S & I Engineering Solutions Pvt. Ltd.A product of research

Number 301, Block A, Raghuram Residency30, MSR Road, Gokula, Bangalore 560012

Karnataka, India

Website: http://www.sandi.co.inPhone: +91802345 4359Fax: +91802337 6035Mobile: +9194490 54359

About the company

Indian Institute of Science (IISc), a premier research institute of India, supports commer-cialization of quality research initiatives on the campus, through an MCIT funded program,under the SID banner. The Simulation and Innovation (SandI) is an outcome of such aninitiative and is housed in the incubation centre of IISc.

The code High Resolution Flow Solver on Unstructured Meshes (HiFUN), the primary prod-uct of SandI, features some of the most recent CFD technologies. HiFUN is fine tuned tosolve typical Aerospace applications and certain flow problems encountered in Automotiveindustries. The code has been extensively used for solving a number of problems, over a widerange of Mach numbers, ranging from Airship aerodynamics to aerodynamics of Hypersonicvehicles. SandI presents with pride, this robust, fast and accurate flow solver to the CFDcommunity.

Why HiFUN ?

HiFUN imbibes most recent CFD technologies; many of them home grown. HiFUN is robust, accurate and fast. HiFUN exhibits highly scalable parallel performance with its ability to scale upto severalthousand processors on massively parallel computing platforms.

HiFUN is available at an extremely competitive price. HiFUN can be customized to meet specific user requirement.

Services offered

Consultancy servicesState of the art high end CFD services using HiFUN, both to the Aerospace and Automotive industries.

Customized code developmentTo meet specific user requirements.

Sales and maintenance of the SandI productsSale of HiFUN at a competitive price and postsale maintenance/upgrades.

CFD EducationFor an uninitiated user of CFD tool.

CFD ResearchFor continuous value addition to HiFUN.

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Present features of HiFUN

Unstructured Face based data allowing for arbitrary polyhedral volumesThe solver allows the use of four basic mesh elements, namely, hex, tet, prism and pyramid, in combination.The adaptive capability of the solver, allowing for hanging nodes, results in arbitrary polyhedral volumes.

Higher order spatial accuracyHigher order spatial accuracy is achieved by using a linear reconstruction procedure based on either methodof least squares or GreenGauss procedure. Use of Venkatakrishnan limiter ensures monotonicity of thesolution for high speed flows.

Numerical Flux FormulaThe solver supports a number of numerical flux formulas, in addition to an option for the user to include hisfavorite flux formula.

Viscous DiscretizationA positive viscous discretization ensures robustness of the flow solver even on grids with highly skewed cells.This is indeed an unique feature of this solver.

Non conformal block interface algorithmThe flow solver is equipped with an efficient algorithm to handle non conformal grids at an interface betweentwo blocks.

Moving wall boundary conditionIt is possible to simulate translating and rotating walls in the flow solver.

Multiple rotating frames of reference algorithmThe flow solver is equipped with an efficient algorithm to handle multiple rotating frames of reference in agiven problem.

Algorithm to simulate porous mediaThe flow solver is capable of simulating flows through porous media.

Turbulence ModelsThe solver supports SpalartAllmaras and kOmega turbulence models. The model equations are solved in adecoupled manner. A robust discretization used for the model equations ensure high levels of convergenceeven for the turbulence quantities.

Wall FunctionFor turbulent computations, standard equilibrium wall function gets automatically activated if grid resolutionnear the wall is not adequate to resolve viscous sublayer.

Convergence AccelerationA matrixfree implicit procedure ensures rapid convergence, both for steady and unsteady computations (indual time mode).

ParallelA unique four layered approach to data handling on each of the processors ensures there is no degenerationin the performance of the parallel code as compared to a serial code, while at the same time achieving a linearspeed up even for several thousand processors. The MPI is used for message passing across the processors.

Features of HiFUN in advanced level of implementation

Higher order time accuracyFormal second order time accuracy is achieved both on stationary and moving grids using a dual time steppingprocedure. On the moving grids the solver is GCL compliant.

Grid AdaptationA hybrid adaptive strategy employing sensors based on both residual error estimator (referred to as R

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Parameter) and error indicator is made use of. The cell division is isotropic. Migratory Memory Algorithm

This unique memory saving device substantially enhances the problem size a given machine can handle usingHiFUN.

HiFUN validation: 1st AIAA High Lift Prediction Workshop

First AIAA High Lift Prediction Workshop (HiLiftPW1) was conducted on 2627 June 2010 inChicago, USA. The focus of this workshop was to evaluate accuracy of RANS codes to predicthigh lift flows. The configuration considered for the study was NASA TRAP WING (givenin figure 1). There were 18 participants in the workshop across the globe which includeIIScSandI, ANSYS, Boeing, DLR, NASA, JAXA and ONERA. Based on the comparison ofsimulation results with experiments, HiFUN was adjudicated as one of the very good codesby the workshop technical committee. HiLiftPW1s summary presentation can be downloadedfrom the link http://hiliftpw.larc.nasa.gov/Workshop1/ParticipantTalks/rumsey-summary.pdf.The salient features of the computations performed for HiLiftPW1 are as follows:

RANS computations with SpalartAllmaras turbulence model. Three hybrid unstructured grids with 7.5, 21 and 63 million volumes were employed. Computations were performed on IBM Blue Gene and 1024 processors were employed forfine grid.

Accurate CLmax and max predictions which is considered to be a challenge in CFD litera-ture.

HiFUN exhibited high level of convergence for mean flow as well as turbulent equations.

Figure 1: NASA TRAP WING: Configuration (left) and pressure fill plot (right)

HiFUN validation: 4th AIAA Drag Prediction Workshop

Fourth AIAA Drag Prediction Workshop (DPW4) was conducted on June 2021, 2009 in SanAntonio, TX, USA. The focus of this workshop was to evaluate accuracy of RANS codes to

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predict drag for transonic transport aircraft. The configuration considered for the study wascommon research model (given in figure 2). There were 28 participants in the workshopacross the globe which include IIScSandI, ANSYS, Boeing, DLR, NASA, JAXA and ONERA.Figure 3 depicts the grid convergence of total drag and moment coefficients obtained usingdifferent codes participated in workshop. From figure 3 it can be seen that amongst variouscodes, the scatter of total drag on finest grid is about 30 counts (1 count = 0.0001). Inthis figure, the blue lines depict the results obtained using HiFUN. The performance of theindividual codes would be known once the experimental results are disclosed shortly. In theabsence of such an information, it is interesting to note that HiFUN data represents the meanof the CFD data for both the aerodynamic coefficients of interest. The salient features of thecomputations performed for DPW4 are as follows:

RANS computations with SpalartAllmaras turbulence model. Three hybrid unstructured grids with 6.3, 21 and 57 million volumes were employed. Computations were performed on IBM Blue Gene and 1024 processors were employed forfine grid.

HiFUN exhibited high level of convergence for mean flow as well as turbulent equations.

Figure 2: Common Research Model: Configuration (left) and pressure fill plot (right)

Figure 3: Grid convergence: Drag coefficient (left) and Moment coefficient (right) with blueline indicating HiFUN results

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HiFUN on EKA

Computational Research Laboratories (CRL) high performance computing platform EKA wasadjudicated as fourth fastest computer at the supercomputing conference in September 2007.EKA has 14400 compute nodes connected by high speed interconnects. With its unique par-allel framework, HiFUN demonstrates high level of parallel scalability on EKA for a range ofgrid sizes. Figure 4 depicts the plot of time per iteration per million volumes obtained onEKA for various grids against number of processors. From this figure, it can be seen that fora given number of processors, the time per iteration per million volumes remains invariantindependent of the grid size. This plot clearly brings out ability of HiFUN to exploit consis-tent parallel performance of EKA. The performance of HiFUN on EKA has enunciated that forgrand challenge problems it is possible to obtain reliable design data in time short enough toimpact design cycle. Table 1 gives the typical grid size on a given set of processors of EKA forwhich a drag polar with about 1215 data points can be obtained within one day.

Figure 4: Performance of HiFUN on EKA: Number of processors v/s time per iteration permillion volumes for various grid sizes

Number of processors 32 64 128 256 512 1024 2048 4096 8192Grid size in millions 0.68 1.25 2.5 5.0 10.0 20.0 40.0 80.0 160.0

Table 1: Grid size on a set of processors for which drag polar can be obtained in a day

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Space shuttle launch vehicle computations using HiFUN

Computations are performed for space shuttle launch vehicle (SSLV) configuration using Hi-FUN at a supersonic Mach number equal to 1.05 and angle of attack of 3.0o. Figure 5depicts the configuration and the surface grid while figure 6 depicts the Mach fill plot andthe streamlines around the configuration. These computations clearly bring out the ability ofHiFUN to carry out simulations for complex geometries involving intricate flow physics.

Figure 5: Space shuttle launch vehicle: Configuration (left) and grid (right)

Figure 6: Space shuttle launch vehicle: Mach fill plot (left) and Streamline plot (right)

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Formula one racing car computations using HiFUN

HiFUN has a number of capabilities to perform computations for high speed car configuration;some of which are listed below:

Translating and rotating wall boundary condition to simulate sliding and rotating compo-nents.

Non conformal block interface algorithm to handle complex grid. Algorithm to simulate porous media flows through radiators/condensers. Multiple rotation frames algorithm to simulate rotating components.

Using aforementioned features, simulations have been made for real life formula one (F1)configurations; the details of which can not be disclosed for reasons of confidentiality. Hencethe results are presented for a generic formula one racing car available in open literature atfree stream velocity of 50 m/s. Figure 7 depicts the surface grid and static pressure fill plotwhile figure 8 depicts the the streamlines around the configuration.

Figure 7: Formula one car: Surface grid (left) and pressure fill (right)

Figure 8: Formula one car: Streamlines front (left) and rear (right) views

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