CCM+ to represent the exhaust are as follows:
• Steady-state• Two-layer realizable k-epsilon
turbulence model• Segregated multi-component gas model• Gravity model to deal with the
buoyancy-driven exhaust �ow
The mesh parameters were set as follows:• Large scale objects are explicitly resolved• Small scale objects are represented by
sub-grid drag terms• Two to �ve million hexahedral cells• Locally re�ned on platform and helideck• Re�ned exhaust outlets
FIGURE 8: Mesh on the platform, showing local re�nements around the exhaust outlets and the helideck
FIGURE 9: Mesh re�nement around the exhaust outlet
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METHODOLOGYThe methodology is summarized in the table below:
STEP 1: Simulate wind from 16 direction and 2 wind speeds
STEP 2: Calculate helideck impairment from exhaust for each scenario
STEP 3: Calculate mean air speed through the platform
STEP 4: Calculate wind chill on the platform
STEP 5: Determine lifeboat drift collision probability
STEP 6: Calculate drag loading on hull as a surrogate for tendon stress
STEP 7: Combine all results using annual wind and current probability distributions
RESULTSThe cost functions for each individual design objective were calculated and are illustrated in Figures 10 to 14. Figure 15 shows the linearly weighted cost function for the combined objectives.The combined cost function shows that the optimum orientation of the platform, once all objectives are taken into account, is for its North to face True East-Southeast. This result does not coincide with any of the ideal orientations found for the individual design objectives, but is the best compromise between all these objectives.
CONCLUSION & FUTURE CONSIDERATIONSThe optimum orientation of the platform, with Platform North facing True East-Southeast, was obtained using simulation tools based on �ve design objectives: ventilation, exhaust,
HOT
COLD
FAST
SLOW
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FIGURE 11: Cost function for the exhaust objective, showing that the ideal orientation from the exhaust perspective is for Platform North to be aligned with True East. However, results would be acceptable anywhere between � = 250 and 330 degrees.
FIGURE 10: Cost function for the ventilation objective, showing that the ideal orientation from a ventilation perspective is for Platform North to be aligned with True North-Northwest
FIGURE 12: Cost function for the wind chill objective, showing that the ideal orientation from a wind chill perspective is for Platform North to be aligned with True Southeast. Note that the cost function curve is the opposite of the one obtained for the ventilation. This is explained by the fact that the wind chill is driven by the air speed in the same way as the ventilation, as well as temperature. In this speci�c case, the temperature was not cold enough to have much of an effect.
FIGURE 13: Cost function for the lifeboat drift-off objective, showing that the ideal orientation from a lifeboat drift-off perspective is for Platform North to be aligned with True South-Southeast, although any orientation θ between 180 and 260 degrees would be equally acceptable.
FIGURE 14: Cost function for the tendon stress objective, showing that the orientation doesn’t have any real in�uence on the tendon stress. This result may be explained by the symmetrical nature of the platform.
FIGURE 15: Cost function for all combined objectives obtained by linear weighting of the individual cost functions. It shows that the optimum platform orientation is facing True East-Southeast.
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LEGEND
wind chill, lifeboat drift-off and tendon stress. The approach taken in this case study considers an early stage of design, with parameters covering both safety and operational issues. As the design progresses, the number of parameters considered is expected to change, as will their weighted contribution. The idea is that the orientation can be further optimized as the design process progresses or in some cases completely alter the selection based on safety and operational prioritizations. If a proper balance of previous experience, qualitative judgement, and the use of formal models such as CFD are deployed,
this function method can be used to achieve an Inherently Safe Design. Further work could involve optimizing the facility layout based on: turbine stack design and positioning, helideck positioning, module placement, �are tower design, etc.
REFERENCES:[1] Philip E. Tetlock: Expert Political Judgment: How Good is It? How Can We Know?
For more information, contact [email protected]
SUB-SURFACE − SUBSEA & FLOW ASSURANCE − MARINE & OFFSHORE
PROCESS & SEPARATION − TECHNICAL SAFETY
DISCOVER BETTER DESIGNS.FASTER.
FLOW – THERMAL – STRESS
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GROUND TRANSPORTATION COME RAIN OR SHINE
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PRASHANT KHAPANE & UDAY GANESHWADEJaguar Land Rover
PRASHANTH SHANKARACD-adapco
INTRODUCTIONVehicles are designed to be as functional as possible in the harshest environmental conditions – snow, heat, rain, �ooding, etc. Unless you are blessed with sunshine and a warm breeze throughout the year, you have probably experienced the dreaded feeling of driving your car through �ooded roads, praying soundly that you don’t get stuck. While it is recommended to avoid �ooded roads, this is not always possible. The ability of a car to maintain its stability and functionality in this scenario, referred to as vehicle wading, is crucial. This is also detrimental to underbody components, bumper cover, electronic circuits, air intake (causing hydro-lock) and engine. All cars are designed with wading capability but the ability to wade through different depths of water varies based on the design. In this article, Jaguar Land Rover (JLR) talks about their revolutionary use of numerical simulation in vehicle wading testing, leading to better wading performance.
Unlike the aerodynamic design of the vehicle body where numerical simulation
COME RAIN OR SHINEJAGUAR LAND ROVER’S REVOLUTIONARY APPROACH TO VEHICLE WADE TESTING
plays a major role, vehicle wade testing is still the only design procedure to analyze and establish wade performance. Wading tests involve driving the car through different depths of water at different speeds. Often, the underbody design and placement of components and the structural design of the chassis has already been decided before wading tests commence and numerical simulation is not used. This leads to late detection of failure modes, expensive design changes, increased cost and time for testing and affects the program timing.
An established Computer Aided Engineering (CAE) process for vehicle wade testing can identify failure modes at an earlier stage, provide insight into the structural integrity of underbody components, and analyze multiple designs with con�dence, leading to testing of an optimum design. The savings associated with cost and time by using numerical simulation are enormous, in addition to better wading capability for the vehicle and structural integrity of the components.
CHOOSING THE RIGHT SIMULATION TOOLThe use of numerical simulation for vehicle wading testing is at a nascent stage in production environments. Wade testing is still the only procedure used here. As such, literature on best-practices and the use of CAE in vehicle wading is limited. The work done by Zheng et al [1] is the major reference for JLR’s development of the CAE process. Aside from this, JLR is the �rst OEM to publish literature on this topic. The need for this process was to understand the failure modes of under-body components early in the design stage and their effect on the vehicle performance and integrity.
The current testing procedure at JLR involves driving the vehicle over a ramp into a wading trough and using another ramp to exit the trough. Testing is done for different speeds and water depths. Various combinations of speed and depth produce differing behaviors in stability, splash pattern and bow wave formation in front of the vehicle. With numerical
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simulation, JLR aims to understand these different behaviors and optimize the under-body design.
With no historic literature or procedure available, JLR’s �rst challenge was to identify a computational tool capable of accurately modeling the motion of a vehicle through water. STAR-CCM+® was one of the contenders, in addition to a Smoothed Particle Hydrodynamics (SPH) code and LS- DYNA, another popular Navier-Stokes based commercial code.
The CAE process needed to accurately simulate the transient pressure forces on the under-body components due to the motion of the vehicle and water relative to each other. To accurately identify failure modes, the tool needed to handle modeling the motion of the vehicle in a fully-transient analysis. After careful
consideration of the tools, STAR-CCM+ was the clear winner due to its proven use in the automotive industry, Overset Mesh capability to model motion and a well validated Volume of Fluid (VOF) model to capture the air-water interface during wading. The motion modeling needed to be robust and be as close to the test scenario as possible. The Overset Mesh capability made STAR-CCM+ the clear winner. This technique involves two different mesh domains, one for the vehicle (overset region) and one for the background domain. This Chimera meshing technique will cut out the region of the background grid overlapping with the overset region leaving the bordering cells (acceptor cells) between the two regions which can communicate with each other through interpolation. This enables handling of large motions in a robust, accurate manner.
VALIDATING THE OVERSET MESHBefore applying the Overset Mesh approach to the vehicle wading simulation, it was imperative to validate this methodology for modeling an object motion into water. For this purpose, JLR scaled down one of their vehicles into a rectangular block to be tested in a towing tank. Six pressure sensors were placed on the block in testing to gather transient pressure data, which can be compared with the CFD results to validate the numerical approach. The box in test was 1000mm x 400mm x 500mm and tests were at water depths of 50mm, 1000mm and 180mm, at speeds of 0.87m/sec and 1.86m/sec. Figure 1 shows the overset mesh with hexahedral cells around the block in STAR-CCM+. The SST k-omega turbulence model in STAR-CCM+, well-validated
FIGURE 1: Mid-plane cross section of Overset Mesh and domain
FIGURE 2: Simulation result (right) at immersion depth of 180 mm and speed of 1.85 m/s compared to test (left)
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in the Marine industry, was used with the Volume of Fraction (VOF) model to capture the air-water interface. Pressure monitors were set up in the simulation at the exact locations as the six pressure sensors. Figure 2 shows the rectangular block at an immersion depth of 180 mm and speed of 1.85 m/s in both the towing tank and simulation. This shows good comparison of the water level around the block between test and CFD. In Figure 3, the correlation of peak pressure data (in mm of H2O) between test and simulation at the six sensor locations is represented for 180 mm and 1.85 m/s. The difference between simulation and test results for all scenarios was within 10%, which was deemed acceptable. In addition, the water level height comparison between CFD (0.158 m) and test (0.16 m) was also satisfactory establishing the validity of this simulation method.
VEHICLE TESTINGWith con�dence in the simulation strategy established, JLR moved to the vehicle wading testing and modeling. A Jaguar XJ was used for the wading tests, conducted in the wading trough at the Millbrook Vehicle Proving Ground in Bedfordshire. Sixteen waterproof pressure transducers were �tted on the underside panels (Figure 4) and bumpers. Protective stainless steel meshes protected the sensing diaphragm. The data acquisition and
signal conditioning system were set up in the rear of the vehicle, with shielded electrical signal wires to minimize contamination of test data. Different speeds and wading depths were tested. The vehicle started from standstill and data acquisition started before the vehicle entered the water and stopped when it came to a standstill.
CFD MODELING OF VEHICLE WADINGFor accurate modeling of the test environment, a CAD representation of the vehicle and the wading trough was
built and cleaned in Hypermesh and ANSA and brought into STAR-CCM+. The vehicle was aligned with the ramp entry and the wheels were �oating to enable rotation (Figure 5). A rectangular domain around the vehicle was created to be the Overset region which moves and the rest of the domain was modeled as the static background region. The coolpacks (intercooler, condenser and radiator) were modeled as separate domains to solve for porous physics along with normal physics and were connected to other regions by internal interfaces through which data interpolation takes place. A hexahedral trimmed mesh was automatically
FIGURE 3: Comparison of peak pressure data (in mm of H2O) at sensor locations for 180 mm, 1.85 m/s
FIGURE 5: Motion de�nition of vehicle and wheels and initial water in STAR-CCM+
FIGURE 4: Sensor locations (white marks) on the vehicle undertray
FIGURE 6: Comparison of transient pressure data in sensor 2 (undertray) at 450 mm, 1.944 m/s
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generated with proper re�nement around the coolpacks, water region and the motion path of the vehicle. The �nal mesh count was around 40 million cells. The Segregated Implicit Unsteady solver was used to resolve the �ow �eld and the VOF model solves for the multiphase �ow physics. Turbulence is modeled using the SST k-omega model and experimental data supplied the inertial and viscous resistance coef�cients for the porous �ow physics. A velocity inlet boundary condition was chosen at the domain inlet and the side and upper faces were designated as pressure outlets. A rotating (while entering trough) and translating motion were prescribed for the vehicle to model test conditions and tangential velocity boundary conditions is given at the wheels using local rotation rate. Sixteen pressure monitors were set up in the simulation at the same locations as the test to compare the results.
Figure 6 shows the comparison of transient pressure data in sensor 2 (undertray) between CFD and testing at 450 mm and 1.944 m/s. The transient pressure data from CFD in all scenarios was within acceptable limits in comparison to test data, especially on stiff components like the undertray. In �exible components such as the aero�ips, the numerical results were signi�cantly higher compared to test data. This is to be expected since these were modeled as rigid bodies in simulation while in testing, the de�ection from loading leads to reduced pressures. The front bow wave structure also corresponded well between CFD and experimental results.
One of the bene�ts of using STAR-CCM+ is the fully coupled, two-way, co-simulation capability with Abaqus, a leading Finite Element Analysis (FEA) structural solver from SIMULIA. Pressure data from STAR-CCM+ was mapped at various time intervals to Abaqus and the loads at various �xtures and high stress areas were obtained. This information is crucial in assisting the underbody design at an early. JLR modeled one-way coupling between the �uid and structure but future work will model two-way coupling. The Von Mises stresses on the undertray at a time step of 0.675 sec from Abaqus are seen in Figure 8.
A full multi-physics procedure is being validated currently on a simpli�ed model using STAR-CCM+ and Simpack, a Multi Body Simulation (MBS) software using a coupling tool called Multi physics Code Coupling Interface (MpCCI). This will allow the forces and torques from STAR-CCM+ to be transferred to Simpack to calculate
REFERENCES1. Xin Zheng & Xin Qiao, Fanhua Kong: “ Vehicle Wading Simulation with STAR-CCM+,” presented at FISITA World Automotive Congress, SAE China, Beijing, 2012
* Wading CAE is a patented CAE technique which has shaped the design of many JLR products.
jumping behavior when the vehicle enters water. Simpack then transfers the corresponding velocities back during jumping behavior to STAR-CCM+.
A VISIONARY APPROACHJLR has developed a revolutionary process for vehicle wade testing using numerical simulation, the �rst published work of its kind among OEMs. The Overset Mesh capability of STAR-CCM+ and
advanced physics models have helped JLR successfully integrate virtual testing into its process, giving better insight into the underbody component loading and potential failure modes at an earlier stage. Future work involving FSI and MBS in addition to CFD will result in an accurate virtual test bed for wade testing. The bene�ts are many; early detection of failure modes, ability to investigate multiple designs, reduced cost of testing, lesser delays in program timing and better wading capability.
FIGURE 7: Bow shock from the STAR-CCM+ simulation
FIGURE 8: Von Mises stresses on undertray at T=0.675 sec
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FLOW − THERMAL − STRESS − EMAG − ELECTROCHEMISTRY − CASTING − OPTIMIZATION REACTING CHEMISTRY − VIBRO-ACOUSTICS − MULTIDISCIPLINARY CO-SIMULATION
DISCOVER BETTER DESIGNS. FASTER.
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INTRODUCTIONAerodynamics for cars is strongly focused on drag reduction. As cars become lighter, they become more susceptible to unsteady events such as crosswinds, which are extremely dif�cult to reproduce in a wind tunnel. Engineering simulation software like STAR-CCM+® can offer valuable insight into such phenomena. In the work presented here, a dynamic coupling was established between the CFD simulation in STAR-CCM+, vehicle handling and driver models in MATLAB, in order to represent the complete and realistic movement of the vehicle on its suspension system. This work was carried out as part of the Programme for Simulation Innovation (PSI), led by Jaguar Land Rover and the Engineering and Physical Sciences Research Council (EPSRC).
DAVID FORBES & GARY PAGELoughborough University
FIGURE 1: Initial simulations use the full scale DrivAer model.
SIMULATING REAL-WORLD AERODYNAMICS FOR CARS
THE MODELSFor the CFD simulation, the CAD geometry of the DrivAer generic car model was used (Figure 1). This was developed at the Institute of Aerodynamics and Fluid Mechanics, Technische Universität München, in order to facilitate aerodynamic investigations of passenger vehicles, bridging the gap between strongly simpli�ed models such as the SAE and Ahmed bodies, and highly complex production cars. To be representative of real world applications, the model is full-scale and includes rotating wheels. About 20 million hexahedral cells were used (Figure 2).The MATLAB handling model was designed at Loughborough University for the university’s 6-DoF, Stewart-type platform driving simulator. It is a comprehensive and realistic dynamics
model that includes the full suspension system and driver’s response. When subjected to a crosswind excitation, the vehicle’s resonant frequencies may cause the driver to respond and either alleviate, or aggravate, the vehicle’s response. The aim of this research is to identify the frequencies where this happens in order to improve the vehicle’s crosswind stability.
COUPLING STAR-CCM+ WITH MATLABThere are three main approaches to coupling STAR-CCM+ with MATLAB:• The �rst method involves calculating
a set of steady-state solutions at different yaw angles in STAR-CCM+ and using interpolation methods to create a generic response to a crosswind.
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This response can then be input into the MATLAB model. This is probably the simplest way to approximate the response to a crosswind.
• The second way consists of running an unsteady CFD simulation; getting a transient history of the forces and sending them to the handling model.
• The third method is fully coupled: Aerodynamic data from the CFD simulation is sent to the handling model and the handling model gives back positional data at every time step (Figure 3).
The �rst two methods are known as one-way coupling: The system is open-loop, so the response from the handling model is not fed back into the CFD, which could change the aerodynamics. A lot of research has been done on these two types of coupling; but using a closed-loop, fully coupled system is fairly new. It is yet to be determined whether the low cost of one-way coupling outweighs the inherent accuracy limitations, or whether full coupling is needed for this type of simulation.In this case, the direct coupling was achieved with the use of a JAVA macro to connect STAR-CCM+ to the MATLAB simulation, permitting a data exchange at every time step. One considerable advantage of this type of coupling is the ability to run a CFD simulation on a large HPC system while the handling model in MATLAB is run on a local machine. This offers the opportunity to run part of the simulation on the other side of the globe, while controlling the handling model locally.
BUILDING THE ULTIMATE SIMULATION: AN ENTIRE OVERTAKING MANOEUVREAlthough yet to be performed, simulating an entire overtaking manoeuvre is built on the foundation of many preliminary tests. One of the �rst tests to be performed was
to simulate a very simpli�ed geometry responding to aerodynamic excitation using the overset mesh with in-built 6-DoF DFBI model in STAR-CCM+. Then the overset mesh zero gap interface was investigated to allow modelling of a contact patch between the tyre and the road for a true rotating wheel. Another step involved the coupling with MATLAB for the simulation of a crosswind: To start with, only the side force and yawing moment were included, but gradually a transition was made to a 6-DOF vehicle model that can roll and pitch on its suspension. Finally, a DES calculation with overset zero gap interface and direct coupling to MATLAB was performed, which
needed four to �ve days of runtime (Figure 4). The results from the fully coupled simulation were compared to their steady-state equivalents (see Figure 4). The comparison highlighted the fact that, the steady-state aerodynamics are unable to predict the unsteady �ow features that exist during such an event, most notably the over and undershoots of the yawing moment as the vehicle enters and exits the crosswind.Because the model is generic and fairly new, validation data, which tends to be speci�c to an existing vehicle, is still very limited. In addition, physical restriction makes wind tunnel testing of crosswinds extremely dif�cult.
Unsteady
Aerodynamics Vehicle
Dynamics/ Handling
Wind Excitation
Vehicle Response
Vehicle Position
Aerodynamic Forces and Moments
FIGURE 2: Dynamic overset meshing allows the motion of solid components within the �uid domain.
FIGURE 3: Work�ow diagram of the fully coupled 6-DoF simulation between CFD and vehicle handling-dynamics model
FIGURE 4: Vehicle aerodynamic response: Side force (left) and yawing moment (right)
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FUTURE CONSIDERATIONS & CONCLUSIONThis research has demonstrated how to fully couple STAR-CCM+ and MATLAB in a comprehensive and realistic ground vehicle simulation. For the automotive industry, this means that various tests, such as crosswind and overtaking manoeuvres, could ultimately be virtually performed at a much earlier stage in the design cycle, allowing design weaknesses to be eliminated before physical prototypes are developed; saving time and reducing costs.The next stage in this project will be to look at a complete overtaking manoeuvre, possibly with crosswind: starting off
with a vehicle placed in the wake of another, overtaking it and emerging on the lee side, into the crosswind, where its response will be analyzed.
This work was supported by Jaguar Land
"Various tests, such as crosswind and overtaking
manoeuvres, could ultimately be virtually performed at
a much earlier stage in the design cycle, allowing design
weaknesses to be eliminated before physical prototypes
are developed; saving time and reducing costs."
FIGURE 5: Wake visualization in STAR-CCM+
Rover and the UK-EPSRC grant EP/K014102/1 as part of the jointly funded Programme for Simulation Innovation.
Calculations were carried out using the HPC-Midlands hera system.
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YOSHITAKA YAMAMOTONissan Motorsports International Co., Ltd.
KUNINORI MASUSHIGE, YUKA TAKAHASHI & SAHAR FAZLICD-adapco
INTRODUCTIONNissan Motorsports International Co., Ltd. (NISMO), located in the Tsurumi district of Yokohama, is sacred ground for Nissan GT-R enthusiasts. NISMO is the development site of SUPER GT and other premier racing cars, as well as production and development of automobile parts. Launched in 1984 as the Nissan works team, NISMO has risen to the challenges of a long list of races, compiling an illustrious track record along the way. After three decades of legendary performance, success and distinction over its core competitors, NISMO uncovers how the unique capabilities of CFD helps the aerodynamic engineers improve their most recent aero-packages during the development process. Yoshitaka Yamamoto, the chief aerodynamicist of the development division of NISMO GT-R GT cars, believes that “CFD serves as an indispensable tool in development aimed at bringing visibility to the invisible dimension of �ow.”
SUPER GT SERIESThe SUPER GT, a FIA-sanctioned international race, contains two different regulations known as the “GT500” for the top class racing category and the “GT300” for the privateer category. - GT500 class: Consists primarily of vehicles developed and manufactured by the big three Japanese automakers
CROWNING THE SUPER GT SERIES CHAMPION FOR 2014:NISMO TAKES THE LEAD WITH STAR-CCM+
TO DESIGN DISTINCTIVE LOW DRAG CARS
Nissan, Toyota and Honda, along with their af�liated companies- GT300 class: Marked by a trend toward amateurs, with the majority of participating teams comprised of privateers
In the actual race, vehicles from these two different categories compete together on the same course. The speed differences translate into even more complex and exciting race conditions. Cars �ght tooth and nail to outstrip each other as they roar around the circuit, making the race very attractive.
The NISMO team develops and produces vehicles for both of these categories. In particular, the GT500 class vehicle, on which the company has truly staked its reputation as an automaker, is formed based on the GT-R. In 2013, the GT regulations became more aligned with those of the German DTM series, which stimulated new mechanical and aerodynamic changes for 2014 Nissan GT-R NISMO GT500. Utilized in its development is STAR-CCM+® – the flagship product of CD-adapco. In his role as chief aerodynamicist at the NISMO Development Division, Yoshitaka Yamamoto oversees the aerodynamic development for the GT500 GT-R, and shares his knowledge and experience in simulation through this article.
JUGGLING THE USE OF WIND TUNNEL TESTS AND CFD IN AERODYNAMIC DEVELOPMENTIn race car development, the traditional approach is to run wind tunnel tests on scale models, with visualization realized through pressure measurements at speci�ed points, smoke, tuft, PIV, and other methods. However, with these methods, it is almost impossible to visualize the entire domain of the aerodynamic �ow. On the other hand, the recent advances in CFD software, hardware, and computing capabilities, has enabled engineers to simulate a complete race car in a highly detailed CFD model from scratch, and to gain deeper insight into their designs, which would not be viable through any other means. CFD substantially helps with understanding the phenomena involved in �uid �ows, permitting accurate display and analysis of the information, with a level of detail that is hard to provide experimentally. It enables engineers to test the car virtually prior to any wind tunnel session, so as to pre-evaluate various con�gurations and “what if” scenarios, and submit to test only the most promising solutions. This makes CFD a widely accepted tool for the design and development of racing cars, complementing wind tunnel tests. Alongside this rapid growth of computing resources, it can be hoped that CFD can hold the promise of offering itself as the “digital wind tunnel” that can replace
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the physical testing in coming years as the science behind CFD improves and computers become even more powerful. Yoshitaka Yamamoto explains the contributions of CFD and the advantages this method offers in comparison to wind tunnel testing in aerodynamic development: “When attaching small parts to the car, for example, wind tunnel testing alone is not suf�cient to determine the impact of these parts on the backside, especially where the effects occur, and whether or not downforce has been obtained. CFD however offers valuable insight into the �ow behavior. GT cars are growing more complex every year. The number of intricate devices is also on the rise, making it increasingly tough to get the job done on the strength of experience alone. This is where CFD becomes necessary.”
HOW CFD INSPIRES NISMO Every aerodynamic engineer in racecar development has two major concerns; the creation of downforce to help push the car’s tires onto the track and keeping it from sliding off in corners due to centrifugal forces; and minimizing the drag
that is caused by turbulence and slows the car down. The harder and faster you drive, the more low pressure air (higher speed) goes underneath the car, and the more downforce will be created.
On the other hand, with increase in speed comes an increase in drag which is not desirable. The ideal setup is normally to get the maximum amount of downforce for the smallest amount of drag generated. However, the decision on either to create an aero-package which is balanced or leaning towards one of these two forces is highly dependent on the track and condition. A track with tight turns requires a car with higher downforce con�guration to navigate the turns. But on tracks with long straightway, wide and banked turns, such as speedways, less downforce is required; higher speed can be negotiated, and hence the drag reduction is of greater importance. When Yoshitaka Yamamoto re-joined NISMO in 2011, he started developing an aero-package with low drag speci�cation that improves the high speed, while prior to this year, all the improvements were carried out to increase downforce while maintaining the drag level. One of the initiatives behind
selecting the low drag speci�cation was to prepare an aero-package speci�cally designed for the Fuji speedway in 2012. Because of its long straight course, lap time could be decreased signi�cantly by reducing the drag.
Under the low drag speci�cations for 2013, the front fender assumed a con�guration along the lines of a steep wall (Figure 2). That resulted in an extremely distinctive front mask for the vehicles. Yoshitaka Yamamoto’s team conducted an initial CFD simulation using STAR-CCM+ to �nd the potential areas for drag reduction, and they found out that the pressure on the mentioned fender section has lowered comparing to their previous design in 2012 (Figure 3). Yoshitaka Yamamoto explains: “As a result of the CFD analysis, we realized that the front fender is an effective area regarding drag reduction. Under the regulations, the only means of expanding that area was to widen it vertically. When we attempted that, the results were favorable – as expected.” Prior to that, they had attempted to eliminate the drag by rounding the con�gurations, but with the help of CFD, the NISMO team understood
FIGURE 1: Chief aerodynamicist Yoshitaka Yamamoto of the NISMO Development Division, posing alongside the 2014 NISMO GT-R.
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that it was possible to reduce such drag without streamlining the shape in that way. They featured this so-called approach as “aerodynamic harnessing modulation” in their 2014 model. This refers to raising performance through the adept use of pressure differences – a step that cannot be taken without CFD. Yoshitaka Yamamoto highlights the impact of CFD in
their new achievement, as he continues: “Measurements may be carried out in wind tunnels as well, but the visualization that becomes necessary is dif�cult to achieve over the entire vehicle. CFD is an effective tool to view the body as a whole.”Although CFD has led Yamamoto’s team to a better design by offering a valuable insight on the entire design
continuum, it has been used until now in a supplementary role rather than as a replacement for the work in the wind tunnel. As he explains: “One of the areas that still has room for improvement lies in evaluating portions that bear the wake of the parts in the front. As a case in point, I still regard assessments of tire wake as posing a stiff challenge. In addition to the wake, we have also failed to get 100% satisfactory results with the rear diffuser and other reverse pressure gradients. If we can achieve that, I feel con�dent that the time will arrive when we no longer need wind tunnels.”
APPLYING TECHNOLOGIES CULTIVATED THROUGH MOTOR SPORTSThe GT500 vehicle development work involves pooling the essence of various different technologies. NISMO channels know-how cultivated through motor sports into the development of commercially marketed cars like GT-R NISMO, JUKE NISMO and other Nissan high-performance vehicles as the automaker’s self-styled “performance brand.” Yoshitaka Yamamoto explains that “with sports-oriented models that bear the name ‘NISMO,’ we instill our race car expertise into the development work. These vehicles have a key selling point featuring their high-performance. Any car carrying the ‘NISMO’ name is supported by development on the aerodynamics side by NISMO engineers, which means that I am also personally engaged in the work.”
2015 NISMO GT-R CURRENTLY UNDER DEVELOPMENTIn 2014, SUPER GT vehicle regulations were uni�ed in tune with the German Touring Car Championship (Deutsche Tourenwagen Masters, DTM), with broad revisions carried out. For that reason, it became necessary to coordinate the development work with each of the
FIGURE 2: Front mask engineered to reduce drag (photographed at the NISMO showroom)
FIGURE 3: CFD results using STAR-CCM+ show reduced pressure on the front fender for the 2013 design (right) compared to the previous design (left)
FIGURE 4: JUKE NISMO (above) and GT-R NISMO (below)
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new regulations. Yoshitaka Yamamoto recaptures: “Because 2014 necessitated the �rst-ever use of regulations integrated with the DTM, we couldn’t tell which of the various rules offered advantages or disadvantages for GT cars. Today, we’ve generally ironed out the details in that regard. For example, we understand that the know-how for the upper side of the exterior is generally uniform, meaning that there were actually no differences linked to the amended regulations.”
CFD AND DEVELOPMENT OF VISIONARY REAR FENDER TURNING VANE1
The regulation for the second Fuji race in 2012 stipulated that engineers were at liberty to tweak the design of the rear fender. From that time onward, this has become a standard measure for all F1 races. Following the regulation, NISMO attached turning vanes that had winged cross-sections mounted on the rear fender (Figure 5). According to Yoshitaka Yamamoto, the primary goal was to create an eye-catching external appearance that could be presented in the F1 series.
In addition to its distinctive appearance, it also contributed positively to the rear wing performance. As can be seen in Figure 6 (left), CFD results showed that the air �ow churned up from the wheel arch lip on the front fender alleviated the breaking away at the rear fender and top surface of the trunk lid. While, as shown in Figure 6 (right), the addition of the rear fender turning vane changed the �ow pattern and improved the �ow attachment towards the trunk lid. This ultimately helped reduce the drag and improve the ef�ciency of the rear wing. But it is notable that the upside-down winged cross section pro�le of the turning vane generated lift in speci�c locations near the front, back, and center of the vehicle. Therefore, the turning vanes were not devices to improve the downforce. But the aerodynamic team approved its addition since it had drag reduction effects, and hence improved the overall performance of the rear fender. However, the ruling authorities (GTA) had a different interpretation of the newly mounted turning vanes. While the rules stipulated only ‘one wing,’ GTA considered the rear
fender turning vane as the second wing. Therefore, it was shelved after having been installed for only ten minutes during the open inspection, and became little more than an illusion.
CONCLUSIONNISMO was crowned series champion in 2014. In their aero-package development process to reach the optimum low drag speci�cation, the aerodynamic engineers at NISMO bene�ted from the great synergy between STAR-CCM+ results and the wind tunnel testing. Accurate pre-evaluation, deeper insight on a full scale model and the ability to assess as many con�gurations as possible are the great advantages for racecar developers at NISMO to continue using CFD as an indispensable tool in their development process in future. Following the interview for this article, the company announced its plans to participate in the Le Mans 24 Hours Race, further fueling the need for CFD in vehicle development. Hence, once again, STAR-CCM+ will be participating into the racing battle along with NISMO.
REFERENCE1. “Motor Fan Illustrated Special Edition”, Kota Sera, Motor Journalist
FIGURE 6: Flow patterns with (left) and without (right) the rear fender turning vane
FIGURE 5: The shape and position of the rear fender turning vane
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REACTING CHEMISTRY − VIBRO-ACOUSTICS − MULTIDISCIPLINARY CO-SIMULATION
DISCOVER BETTER DESIGNS. FASTER.
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AEROSPACE QUIET, RELIABLE AND EFFICIENT!
JOBY AVIATIONJoby Aviation is a privately-held company headquartered in Santa Cruz, California. In 2009, leveraging the control systems and electric propulsion systems developed at Joby Energy, Joby Aviation was founded to revolutionize how we commute. Joby Aviation's strengths in composite airframe design and fabrication, high-fidelity aerodynamic analysis, and through the sister company Joby Motors (www.jobymotors.com), high-performance electric motor design and fabrication, place it in a unique position to create a new generation of electric personal aircraft.
ALEX STOLLAlex Stoll is an aeronautical engineer at Joby Aviation and is the chief designer of the Joby Lotus and S2 aircraft. He holds a B.S. in mechanical engineering from Rice University and an M.S. and an Engineer’s degree in aeronautics and astronautics from Stanford University.
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ALEX STOLLJoby Aviation
QUIET, RELIABLE AND EFFICIENT!JOBY AVIATION PAVES THE WAY FOR AN ELECTRIC FUTURE
INTRODUCTIONElectric propulsion is on the verge of causing the biggest changes in aviation since the advent of the jet engine. At �rst glance, it may seem that the excessive weight (i.e. low speci�c energy) of today’s batteries limits electric aircraft to, at best, a few trivial niches. However, the different properties of electric propulsion compared to traditional combustion power, coupled with recent technology advances, promise to signi�cantly relax typical design constraints for many aircraft con�gurations, which will allow for new types of aircraft that were previously impractical or impossible. This is particularly true for shorter-range designs, which have traditionally been relatively small and piston-powered.
WHY ELECTRIC PROPULSION?Because of the size, weight, and maintenance requirements of piston engines, most piston aircraft designs are limited to a small number of engines (often just one) located in a small number of practical locations. This is why most modern general aviation airplanes and helicopters look very similar to designs from the 1950s. In contrast, electric powertrains are much smaller and lighter, and they are incredibly simple – some having only a single moving part – compared to the relatively extreme complexity of piston engines, which include a coolant system, an electrical system, an oil system, a fuel system, and so forth. This reduced complexity translates to much lower maintenance requirements.
While smaller combustion engines suffer from lower power-to-weight and ef�ciency, electric motors are relatively scale-free. This means that the power-to-weight and ef�ciency will be similar between, for example, a 1 kW motor and a 1,000 kW motor. An electric powertrain is also about three times as ef�cient (around 90%-95% compared to around 30%-40%). Electric motors can operate well on a much wider range of RPMs, and they can change RPM relatively quickly.
Electric powertrains are signi�cantly quieter than combustion powertrains,
as anyone who has heard an electric car can attest.
While simply replacing a combustion engine with an electric motor will see the bene�ts of lower noise and higher powertrain ef�ciency, much greater advantages can be gained by designing an aircraft with electric propulsion in mind from the start. The different properties of electric propulsion mean that aircraft can effectively employ a large number of small motors without incurring an undesirable amount of complexity (and maintenance costs) and without compromising on motor weight or performance. These motors can be located in a much larger range of positions on the aircraft, due to their relatively low weight and small size. Additionally, the drawbacks of carrying motors that are only used in some portions of the �ight (e.g. takeoff and landing) are relatively minor, since the motors themselves are so light.
While traditional propulsion installations often compromise aircraft performance – for example, the scrubbing drag caused by a tractor propeller increasing the velocity over the fuselage – the �exibility of electric propulsion allows for propulsion installations that actually result in bene�cial aerodynamic interactions. One such example is locating propellers on the wingtips, where they can recapture some of the energy lost to the wingtip vortices.
With its expertise in electric motor design and fabrication, high-�delity aerodynamic analysis, and composite airframe design and fabrication, Joby Aviation is fully capitalizing on the promise of this new technology to develop several aircraft providing capabilities that were never before possible. However, due to the complex nature of these interactions and the lack of previous designs to extrapolate from, a large amount of high-order aerodynamic analysis must be performed in the design process. For this reason, Joby Aviation has leaned heavily on CFD analyses using STAR-CCM+® in the development of its unconventional designs.
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JOBY S2Joby Aviation’s main development effort is the S2 Vertical Takeoff and Landing (VTOL) aircraft, shown in Figure 1, which addresses the high noise, high operating costs, low speed, and relatively low safety levels that, together, have severely limited the proliferation of conventional VTOL aircraft of this size (helicopters). The S2 employs multiple propellers in takeoff and landing to increase safety through redundancy. In cruise, most of these propellers fold �at against their nacelles to reduce drag. The design of these propeller blades is a compromise between propeller performance and the drag of the nacelles with the blades folded, and higher-order tools were required to properly analyze this tradeoff. A variety of propeller designs were assessed under various operating conditions in STAR-CCM+, and the nacelle was analyzed in the cruise con�guration using the γ-Reθ transition model. One such nacelle geometry can be seen in Figure 2, where both the unmodi�ed clean nacelle and the same nacelle with the folded blades and spinner gaps are shown. Such results indicate where reshaping the propeller
blades may increase laminar �ow and reduce cruise drag.
JOBY LOTUSAnother Joby Aviation project is the Lotus aircraft, shown in Figure 3, which is exploring a novel VTOL con�guration on the 55-pound UAV scale. In this aircraft, two-bladed propellers on each wingtip provide thrust for vertical takeoff. After the aircraft picks up enough forward speed for suf�cient wing lift, each set of two blades scissors together and the individual blades become wingtip extensions, forming a split wingtip. A tilting tail rotor provides pitch control during takeoff and landing and propels the aircraft in forward �ight. The takeoff and cruise con�gurations of the Lotus are illustrated in Figure 4. As one may expect, the design of these wingtip blades – the span, airfoil choice, twist and chord distribution, pitch, and dihedral – was an interesting compromise between propeller and wingtip performance. Dozens of CFD simulations were run on different combinations of these design variables in the cruise con�guration, to maximize the
cruise performance within the constraints of the con�guration. At the same time, the performance of these blades in the propeller con�guration was also analyzed with CFD to validate lower-order design methods. Example results from some of these simulations are shown in Figures 5 and 6.
LEAPTECHThe third project Joby Aviation is participating in is LEAPTech (Leading Edge Asynchronous Propeller Technology), a partnership with NASA and Empirical Systems Aerospace. The goal of this design is to investigate potential improvements in conventional �xed-wing aircraft through electric propulsion. A row of small propellers is located along the leading edge of the wings and, during takeoff and landing, these propellers increase the velocity (and, therefore, the dynamic pressure) over the wings. This increases the lift produced by the wing and allows for a smaller wing to be used for the same stall speed constraint. Since many small aircraft use a wing sized to meet a stall speed constraint but too
FIGURE 1: A rendering of the Joby S2
FIGURE 2: CFD analysis of the S2 nacelles, showing a clean nacelle (left) and a nacelle with folded blades and spinner gaps (right)
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FIGURE 3: The Lotus during assembly, in cruise con�guration
FIGURE 4: The Lotus in takeoff (left) and cruise con�gurations (right)
FIGURE 5: CFD analysis of the Lotus in cruise con�guration FIGURE 6: CFD analysis of the Lotus wingtip propeller at takeoff
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large for optimal cruise performance, this smaller wing allows for more ef�cient cruise. Additionally, the ride quality is signi�cantly improved due to the higher wing loading. However, the performance of this blown wing is dif�cult to analyze with lower-order tools, particularly since much of the required analysis occurs around stalling conditions. Therefore, a large number of CFD simulations were performed in the design process, looking at various combinations of propeller sizes and powers, wing aspect ratios and sizes, angles of attack, etc. To reduce the computational expense, the propellers were modeled as actuator disks with the body force propeller method in STAR-CCM+, which negated the need to resolve the actual blade geometry, drastically decreasing the required mesh size.
The �rst phase of testing this con�guration was to build the full-scale wing, propellers, and motors, and mount them above a modi�ed semi-truck which was run at takeoff speeds on the runway at NASA Armstrong Flight Research Center. An example CFD solution of this con�guration is shown in Figure 7, and the experimental
test apparatus is shown in Figure 8. Outside of takeoff and landing, these leading-edge propellers are planned to fold against their nacelles – similar to the S2 propellers – and wingtip propellers, as mentioned above, will provide propulsion. Although lower-order analysis methods were evaluated for estimating the drag and ef�ciency impact of operating these propellers concentric with the wingtip vortex, unsteady CFD proved to be the most reliable analysis method. A range of design parameters were analyzed, and one such solution is shown in Figure 9. A �ight demonstrator is planned for �ights beginning in 2017; a rendering of this aircraft is shown in Figure 10.
CONCLUSIONJoby Aviation is quickly advancing the state of general aviation aircraft with its revolutionary electric propulsion concepts, and simulation is playing a big role in understanding the complex nature of their state-of-the-art ideas and in the design and development of their unconventional systems. The S2, Lotus, and LEAPTech designs show great promise towards an electric future in aviation never before possible.
FIGURE 8: The LEAPTech experimental test apparatus at NASA Armstrong (NASA photo)
FIGURE 9: CFD simulation of a wingtip propeller
FIGURE 7: CFD simulation of the LEAPTech wing at takeoff; the propellers are modeled with actuator disks.
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"Joby Aviation's unconventional aircraft designs benefit from an unusual degree of coupling between the propulsion and airframe design; however, this coupling complicates the analysis, since low-order tools are not powerful enough and statistical methods are less useful due to the lack of significant historical data. For these reasons, STAR-CCM+ has been an extremely valuable component of our design and analysis toolkit."
FIGURE 10: Rendering of the LEAPTech demonstrator (NASA photo)
QUIET, RELIABLE AND EFFICIENT! AEROSPACE
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As telecommunication and Radio-Frequency (RF) power electronics applications continue to push the envelope of waste heat dissipation, we increasingly see a need for active thermal control employing forced air electronics cooling fans in unison with pumped �uid loops to successfully meet temperature and performance requirements. A system-level approach using simulation tools enables examination of “cause-and-effect” scenarios for such novel electronics cooling solutions and helps to address the ever-changing requirements of affording more power in a smaller packaging level.
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KEVIN R. ANDERSONCalifornia State Polytechnic University
FIGURE 1: Thermacore k-Core technology [2]
INTRODUCTIONIn this article, STAR-CCM+® is used for heat transfer and �uid �ow simulations of a novel heat exchanger/cold plate fabricated from k-Core high thermal conductivity material in order to realize a thermal control system hardware design for intermediate compact electronics packaging scenarios with large power densities. K-Core thermal spreader cold plates provide high-ef�ciency heat transfer in the absence of moving parts and are invaluable in applications where space, volume, seamless hardware integration and weight constraints dictate thermal design solutions. In this work [1], trade studies involving different heat exchanger/cold plate materials as well as various fault scenarios within a typical electronic system are investigated to illustrate the upper bounds placed on the convective heat transfer coef�cient.
HEAT SPREADER THERMAL TECHNOLOGYPhysically, k-Core-based thermal spreader cold plates absorb heat from sources such as high power dissipating electronic components, rejecting it to ambient air or liquid coolants. These types of cold plates are well suited for operating in harsh environments. For cooling high-power density applications (e.g. semiconductors, lasers, power generation, medical equipment, transportation, military electronics, etc.) and other demanding applications where air cooling is insuf�cient, liquid cooling is essential. In this work, the heat transfer of k-Core
MORE POWER TO YOU !AFFORDING MORE POWER IN SMALLER PACKAGES USING NOVEL ELECTRONICS COOLING SOLUTIONS
thermal conduction plates available from Thermacore, Inc. [2] is studied in conjunction with a pumped �uid loop.
The physical concept of Thermacore’s k-Core heat transfer technology is illustrated in Figure 1. Using encapsulated Annealed Pyrolytic Graphite (APG) allows one to manufacture a device such as a heat spreader with heat sink integrated on one face while a labyrinth of tubing for a pumped �uid loop can be fabricated on the opposing face. Since APG is anisotropic in thermal conductivity, with
the x- and y-components of thermal conductivity being very large with respect to the z-component, the APG heat sink/heat spreader greatly enhances overall heat transfer via preferential heat spreading. Figure 2 illustrates the temperature dependency of the thermal conductivity of APG.
SYSTEM HARDWARE DESCRIPTIONThe con�guration of Figure 3 was selected to represent a medium-sized high power
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density electronics application. The overall system consists of a very large housing with eight identical sub-modules arranged in order to minimize overall system volume. The sub-modules were each populated with various components, totalling twelve each per sub-module. The thermal control system has three major components: the heat sink, the cold plate and the forced convection fans/housing enclosure. The enclosure is roughly the size of a footlocker (W x L x H = 0.5 m x 1.0 m x 0.5 m).
CFD MODEL AND GRID STUDYThe mesh for the simulations with STAR-CCM+ consisted of a polyhedral unstructured mesh with �ve prismatic layers to resolve the boundary layer at all solid-�uid interfaces (Figure 4).Typical cell count for the system-level CFD model was on the order of 2.5 million cells; the particular mesh size was selected from runs to determine the optimum mesh size giving accurate answers as quickly as possible. CPU run-time on a 64-bit quad-core workstation was on the order of six hours.
The simulations included fully 3D conjugate heat transfer, forced external air convection, forced internal liquid cooling loop, solid conduction, and surface-to-surface gray body radiation. To mimic typical Commercial Off-The-Shelf (COTS) cooling fans, the fan curve (for a fan rated at 30 CFM with 50 Pa static pressure) of Figure 5 was used in the simulations.
BASELINE RESULTSThe baseline simulation results showing temperature distribution and streamlines are illustrated in Figure 6. The parameters
for each of the hardware subcomponents in the simulation were set up so the power density at the liquid cold plate is 1.6 kW/m2, which is in the category of a very high power density application. Once the baseline results were obtained, trade studies were performed to determine system sensitivities.
EFFECT OF CONDUCTIVITY ON HEAT TRANSFERThe primary objective of this work is to demonstrate the heat spreading characteristics of APG k-Core material in comparison to traditional heat sink materials. To accomplish this, the thermal conductivity of the heat sink/cold plate was varied from a very low (bare aluminum) to a very high value (APG k-Core). The results of this parametric study are shown in Figure 7 showing the
expected linear variation of the system temperature as a function of the heat sink/cold plate thermal conductivity.
EFFECT OF INLET FAN AIRSPEEDThe thermal performance of a system will signi�cantly vary with the �ow rate provided by the fan and a key way to measure the variance is with the convective heat transfer coef�cient. Figure 8 shows values of convective heat transfer coef�cient for a typical CFD simulation. These were in agreement with the heat transfer literature [3] for systems of this category.
To quantify the effect of inlet fan air speed on the heat transfer coef�cient, a series of simulations was performed with varying �ow rates of air, resulting in a different average velocity emanating from
FIGURE 2: Thermal conductivity for APG material [2] FIGURE 3: Electronic system components
FIGURE 4: CFD mesh used for simulations
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the fan. Figure 9 shows the results of the simulations compared with Ellison [4].
The correlation of Ellison is conservative, as expected, and corresponds to standard textbook-based correlations with typical uncertainties of at least 25%. The data from our CFD study are also found to be in qualitative agreement with the simulations and correlations offered by the research of [5] and [6].Next, the heat transfer coef�cient of the
system is plotted against temperature delta of the system as shown in Figure 10. The CFD data points are assigned a power-law curve �t and compared to the correlation offered by [7].
The handbook correlation is found to once again over-predict the heat transfer coef�cient. As mentioned above, the heat transfer correlations are expected to carry an uncertainty of at least 25%. The agreement of the CFD results in Figures
9 and 10 are within the realm of this uncertainty range.
EFFECT OF COOLANT FLOW RATENext, a series of CFD simulations was carried out varying the inlet �ow rate of the water in the cold plate. The heat transfer coef�cient of the entire system is plotted as a function of the �ow rate in Figure 11.
FIGURE 5: Fan curve speci�cations for thermal control hardware simulations
FIGURE 6: Streamlines colored by air stream speed and electronic components colored by temperature
FIGURE 7: Thermal conductivity effect on heat spreading in CFD simulations
FIGURE 8: Convective heat transfer coef�cient contours
FIGURE 9: Heat transfer coef�cient versus fan air speed
FIGURE 10: Heat transfer coef�cient comparison
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As can be seen in Figure 12 the heat exchanger/cold plate assembly maintains a relatively constant temperature. This is the primary advantage of utilizing a pumped �uid cold plate apparatus, i.e. it gives one the ability to hold a nearly isothermal boundary condition in an actual thermal system.
EFFECT OF CONTACT RESISTANCEA trade study was performed on the thermal interface material providing thermal conductance between the various components in the system. Simulations were done for a range of contact heat transfer coef�cients for typical thermal interface materials in the commercial electronics cooling industry (ranging from a “very good” thermal interface to a “very poor” thermal interface). Figure 13 plots maximum temperature in the system versus thermal contact resistivity. As theory predicts, the trend is linear.
Figure 14 shows isothermal contours for a sub-module which has a very poor thermal contact interface. The image
shows a wide range of temperatures, with the lowest temperature where the PCB card contacts the heat sink/cold plate assembly, to a highest value where the warmest component on the PCB is near the air �ow region. Here, the impact of a poor thermal contact interface is profound, leading to a large temperature gradient across this particular sub-model.
THERMAL RUNAWAY AND FAN OUTAGEThermal runaway due to faulty components in the system is readily predicted using this simulation approach. Figure 15 shows the result of a bad component (e.g. due to a manufacturing �aw or an internal short) on a particular PCB. The component experiencing thermal runaway is immediately identi�ed as the outlier with very high temperature in Figure 15.
One common cause of failure in thermally-controlled electronic systems is fan failure. This forces the thermal designer to use a redundant system, i.e. back-up
fans. To understand the effects of fan failure, various CFD simulations were performed at different air�ow rates. Figure 16 is a plot of the system level maximum component/chip temperature versus fan air speed. The trend depicted is as expected, i.e. as the fan speed approaches zero, the various components and PC cards within the sub-modules will witness a large temperature. The results are in agreement with the trends in the study of [5] and [6].
CONCLUSIONSTAR-CCM+ simulations of heat and fluid flow behavior in a moderately sized package of electronics undergoing very large power dissipations enabled the study of novel thermal control strategies at the system level. Results were in agreement with previous studies, and the approach used demonstrated quick and cost-effective evaluation of “cause-and-effect” scenarios for new electronics cooling solutions. This helps address the ever-changing requirements of delivering more power in smaller packages.
FIGURE 11: Heat transfer coef�cient versus cold plate �ow rate FIGURE 12: Heat sink/cold plate temperatures
STAR-CCM+ has enabled our team with a turn-key solution to complex CFD problems. By using STAR-CCM+, our research team has been able to address critical path design related issues. e user friendly interface and technical support offered by CD-adapco help put STAR-CCM+ at the forefront of today's CFD and multi-physics CAE simulation tools in both industry and academia.
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The approach used with STAR-CCM+ demonstrated a quick and cost-effective evaluation of cause-and-effect scenarios for new electronics cooling solutions. This helps address the ever-changing requirements for delivering more power in smaller packages.
REFERENCES[1] K.R. Anderson, M. Devost, W. Pakdee, N. Krishnamoorthy: “STAR-CCM+ CFD Simulations of Enhanced Heat Transfer in High-Power Density Electronics Using Forced Air Heat Exchanger and Pumped Fluid Loop Cold Plate Fabricated from High Thermal Conductivity Materials,“ Journal of Electronics Cooling and Thermal Control, 2013, 3, 144-154[2] “Thermacore k-Core Data Sheet,” 2013: http://www.thermacore.com/products/kcore.aspx [3] F. P. Incropera & D. P. Dewitt: “Heat Transfer,” Mc- Graw-Hill, New York, 1991[4] G. Ellison: “Thermal Computations for Electronics - Conductive, Radiative, and Convective Air Cooling,” CRC Press, Boca Raton, 2011[5] I. Tari & Y. Fidan-Seza: “CFD Analyses of a Notebook Computer Thermal Management System and a Proposed Passive Cooling Alternative,” IEEE Transactions on Components and Packaging Technologies, Vol. 33, No. 2, 2010, pp. 443-452[6] M. A. Ismail, M. Z. Abdullah & M. A. Mujeebu: “A CFD Based Analysis on the Effect of Free Stream Cooling on the Performance of Micro Processor Heat Sinks,” International Communications in Heat and Mass Transfer, Vol. 35, No. 6, 2008, pp. 771-778 [7] Y. A. Cengel: “Heat Transfer - A Practical Approach,” McGraw-Hill, New York, 2010
FIGURE 13: Interface thermal contact resistivity versus maximum temperature
FIGURE 14: Effect of poor thermal contact resistance
FIGURE 15: Thermal fault simulation of defective chip FIGURE 16: Maximum component/chip temperature versus cooling fan inlet air speed
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ENERGY & POWER AIR POLLUTION CONTROL
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STEWART BIBLEFuel Tech, Inc.
FIGURE 1: General arrangement of common APC equipment in solid fuel �red power generating combustion unit
INTRODUCTIONFuel Tech, Inc. is a world leader in providing advanced engineering solutions for Air Pollution Control (APC) in power plant utilities. Their NOx reduction technologies are now installed worldwide on over 800 units, providing customers with the most cost effective and environmentally sustainable methods to produce energy and processed materials. The recently patented Graduated Straightening Grid (GSG®) technology, which has been developed with extensive CFD modeling, is capable of dramatically improving upon typical Selective Catalytic Reduction (SCR) unit performance by optimizing �ue gas velocity pro�les, reagent consumptions, and catalyst lifetimes while minimizing system pressure losses.
In one of the �rst GSG retro�t applications at Kansas City Power & Light Company’s
THE IMPACT OF CFD IN DEVELOPMENT OF FUEL TECH’S INNOVATIVE NOX REDUCTION TECHNOLOGY
La Cygne Station Unit 1, the installation of the new technology has resulted in operational cost savings of more than $5 million associated with reduced system pressure drops, reduced build-up of particulate on catalyst layers, less catalyst erosion and longer catalyst lifetime. But most impressive is how Fuel Tech has developed this technology and attained its current industry standing. In this article, the reader will be walked through the development of the GSG which is now part of all new Fuel Tech SCR applications.
BACKGROUNDWith the urge to reduce emissions to meet directives in Europe, as well as new rules proposed by the U.S. Environmental Protection Agency (EPA), many fuel resources, including coal, are faced with being expelled from the market due to
environmental control costs. However, environmental rules typically trigger the development of new cost-effective technologies for emission reduction. As a result, one of the foremost applications of Computational Fluid Dynamics (CFD) and Experimental Fluid Dynamics (EFD) modeling over the past ten years has been in the area of optimizing Air Pollution Control (APC) systems used to reduce emissions from fossil fuel �red stationary sources such as power plants and steel mills. The most common of these are depicted graphically as a general arrangement of equipment on a power generation unit in Figure 1.
The SCR technology was developed in the 1980s in Japan to effectively reduce NOx concentrations in �ue gas by the injection of ammonia (NH3) reagent and the reaction via a catalyst substrate. This development was critical in helping coal
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remain in the market as the most cost-effective fuel source.Despite the relatively developed nature of this technology, there still exist challenges for coal-�red power plants with SCR units that have been dif�cult for owners and operators to overcome, such as:• Minimization of the system pressure
losses with the aim to reduce fan power consumption and power plant operating costs,
• Achieve optimally distributed concentrations of velocity, NOx, NH3 reagent, and temperature pro�les entering the SCR reactor, enabling better reduction ef�ciencies and lower ammonia usage,
• Ensuring uniform gas velocity vectors at the catalyst in order to reduce the risk of catalyst erosion and consequently extend catalyst life by lessening catalyst wear and deactivation,
• Avoid particulate dropout and ash accumulation in SCR units which increase system pressure losses, unit downtime for cleaning purposes, and reduce catalyst lifetimes.
With the recent signi�cant progress in computing hardware capabilities, engineers at Fuel Tech now take advantage of extensive use of CFD in order to develop �ow distribution devices that successfully address these SCR-operation-related issues.
FLOW TACK AND DEVELOPMENT OF THE GSG:A HISTORICAL VIEWIn 2003, Flow Tack, LLC, was formed as an engineering consulting company providing CFD and EFD services as a design tool for APC technologies including SCR, static mixers, Electro-Static Precipitators (ESP), baghouses, and Flue Gas Desulphurization (FGD) units. The conventional apparatus for �uid dynamics optimization of APC technology was EFD (example Figure 2). By use of this method Flow Tack engineers found that experimental modeling suffered from the following drawbacks and limitations. With experimental models one does not have access to the full continuum of �ow �eld data and the engineering analysis is limited to the �nite number of points at which measurements are taken. There is always the paradox of locating discrete measurement points that are required to best capture potential problem areas which are not a priori known. On top of that, there is the need for highly-skilled experimentalists to gather and apply the data. Lastly, the time required to test, then modify, and then retest experimental models becomes a signi�cant limiting factor in the degree of system optimization when complying with typical project schedules.
For these reasons, Flow Tack was an early adopter of CFD and helped promote the use of this revolutionary tool in the APC marketplace. Among other bene�ts, CFD made it possible to look at the entire continuum of �ow vectors. By looking at the full continuum, Flow Tack engineers noticed recirculation being caused by typical SCR �ow distribution devices that were not always evident in experimental models and it became obvious that such �ow conditions were the cause of many particulate related SCR problems. CFD also made it inexpensive to try many different �ow distribution device con�gurations in order to optimize the velocity pro�les entering the catalyst, and thus reduce the potential for ash accumulations. As shown in Figure 3, the GSG design was aimed to minimize horizontal surfaces predisposed to ash build-up in order to provide a much denser grid of plates, resulting in a more uniformly distributed resistance and consequently, a better velocity pro�le into the catalyst.
Upon its invention, the GSG performance was veri�ed using experimental �uid dynamics modeling on a 1-to-10 scale model. At �rst, the experimental results did not replicate the CFD results to Flow
FIGURE 2: Typical experimental 1-to-10 scale �uid dynamics model testing of SCR system
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Tack’s satisfaction. Improvements in the fabrication of the experimental models and technological advancements in CFD modeling, such as mesh re�nement in areas of interest, eventually led to a set of modeling standards for both model types that provided suf�cient agreement between results. Since then, Flow Tack has gone from project to project, and with minor adjustments, found the optimized turning vane con�gurations for each project using this simulation process. In 2007, with the support of STAR-CD® 3.24, Flow Tack parameterized the successful instances of the GSG and applied for its patent. The technology has
been further developed and �ne-tuned using STAR-CCM+® in the subsequent years.
In 2008, Flow Tack was acquired by the APC technology company, Fuel Tech, Inc., which also had been developing Selective Non-Catalytic Reduction technology (SNCR) using CFD. Both companies had many overlapping goals in promoting SCR technology development. A major reason for Flow Tack’s acquisition was their signi�cant work on applying CFD to develop new methods in mixing and optimization of APC equipment. The GSG patent was approved in 2012 and has been successfully applied to several
operating power plants and industrial facilities with SCRs starting in 2009 (see Table 1).
HOW GSG IMPROVES SCR PERFORMANCEOne of the major issues with SCR units is the frequently reported particulate and ash accumulation on the horizontal face of the catalyst. The reactive solution is to stop the unit and vacuum the ash periodically, which, accompanied by the associated downtime, is a �nancial burden. In previous designs, an array of large turning vanes has been used to control the velocity distribution into the face of the �rst catalyst layer. By modifying the angle, position, and quantity of turning vanes, engineers and designers were able to improve the velocity distribution entering the catalyst. Also, in order to control the direction of the �ow to the catalyst layer, a straightening grid was typically installed immediately above the �rst catalyst layer. However, high �delity CFD results highlighted for the �rst time the formation of �ue gas recirculation and low velocity zones behind the SCR turning vanes, a potential root cause of �y ash drop-out onto the catalyst layers below. Another drawback of using the traditional turning vane design was its sensitivity to upstream �ow conditions and, as such, its requirement for exact spacing and angling during SCR construction to ensure that prototype results matched model results.
FIGURE 3: Figure from GSG patent US 8141588 B2 (Bible, Tan & Triece)
Owner Location Size Start-up Year
Saint John’s River Power Jacksonville, Florida 2 x 650 MW 2009
AECI Thomas Hill, Missouri 180 MW, 303 MW 2009
Wangqu China 2 x 600 MW 2009
Huaneng Hainan Dongfangang 1&2 Dongfang, China 2 x 350 MW 2009
Edenderry Power Edenderry, UK 120 MW 2009
Mataro Waste to Energy Mataro Italy 2 x 1000 t/hr steam 2010
China Light & Power Castlepeak, Hong Kong 4 x 685MW 2010
E.On. UK Ratcliffe, UK 4 x 500MW 2012
Nanbo Glass China 600 t/h steam, 900 t/h steam 2012
China Steel Kaohsiung, Taiwan 3 x 80 MW 2012
Kansas City Power & Light La Cygne, Kansas 740 MW 2012
Gainesville Renewable Energy Center Gainesville, Florida 100 MW 2013
Cabot Chemical Xing Tai, Hebei Province, China 1000 t/hr steam 2013
Orlando Utilities Staton, Florida 444 MW 2013
Dupont Johnsonville TN 2 x 200 t/hr steam 2014
TABLE 1: Successful GSG® installations from 2009 to 2014
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Any changes to the system upstream warranted a model revisit and potential modi�cations of the vanes to maintain the required distributions.
Fuel Tech, instead, performed CFD optimization which resulted in the combination of the traditional SCR hood turning vane array with a straightening grid into the single GSG device. The GSG consists of parallel plates installed in the SCR hoods on the diagonal to turn the �y ash and �ue gas vertically into the �rst catalyst layer, as shown in Figure 4. This device has less sensitivity to upstream �ow distribution, which means the catalyst and catalyst performance can be protected even if the unit is not running at optimum design conditions.
GSG APPLICATION AT LA CYGNE UNIT 1Kansas City Power & Light Company (KCP&L) is an electric utility serving customers in Kansas and Missouri. KCP&L’s La Cygne Unit 1 is an 815-MW Babcock & Wilcox cyclone boiler with over�re air and SCR. Unit 1 burns a blend of 90% Powder River Basin (PRB) and 10% local Missouri. Because of poor �uid dynamic design, ash was accumulating within the SCR reactor at a typical rate of 1,450,000 pounds per year, resulting in high ash removal costs, high catalyst replacement costs, high catalyst pressure drop and fan operational costs, and high NH3 reagent costs. In addition, La Cygne suffered high NH3 slip (unused reagent that “slips” through the catalyst unreacted) as the catalyst activity decreased. This increase in slip caused operational issues downstream of the reactor including pressure loss increases over the air preheater and more frequent APH washings. In 2012, the ash accumulation rate increased to twice the typical amount as the power plant operated for an extended time with a low demand factor. Although many minor changes were made to the system to improve the performance over the course of the previous years, none of them were successful as they did not address the root cause of the problems.
However, the GSG technology developed and designed by Fuel Tech successfully addressed all of these complexities, and produced a signi�cant improvement compared to traditional turning vane techniques. Fuel Tech was contracted by KCP&L in 2012 to perform a thorough CFD optimization and EFD validation study, and, later, to provide equipment and equipment performance guarantees based on the results of this study. The
resulting gas �ow pro�le in the inlet hood of the reactor is shown in Figure 5.
The unit was restarted in 2013 with a notable reduction in catalyst pluggage due to ash precipitation and the retro�t has been deemed a large success. According to the paper by KCP&L engineer Scott Heideman [1] the GSG has resulted in annual operational savings of $5 million. It is notable that the next catalyst layer replacement for this unit is not budgeted until 2019. This means that only one layer needs to be replaced over the next seven years, which is a major improvement
compared to eight layer replacements over the course of the previous �ve years.
FUEL TECH AND THE BIRTH OF I-NOXIntegrated NOx Reduction, or I-NOx, is an advanced NOx removal technology that successfully addresses all of the challenging aspects of an SCR unit and takes it to a higher level of performance while maintaining a low capital cost. This technology was �rst introduced by Fuel Tech to be a combination of their CFD-guided staged approach
FIGURE 5: Gas velocity contour plot at La Cygne unit 1 SCR inlet, post GSG
FIGURE 4: Typical SCR unit with a GSG located above the catalyst
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for NOx reduction including advanced combustion modi�cations consisting of Low-NOx-Burners (LNB) and Over Fire Air (OFA), and post combustion de-NOx approaches including Selective Non-Catalytic Reduction (SNCR), and Selective Catalytic Reduction (SCR). It also includes the optimal use of highly effective �ow distributors such as static mixers, custom designed Ammonia Injection Grids (AIG), and the employment of the GSG. CFD analyses enabled engineers to proactively decide on what approaches to take during each step of the design phase. There are many factors that contribute to achieving an optimal design. For example, knowing the continuum concentration distribution of NOx and NH3, the temperature pro�les, and the velocity patterns within the system are critical. If the pro�les are highly skewed, engineers could perform CFD analysis on the number of required mixers to achieve a homogenous distribution. If the pro�les are relatively well distributed, a tunable AIG may suf�ce without the need for a mixer which increases pressure loss. Furthermore, the GSG provides the optimally distributed uniform gas velocities into the catalyst where space is limited. With all of these upstream NOx removal technologies and �ow distribution devices including AIG,
FIGURE 6: GSG retro�t in-progress at KCP&L’s La Cygne station unit 1
static mixer, and Fuel Tech’s patented GSG technology, the SCR system can be fully optimized.
Each of these NOx reduction components is optimized by the extensive use of advanced Computational Fluid Dynamics which enables engineers to take a system-wide design approach resulting in considerable cost savings compared to going to ground with a traditional stand-alone SCR facility and trouble-shooting problem areas later as they arise. I-NOx has been estimated to achieve de-NOx rates of up to 85% at less than half the capital costs of stand-alone SCR. Installed costs are estimated to be in the range of $100-150 per kW for an I-NOX installation compared to $300 per kW for a full stand-alone SCR. In a coal-�red power plant in Hong Kong, many elements of I-NOX have been successfully implemented and helped the unit exceed all performance criteria. Because of the successful achievement at this facility, Fuel Tech continues to receive more orders for complete I-NOx retro�ts (combustion modi�cation, SCR, and SNCR) from other facilities throughout the world.
CONCLUSIONThe rapid development of computer
hardware and CFD technology has brought engineers a revolutionary tool in addressing all the complexities needed for APC at power plants and industrial facilities. The result is a high performance pollution control system capable of meeting strict environmental regulations while maintaining the low capital and operating costs.
ACRONYMSAIG = Ammonia Injection GridAPC = Air Pollution ControlESP = Electro-Static Precipitators FGC = Flue Gas Conditioning FGD = Fuel Gas DesulphurizationGSGT = Graduated Straightening GridI-NOX = Integrated NOx ReductionKCP&L = Kansas City Power & Light LNB = Low NOx BurnerOFA = Over-Fire AirSCR = Selective Catalytic Reduction SNCR = Selective Non-Catalytic Reduction
REFERENCE[1] Scott Heidemann, R. Thomas, D. Pfaff & D. Fischer: “Reducing SCR Fly Ash Accumulation with Improved Reactor Inlet Air�ow” POWER, October 2013
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LIFE SCIENCES INTERVIEW: SIMULATE, COLLABORATE & REGULATE
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INTERVIEW: SIMULATE, COLLABORATE & REGULATE LIFE SCIENCES
KRISTIAN DEBUS & PRASHANTH SHANKARACD-adapco
The group of industries known as Life Sciences – biomedical devices, diagnostics and pharmaceutical, is seeing unprecedented change in how new medicines and technologies are being developed. The industry leaders here are increasingly turning to numerical simulation and modeling to reduce costs, lessen risks, foster innovation, augment diagnostics and trials and create better products. Still, the adoption and proliferation of Computational Modeling and Simulation (CM&S) in Life Sciences is at a nascent stage. The major challenge here is that this is a highly regulated industry and as such, the vendors and users alike are looking up to the regulatory agencies for guidance on incorporating Computational Modeling and Simulation (CMS) into the total product life cycle.
The Medical Device Innovation Consortium (MDIC) is the first-ever public -private partnership (PPP) independent organization to advance medical device regulatory science and to bring CM&S into regulatory decision making. CD-adapco’s Director of Life Sciences, Kristian Debus talks with Dawn Bardot, Senior Program Manager, Modeling & Simulation at MDIC to understand their vision to place CM&S as regulatory grade evidence in the United States and
COMPUTATIONAL MODELING AND REGULATORY DECISION MAKING FOR MEDICAL DEVICES
Q&A WITH DAWN BARDOT, MEDICAL DEVICE INNOVATION CONSORTIUM
how simulation vendors can help with this vision.
KRISTIAN: Thanks for talking to us, Dawn. What exactly is the role of MDIC?
DAWN: MDIC, formed in late 2012, is a public private partnership between industry, government (including FDA, Centers of Medicare and Medicaid, National Institute of Health) and other interested parties, software manufacturers and medical device manufacturers. Our vision is to create an opportunity for all the stakeholders to come together and collaborate on regulatory science. If we are to meet the 21st century technology demands, we have to find new ways of demonstrating device safety and cost effectiveness. We have three projects at MDIC that identify new methods and tools for demonstrating medical devices. The Patient Centered Benefit-Risk Assessment (PCBR) project is looking at bringing the patient’s voice and needs into medical device assessment. The Clinical Trial Innovation & Reform (CTIR) project focuses on bringing US products faster to market as well as ways to do more simple trials based on electronic medical records and collected data while maintaining safety standards. The last is the Computational Modeling and
Simulation (CM&S) project to facilitate the use and acceptance of simulation tools in the regulatory review process.
KRISTIAN: Where can CM&S tools help the most in the regulatory review process?
DAWN: We now have the ability to rely on CM&S for business decisions and root cause analysis. But the use of CM&S is not prevalent in the areas where the medical device companies face major expenditures: clinical trials and reimbursement. Clinical trials account for up to 50% of the cost to bring the product to market but numerical simulation, which can keep these costs down, is not being used. The other biggest opportunity is in the reimbursement stage where these models can be used to answer questions for the reimbursement agencies.
KRISTIAN: So MDIC wants to foster the cooperation and communication necessary to achieve these goals for CM&S?
DAWN: Yes. There’s no other place where all of the stakeholders can collaborate and talk about how to overcome these barriers. There are other professional trade organizations
SIMULATE, COLLABORATE & REGULATE
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but MDIC is the only one that’s really focused on that collaboration as a 501(c)3 non-profit organization.
KRISTIAN: So how close are the other two groups working with the CM&S group? There seem to be mutual benefits here.
DAWN: It’s absolutely right that all three project groups of MDIC should have overlap. For example, the CM&S working group recently had a presentation at a workshop hosted by the CTIR project. The presentation showed that by using virtual patients from CM&S, the number of real patients necessary in a clinical trial can be reduced. In addition to demonstrating the device in a safe, effective and faster manner, CM&S can also augment trials where recruiting real patients is difficult. This is a great example of collaboration between these two projects. The PCBR project recently released their framework document. A major focus was on demonstration projects for evaluating a patient’s
preference or a population of patients’ preference or risk tolerance for medical devices. Here, the willingness of patients to use devices that have been demonstrated through virtual patients can also be used as evidence to support bringing the device to market.
KRISTIAN: What are some popular application areas of CM&S right now?
DAWN: A lot of the use has been in analy zing blood f lows and in respirator y analysis. Then there is the biophysics component – dev ices that are ei ther interact ing wi th the human or implanted in the human. Outside the body, there are ex ternal components of the medical dev ice process that are of interest; things l ike dialysis machines. In the quali f icat ion of dev ices, modeling the manufactur ing process and understanding the r isk and l iabi l i t y is becoming impor tant. In the total product development cycle, CM&S can be informat ive in mult ip le aspects.
KRISTIAN: Do you see applications in diagnostics and patient monitoring as well?
DAWN: Absolutely. Increasingly, there will be opportunities for CM&S to help inform citizen scientists, whether it is in the context of medical devices or health apps. For example, asthmatic systems are trying to understand triggers and the need for an inhaler. Environmental conditions are crucial here. Based on big data, these systems can predict triggers but they can also rely on atmospheric condition models to monitor environmental changes. The predictive understanding from CM&S and the empirical observations from big data can come together to work for health apps and medical devices. It will be interesting to see where the discussion goes around diagnostics using CM&S. Modeling can play a supporting role in treatment planning, interventional planning, device positioning, etc. CM&S can also help in the physician training realm.
FIGURE 1: Cardiovascular simulation with STAR-CCM+ showing streamlines and velocity through a stent: Medical device companies can develop safer, more effective stents through simulation which could ultimately lead to personalized stents.
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KRISTIAN: At CD-adapco, our current focus is on multidisciplinary, multi-physics simulations in STAR-CCM+®. How important is this in regulatory science?
DAWN: I think it’s really important for the code manufacturers to consider multi physics. Medical devices are natively multi physics oriented with the interaction between the biological and engineering component.
KRISTIAN: What are the biggest challenges for current tools to be accepted in regulatory decision making?
DAWN: Last year, MDIC asked our members about the biggest hurdles in using CM&S at the regulatory stage. Return on investment and the specialization needed to use CM&S tools were some popular answers but the majority found regulatory uncertainty as the biggest hurdle. We can get around this by having examples where CM&S is leveraged at regulatory stage and making this information public. Also, demonstration projects
to showcase the utility and credibility of CM&S in the regulatory phase are needed to overcome this uncertainty.
KRISTIAN: So it is a chicken and egg problem. The regulatory body wants to see more modeling and examples from the industry while the industry will start going in that direction only if the FDA requires CM&S.
DAWN: It is less about CM&S being required by the FDA and more about the manufacturers wanting to understand the expectations by the regulatory agencies. More examples and use from manufacturers will help the FDA explore frameworks and validity and offer feedback on CM&S tools. Currently, such dialog occurs one-on-one between a sponsor/manufacturer and the agency. What we need is a grand challenge that can bring together academics, industry and regulators; a challenge similar to the race to the moon or the genomic project that is given to the community. I think the community would rise to
that challenge. We need to identify what the ‘moonshot’ challenge for this community is.
KRISTIAN: What about the conflict of interest for commercial code manufacturers in collaborating?
DAWN: We have to recognize that in this complex space, none of us have all the tools and knowledge to go alone. We have to explore a cooperation model to fiercely compete but collaborate where the information is shareable.
KRISTIAN: Do you think the technology from the manufacturers is ready right now to tackle these challenges?
DAWN: Absolutely. CM&S has been used in regulatory submission for decades. In angioplasty, there was a device approved for market based solely on simulation almost two decades ago. The technology from code manufacturers is ready. The people
FIGURE 2: Volume rendering of vorticity inside an Abdominal Aortic Aneurysm (AAA) �tted with an endovascular stent graft: A collaboration between Medtronic, Materialise, Simulia and CD-adapco is investigating robust computational work�ows for stent graft design. Such collaboration between different vendors is crucial in establishing best practices.
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using these technologies are ready. MDIC wants the stakeholders to know that modeling is ready to use. Our goal is to find a grand challenge for the community so we can establish verification and validation benchmarks and foster cooperation.
KRISTIAN: What’s your advice for end users and vendors?
DAWN: Start using CM&S throughout the entire product development cycle more frequently. You can enrich and validate the models at every step and document this to start developing the processes. Credibility of the models and documentation of the processes is important. We have seen surprising openness from the code manufacturers to collaborate and work together. Most of the end users are single user or smaller teams and the code vendors can help them through established processes, automated scripts, cloud-sourced HPC nodes, code verification suites and so on. Vendors like CD-adapco can support both end users and regulatory organizations in this journey.
Dawn Bardot, PhD, is the Senior Program Manager, Modeling & Simulation at the Medical Device Innovation Consortium (MDIC). Dawn has more than 15 years of experience in computational model validation and uncertainty quanti�cation. She is passionate about the application of modeling and simulation to improve health care and lower the cost of bringing products to the market. Over the course of her career, Bardot has worked with startup companies, government organizations and academia on computer simulations and validation. She has tackled the challenge of big data and data curation; disseminated and promoted the �rst computational model V&V standard; and recruited, hired and commandeered associated teams. Dawn also holds a number of patents and pending applications, has been published in countless industry and educational journals, and is called upon regularly to address conferences and seminars.
CD-adapco is an active member of MDIC and routinely participates in the efforts to increase the use of CM&S evidence in regulatory decision making. Our CM&S tools are being used by industry and academia in various medical applications like hemodynamics, respiratory analysis, pharmaceutical manufacturing like mixing and tablet coating, micro�uidics, diagnostics and many more. CD-adapco is proud to bring its expertise and resources to the table to advance the use of simulation in the medical device industry through MDIC.
FIGURE 3: Thermal and �ow analysis inside an infant incubator using STAR-CCM+: Devices like incubators can get regulatory approval using simulation as evidence.
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FLOW − THERMAL − STRESS − EMAG − ELECTROCHEMISTRY − CASTING − OPTIMIZATION REACTING CHEMISTRY − VIBRO-ACOUSTICS − MULTIDISCIPLINARY CO-SIMULATION
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MANUFACTURING OPTIMIZING COMPONENT DESIGN
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JOHN KIRKLEY interviews BYRON BEMIS of Owens Corning
It wasn’t your everyday assignment. Byron Bemis, Senior Research Associate at Owens Corning, and his team were asked to design a new generation for manufacturing components that could not be made using existing manufacturing processes. Owens Corning is a leading global producer of residential and commercial building materials, including insulation and roo�ng shingles; glass-�ber reinforcements for products such as cars, boats, wind blades and smart phones; and engineered materials for composite systems. Its Science and Technology Center, where Bemis is located and much of the company’s research and development takes place, is located in Granville, Ohio.
With this particular R&D project, Bemis and crew were breaking new ground. To design and fabricate the requested parts, their initial designs called for blind keyhole welding through one sheet metal part and into another. “Developing the welding parameters to make those welds work reliably and robustly took a lot of trial and error,” Bemis says. “To accomplish this using physical prototypes meant fabricating the individual component parts and then laser welding them up using a set of pre-determined parameters to see what happens. You continue doing
SIMULATION OF LASER WELDING SHORTENS DESIGN CYCLE AND OPTIMIZES COMPONENT DESIGN AT OWENS CORNING
this until you �nd the right combination.” Bemis adds that one of the most challenging aspects of this project was the necessity to weld close to small features or near corners or edges. If the laser is running too hot or moving too slowly, the feature or edge could melt, ruining the part.
These were small welds, varying in size from millimeter to submillimeter scale, made on very small parts that demanded high precision fabrication. Bemis says they were running very narrow weld beams — in many cases 50-micron weld spots using up to a kilowatt of laser power on an individual spot.
The materials used were alloys with very high melting points, high molten metal viscosity, and surface tension. This made for some interesting, non-standard welding physics.
Complex geometries were also involved, including small features, edges and circular sections. Because deep penetration was necessary to make the part, a keyhole mode was required. Keyhole mode is a welding technique in which a concentrated heat source, such as a laser, penetrates completely through a work-piece, forming a hole at the leading
edge of the molten weld metal. As the heat source progresses, the molten metal �lls in behind the hold to form the weldbead.
All of these considerations - in particular, the blind keyhole welding - meant a lot of trial and error. Running hundreds of repeated physical experiments using expensive alloys and high-value component parts was prohibitively expensive and time consuming. It was obvious that simulation was the answer. But high �delity simulation of the complete keyhole physics was very complex, expensive and slow. The simulation had to adequately predict quantities of interest - such as weld pool diameter and zone shape as well as penetration depth - in order to specify the optimal laser process parameters.
SEEKING A SOLUTIONContemplating the task at hand, Bemis recalls, “We needed an economical solution - one that was fast, robust and easy to use.” In search of this solution, he spoke with his support engineer at CD-adapco, who had experience simulating welding. Based on the guidance from CD-adapco he decided to use STAR-CCM+® to conduct the simulations.
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STAR-CCM+ features a comprehensive suite of geometry creation and preparation tools that signi�cantly reduce the number of man-hours required to prepare a model for meshing. In addition, STAR-CCM+ features a single integrated environment, which provides a fast, most automatic route from complex CAD models to engineering solution. This met two of Bemis’ main criteria - speed and robustness. As to ease of use, the software’s powerful meshing tools cut down geometry preparation and meshing time from weeks and months to hours, while delivering a high-quality mesh on sophisticated geometries. All of these capabilities can be leveraged from within familiar CAD and PLM environments.
Bemis used STAR-CCM+ to simulate the welding heat transfer process. The solution proved to be excellent in predicting both the weld width and the behavior of features affected by the blind welding. “STAR-CCM+ has the unique ability to simulate the welding process and provide insight into the thermal transient experienced during welding in a manner that is both practical and fast enough for industrial use,” Bemis says.
POWER OF OVERSET MESHINGFor the past 30 years, engineers trying to perform Computational Fluid Dynamics (CFD) simulations struggled with the interaction between multiple moving objects. Traditionally, this required the generation of an interconnected mesh between the objects, an intensive manual process that was extremely dif�cult and time consuming. In fact, it was almost impossible if extreme ranges of motion or close interaction between objects was involved.
With Overset Mesh, STAR-CCM+ solved the problem. Overset meshing
— sometimes called “overlapping” or “chimera” mesh - in STAR-CCM+ presents a new and more effective way to handle the modeling and simulation of the complex physics associated with moving objects. This approach allows the user to generate an individual mesh around each moving object, which can then be moved at will over a background mesh.
“In the welding process, you can either move the heat source or the material,” Bemis says. “Overset meshing allows you to simulate the relative motion between the heat source and the parts that are being welded together. That motion, along with the laser’s power, really dictates how wide the weld gets, the size of the molten zone, and the depth of penetration.” Bemis continues, “With the use of overset meshes, we were able to run a fairly coarse background mesh as well as a
�ne, detailed mesh of the weld zone. We moved rather arbitrarily through the background mesh and generated any weld pattern we wanted. Some of the welds were 100 mm long and ½ mm thick, resulting in big aspect ratios and a really large mesh count to re�ne the simulation in the weld zone areas.”
Bemis was working with a moving target. Heat tends to build up in the weld zones resulting in changing parameters as you move from weld to weld. Overset meshing allows the designer to simulate individual welds on the component, taking into consideration the changing nature of the material being worked on due to heat transfer. An implicit unsteady simulation with a moving overset mesh permits the prediction of the extent of the molten zone as it progresses along the joint, temperatures in the work piece, and heat
Overset
Keyhole
FIGURE 1: One of the unique features of STAR-CCM+ that Bemis found extremely useful is Overset Mesh, a major advance in simulation.
FIGURE 2: Typical Welding Cycle
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Melting through to the tube
Case 1• Initial setup• Constant Power
Case 2• Optimized setup• Ramped power
FIGURE 3: One of the MDX concepts that Bemis employed to design the component was an add-on module known as STAR-CCM+ / Optimate™.
FIGURE 4: Optimized start dwell and ramp produces a full depth weld at the start. Decreasing power ramp through the circular weld maintains weld size and depth as heat builds up.
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Case 2• Optimized setup• Ramped power
Case 1• Initial setup• Constant Power
MANUFACTURING OPTIMIZING COMPONENT DESIGN
transfer to the �xture. Parameters such as laser power, travel speed, acceleration, and pulse frequency can be tuned to provide the desired optimal weld.
A NEW METHODOLOGYThe research team also worked with a methodology recently introduced by CD-adapco known as Multidisciplinary Design eXploration (MDX). MDX allows the automatic testing of designs from early in the concept stage against all of the physics that might impact performance. This is possible because the increasing capabilities of sophisticated simulation software such as STAR-CCM+ allow engineers to determine how a product will perform under the actual conditions it will face during its life cycle.
“We used Optimate to explore the parameter space up front and alter the process and components to get the �nal results we wanted,” Bemis explains. “We were able to set up weld speed, power, and �eld functions to mimic laser control. We could ramp the laser up and down, simulate voltage feeds, and all the other parameters that Optimate could access. We then used the software to run cases to determine such things as how far back from a corner we needed to slow down, and how much to drop laser power in order to make a weld around a sharp corner while maintaining the same heat effective zone in the base material components. The simulation allowed us to prescribe all the welding parameters for experimental validation early in the design process.”
“ACCURATE ENOUGH”He points out that using the CD-adapco simulation solutions meant that the results were “accurate enough.” Rather than attempting to generate a perfect simulation of the problem, the results they obtained provided suf�cient information to accurately predict real world weld characteristics, evaluate parameters and decide which directions to take.
This process, Bemis says, was very fast considering it was a fully transient simulation with motion and overset meshing. He was able to run enough cases on a high-end workstation loaded with STAR-CCM+ / Optimate to allow design space experimentation and optimization. The software is �exible enough to simulate complex motion in time-dependent parameters for heat input �eld function interface. “It’s incredible to have that kind of power at your �ngertips without having to write your own C code
or FORTRAN,” Bemis resumes. “I �gure we saved at least six months of trial and error development — six months of experimental lab time - which is huge. In fact, by freeing up more time for design, we managed to �gure out how to avoid using blind welds, a de�nite plus. “The high quality simulation using STAR-CCM+ and Optimate allowed us to explore the research, design and analysis of the component as well as the manufacturing process all at the same time. We were able to deliver the �nal
component design to our manufacturing facility complete with all the fab steps and processes in place. This is a very powerful way to work.”
“We use STAR-CCM+ every day,” Bemis concludes. “The CD-adapco software has become an integral part of our design and development activities.”
REFERENCEPraxair Direct: Welding Terms Glossary
Additional cut plane to show the weld interior shape
0.0453 in
FIGURE 5: Case 1 shows how weld pool melts through the corner at constant power and speed. Case 2 pictures an optimized setting allowing for a uniform melt pool throughout the weld cycle.
FIGURE 6: Validation of weld zone width and depth was done using optical microcopy
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TRAINING
• JAVA™ Scripting - Process Automation• STAR-CCM+® Wizard Creation• Computational Fluid Dynamics (CFD) for the Chemical Industry • Vehicle Thermal Management• Effective Heat Transfer• Introduction to Particle Modeling using the Discrete Element Method• Lagrangian Multiphase Flow Modeling• Advanced Engineering Optimization (Coming Soon)• Internal Combustion Engine Analysis• Turbomachinery Engineering• Applied Computational Combustion• External Vehicle Aerodynamics (Incompressible) • Aeroacoustics
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CHOOSE FROM ONE OF THE FOLLOWING COURSES:
TRAINING COURSESTraining adds incredible value to the software you have purchased and comes highly recommended by all. Courses are regularly held at CD-adapco™ of�ces around the world including Detroit, Houston, Seattle, London, Nuremberg, Paris, Turin and others. The courses listed on our website can be scheduled to suit your requirements. To take advantage of this, please request information from your account manager.
Courses are held in small groups and the number of available places can be checked online at:www3.cd-adapco.com/training/calendarJust click on the course you are interested in to get an overview on the dates, locations, and availability. If the course is not scheduled in an of�ce near you, then why not take it via distance learning, CD-adapco’s internet-based remote learning service. To �nd out more or to get a course scheduled to suit your requirements, please contact your account manager.
Specialized Courses:New specialized courses relating to application-speci�c areas are developed throughout the year. Please contact your account manager for more information. Note:In most situations, it will be possible to register trainees on the course of their choice. However, if requests for places are received too close to the course date, this may not be possible. Availability of places can be obtained online or by contacting your local of�ce.
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View your local course offerings, customer testimonials and register for an upcoming course at www3.cd-adapco.com/training.To register for a course complete the online registration or request a faxable form from your local support of�ce. Alternatively, if you are located in Americas or Europe register through the STAR Academy! Log into the Steve Portal and select 'Training' to view the agenda and register for a training.
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