Entering the in silico era · 2015. 11. 13. · 1 © 2015 ANSYS, Inc. November 12, 2015 ANSYS Confidential Entering the in silico era Simulation Driven Product Development in the

1 © 2015 ANSYS, Inc. November 12, 2015 ANSYS Confidential

Entering the ‘in silico’ era Simulation Driven Product Development

in the Cardiovascular Sector


The Clinical World is Changing

Engineering simulation makes it possible to Ensure a successful FDA approval

through comprehensive upfront ‘in silico’ testing

Test disruptive innovations in a humanlike environment

Equip designers and surgeons with additional knowledge

“I don’t understand why simulation is used so much in automotive and aeronautic applications and so little in the medical world, where we directly impact a patient’s life. As surgeons, we are spending years to acquire enough know-how and experience to learn how to react quickly when the patient is lying on the operating table; but simulation is giving us the luxury to examine the situation when we still have plenty of time to think through

more quietly. I trust that simulation will be used increasingly in the clinical world in the near future.” Dr. Antoine Lucas Cardiovascular Surgeon

University Hospital of Rennes

ANSYS in the operating room

Source: ANSYS Healthcare blog


The Medical World has Experienced Several Revolutions that Accelerated the Pace of Innovations

Ind

uce

d In

no

vati

on

Rat

e

Time

In Vivo Surgery

In Vitro Antibiotics

In Silico Preventive personalized medecine

5 © 2015 ANSYS, Inc. November 12, 2015 ANSYS Confidential © 2015 ANSYS, Inc.

ANSYS Confidential

5

Rising Health Care Cost

Challenges • Reduce cost of

equipment • Avoid replacement

treatment • Minimize hospital

stays • Minimize nursing

cost

Access to Health Care

Challenges • Reduce cost of

medicine • Remote Persona-

lized medicine • Facilitate sterile

storage • Participatory

medicine

Safety & Increased

Regulation

Challenges • Failure could mean

injuries, death hence bad press

• Longer approval process

• Multiplication of clinical testing

• Difficult access to target population

Aging Population

Challenges • New emerging

pathologies • Replacement of

implanted devices • Chronic diseases • Lack of nursing

resources

Human Variability

Challenges • Multiplication of

clinical testing • No 100% certainty

of product integrity • Patient specific

geometry / material properties

• Rare disease

Industry Challenges Provide Opportunities

6 © 2015 ANSYS, Inc. November 12, 2015 ANSYS Confidential © 2015 ANSYS, Inc.

ANSYS Confidential

6

Healthcare Companies Are Focusing On These Initiatives

Affordable & Profitable Medicine

to make health care affordable to a larger audience despite the

“medicare” impact

Effective Medical Innovation

to ensure sustainable competitive advantage through disruptive

innovations

Ensuring Extreme Product Integrity

- to fulfill patient expectations

- to prevent product recall and treatment replacement

Accelerating Globalization

- to assist the entire population

- to collaborate with relevant resources

Digitalization of Medicine (and PDP)

- to increase product throughput

- for quicker treatment to patients

P4 Medicine:

Personalized, Participatory, Preventive & Predictive

to create new opportunities and open new markets


What Motivates Healthcare Companies to Look for Alternative Solutions?

1. Strong pressure for effective innovation

2. Long & costly clinical testing

3. Compliance to regulatory authorities approvals

© 2015 ANSYS, Inc.

ANSYS Confidential

7


What Motivates Healthcare Companies to Look for Alternative Solutions?

1. Strong pressure for effective innovation

2. Long & costly clinical testing

3. Compliance to regulatory authorities approvals

© 2015 ANSYS, Inc.

ANSYS Confidential

8

Virtual Human Laboratory

In Silico Testing

Simulation Driven FDA

Approval Process


Global Engineering Simulation Leader

Including 97 of the top 100 Industrial Companies on the FORTUNE Global 500

40,000 Total Customers

315,000 Commercial Seats 290,000 University Seats 200 Channel Partners 160 Industry Partners

Only software company focused solely on simulation with 44 years of simulation software experience

•Approx. 2,500 employees / 60+ sales offices on 3 continents

• Network of sales channel partners in 40+ countries

• 22 major development centers on 3 continents

2014 Target % of revenue spending on R&D: 15%

2014 Target Revenues: ~$ 1 Bn

Market Cap: ~$8.5B

http://www.gm.com/


ANSYS Created a Platform For Complete Virtual Prototyping of Medical Device and Human Body System

Tech

nical D

ep

th

Steady-State, Transient, Parameterized, Harmonic & Modal

Linear & Nonlinear

Technical Breadth

Meshing 2-way CAD interface

Simulation Project Management Design Xploration

Engineering Knowledge Management

HPC More…

Simulation Process and Data Management

Conduction

Convection

Radiation

Phase Change

Mass Transport

More…

Thermal

Ophthalmology: Laser Surgery

Structural

Large Displacements

Finite Strain

Contact

Multibody Dynamics

Random Vibration

Implicit & Explicit

More…

Orthopedic: Spinal Disorders

Compressible

Incompressible

Laminar Flow

Turbulence

Multiphase Flow

Non-Newtonian Fluids

More…

Fluids

Respiratory: Drug Delivery

Quasi static (Low Freq)

Full Wave

Joule Heating

Eddy currents Current flow Circuit Coupling

More…

Electromagnetics

Diagnosis Equipment: MRI Compatibility

Model-Based System & Architecture Design

Human Body Model

Reduced Order Model

Cardiovascular, Skeleton, PKPD

More…

System

Cardiovascular: Full System Modeling


Leading Medical Device Companies Rely on Simulation

Global ANSYS Medical Device Customer Base

• 88% of top 50 largest medical device

companies including:

- 16 largest medical device companies WW

- 40 largest US medical device companies

- 96% of the Top 25

• 88% of top 20 largest cardiology companies

including:

- 10 largest cardiology companies

• Typical Best in Class CAE investment:

- 0.2% of their total R&D investment in

engineering simulation

- Target 5% of their engineering force to use

simulation


The cardiology market is expected to grow but some key players are likely to loose market share.


Contents

• The On-Going Medical Revolution

• Case Studies:

- Patient Specific Modeling of LVAD and PVAD

• Biomedical Applications

• Cardiovascular Applications

• Industry Best Practices

• Wrap up: A Successful Revolution


Contents

• The On-Going Medical Revolution

• Case Studies:







Investigating the Position and Shape of the Outflow Cannula to Minimize Cerebral Embolization

By proper adjustment in this CFD study of a synthetic

model of an aortic arch bed, we found nearly a 50%

reduction of cerebral embolism could be achieved for a

configuration consisting of a shallow angle of implantation

over a baseline normal incidence of the LVAD cannula.

Department of Mechanical, Materials and Aerospace Engineering,

College of Engineering and Computer

Science, University of Central Florida, Orlando, FL, USA


Tracking Where the Particles of Different Sizes (2 & 5 mm)are Flowing Minimizes the Risk of Cerebral Embolization

In the standard configuration

with an angle of β=0 (right

column), a major feature of the

flow is a large recirculation zone

and a stagnation flow region as

the jet emanates from the LVAD

cannula. Thrombi of all

diameters are trapped in that

zone and are susceptible to

ingestion at the IA take-off.


The Shape of the Inflow Cannula is Playing a Major Role to Prevent Blood Damage

Blood damage index for various inflow cannula shapes

Local vortices facilitates the formation of clots

Courtesy of Medical College, Beijing, China


Miniaturization of Pediatric Implants without Compromizing with Performance is Very Valuable.

• Optimizing the structure of a PVAD impeller by minimizing volume, thereby minimizing mass. A total volume reduction of 46% was achieved.

• Combining computational analysis and optimization methods significantly reduced the cost of redesigning the impeller

• Despite the PVAD redesign, the critical speed of the PVAD was successfully increased, ultimately providing a more effective tool for assisting weak hearts in small children.

Paul Witherell, Sundar Krishnamurty, Ian Grosse, James Antaki

Department of Mechanical & Industrial Engineering

University of Massachusetts


Ventricular Assist Device are Complex Product Involving Different Physics

Numerical analysis and optimization of fluid dynamics,

electromagnetics, heat transfer, and structural stresses

• Generating a clot free blood

flow with the right speed and

volume

• Minimizing the stress to limit

the risk of fatigue deterioration

• Managing the heat exchange

to avoid patient incomfort

• Inducing the proper

electromagetic field to ensure

reliable power

• Avoid noise induced vibration

Courtesy of LaunchPoint Technologies, Inc.


Contents

• Adoption of simulation

• Case Studies:







Cardiovascular

Challenges • Patient-specific geometry

• Plaque growth and rupture

• Blood recirculation and low Wall Shear Stress (WSS)

• Deforming artery wall

• Fatigue induced by large number of cycles

Solutions brought by simulation • Local evaluation of WSS, pressure and flux at

aneurysm neck

• Deterioration of blood cell in blow pump

• Parametric studies for extreme conditions

• Automatic Design exploration of geometry, material and operating conditions

• Virtual prototyping e.g. for hearth valves

Courtesy of UPF, Barcelona & LTSI, Rennes

Peak-systolic aneurismal flow pattern and wall shear stress(WSS) of the unstented aneurysm model.

after the stent 2 deployment.

Stent deployment close to an aneurysm (stress in the stent)



• Continuously varying material properties (porosity, density, Young’s modulus)

• Bone fragility (osteoporosis)

• Minimizing patient recovery times

• Transient loads

Benefits of Modeling • Testing different prostheses / implants

designs

• Investigation of different scenarios and impact on the bone / implant interface

• Virtual prototyping on a large number of virtual patients

• Various pathologies considered during design

Orthopedics

Pictures, courtesy of CADFEM, ANYBODY


Respiratory


• Continuously varying materials properties (porosity, density, Young’s Modulus)

• Size of the drugs particles (~ few mm)

• Minimizing patient recovery times

• Transient loads

Benefits of CAE • Particles tracking in the inhaler and the

upper air way

• Including patient-specific physiology and health condition

• Virtual process adjustment from new born to elderly

• Surgical planning and training


Contents


• Case Studies:







EndoSize Ansys

- Navigation along centerlines of the vascular structure - Extraction of lumen contours from planes orthogonal to

the centerline - Mapping of density values and distance between aorta and

spine onto a mesh of the vascular structure - Export to Ansys DesignModeler

- Import of centerlines, planes and contours

- Reconstruction of the vascular wall by surface interpolation

- Conversion of « image data » to « mechanical data » (material properties, boundary conditions)

Geometry reconstruction from CT data


Segment Patient Data

Artery wall properties (healthy, calcified, thrombotic) Aorta-spine distance


Material models artery

UPPER Boundary Conditions tools

INSERTION boundary conditions tools

Boundary conditions Spine-Aorta support artery

Boundary conditions internal iliac arteries artery

Arterial pressure artery


Material behavior laws

Hyperelastic anisotropic model

𝑊 = 𝐶0(𝐼1 −3)+𝑘12𝑘2

(exp 𝑘2 𝐼4 − 1) 2

− 1 +𝑘32𝑘4

(exp(𝑘4 𝐼6 − 1) 2

− 1)

Neo-Hookean model

𝑊 =𝜇

2(𝐼1 −3)+)

1

𝑑(𝐽 − 1)2

𝐶0 (MPa) 𝑘1,3(MPa) 𝑘2,4 α

AA 0,006 0,015 18 42,3°

AAA 0,001 0,045 28 37,6°

𝜇(Mpa) 𝑑(MPa-1)

calcifications 17,86 4.10-8

Arterial tissue

Calcifications

Thrombus

Need to be taken into account for the good determination of the zero-pressure geometrie and

to apply internal pressure (1st simple approach)

Wall thickness (or stiffness) increase


Artery boundary conditions Aorta-Rachis support Internal iliac arteries support

𝑘 = 𝒌𝒎𝒂𝒙(1 −𝑙

𝒍𝒎𝒂𝒙)4

Distribution of stiffness decreasing with rachis-aorta distance

Two more linear springs

0 ≤ 𝑘 ≤ 5.10 −4𝑁/𝑚𝑚

𝑘~0,1 − 1𝑁/𝑚𝑚

Internal iliac arteries


1. Initial state: - Deformed artery and guidewire in equilibrium state - Delivery system : undeformed, outside the artery, insertion zone fixed

2. Delivery system initialization : - Displacement of the delivery

system into vascular lumen - Guidewire /artery contact

deactivation - Delivery system/artery contact

activation

3. Equilibrium: Relaxation of delivery system constraints until equilibrium state

Delivery system simulation process


Delivery system simulation results

FRONT 30° LAO 30° RAO


@neurist Hemodynamic Toolchain

3D solver

ANSYS-CFX

Patient medical image

(3DRA) Vessel surface extraction Reduction to region of interest,

and skeleton generation

Computational mesh

Hemodynamic results Flow rates from 1D model or MRI


Influence of the Stent on HemoDynamics

The stent is an important flow driver as it prevents blood to massively flow into the aneurysm.

Blood flow reduction progressively lead to filling the cavity and prevent additional flow

Courtesy of A. Frangi, CIBER-BBN

Peak-systolic flow pattern and wall shear stress(WSS) of the unstented aneurysm model

Peak-systolic flow pattern and wall shear stress(WSS) of the stented aneurysm model


Maximum principal stress

distribution

Deformed Stent-Artery Configuration

• As a result of structural analysis, stress, strain and deformation in the stent are obtained as well as the position of the deformed artery, possibly the plaque and the deployed stent.

• This initial configuration may lead to important fatigue modeling as the stent is expecting to experience 400 million cycles during its lifetime.

• Finally, this provides the at rest configuration for hemodynamics applications critical to evaluate different stent model performances.

INLET

OUTLET

WALL

Velocity profile: parabolic and transient

Constant fixed

pressure

No slip

condition

Fluid mechanical perturbations induced by stent implantation: a numerical study Rossella Balossino, Francesca Gervaso, Francesco Migliavacca, Gabriele Dubini LaBS, Department of Structural Engineering, Politecnico di Milano, ITALY


Influence of the Stent Geometry on Hemodynamics

A pulsatile flow analysis for hyperemic condition is performed for a freshly deployed stent in a human coronary artery having a three dimensional geometry with an axial length of 20 mm and a larger diameter (6 mm).

Following the deployment of the stent, it is assumed that half of the stent is exposed to the blood flow whereas the other half is embedded in the arterial wall.

A constant blood density of 1.05 gm/cc and an infinite-shear-rate viscosity of 3.45 cp were considered. Time-varying, uniform velocity boundary condition was imposed at inlet boundary

A close-up view of the velocity vector at time 3.14 s shows significant recirculation zones in the voids created by cross-links of stent wires.

Rupak K. Banerjee (1), John Straus (2), S. Subbiah (2), K. Bhargava (3), Lloyd H. Back (4) Developing pulsatile flow through the entrance region of a deployed stent in a coronary artery

velocity vector near the stent and artery interface at time t= 3.14 s

Cycle for post-processing (2.4-3.2 s)

Time, 100th of s


Influence of Strut Shape on Restenosis

The results show that the presence of stents markedly affects the WSS distribution.

In the stent strut, the corners are the critical areas, i.e., areas with low wall shear stress (


Click to edit Master text styles

MRI Induced Implant Heating Simulation using ANSYS Workbench

HFSS Simulation

Transient Thermal Simulation

+ =

Temperature Profile


Fluid Mechanical Systems Enables Innovative Treatments For Preventive Medicine

Business Initiative:

Increase Market Share

• Clinicians are demanding for customized solution for critical client pathologies but very cautious with new

solutions.

• Convincing a surgeon a new solution is better and safer requires lot of clinical and visual evidences

Customer Success Factors

•ANSYS Fluent is used to analyze the blood flow for a specific patient with a given stent model.

•The Cardiatis stent value is shown by comparing the blood flow for the same patient without any implant.

“Cardiatis has invested significantly in the latest simulation and analysis tools such as ANSYS and

Mimics®. These tools allow virtual simulation so clinicians may evaluate the effect of the implanted

MFM® on blood flow, velocity, wall shear stress (WSS) and peak WSS.”

Ait Brik Boubker Virtual Simualtion & Core Lab Director Cardiatis

Key Results

• Stenting solution could be customized in days for specific patient • More surgeons adopting the Cardiatis solution after

20x more patients benefiting

from simulation

Risk of complication

divided by 5

Gro

wth

Co

st


Contents


• Case Studies:




• Industry Practice



Contents


Comprehensive Multiphysics Solution

Simulation Assisted FDA Process

Virtual Human Environment

Systematic Use of Simulation Early

In silico Testing

Robust Design Optimization

Collaborative Simulation Environment

HPC Scale Up and HW Optimization

System Architecture Design

Embedded Software Design

Early Reliability Prediction & Simulation


Fluid Structure Savvy Devices – Value Proposition

• Better performance optimization through deeper system knowledge

• Ensure product integrity through comprehensive modeling

• Accelerate product development process through virtual prototyping

• Minimize warranty cost

• Boost disruptive innovation rate

• Combine best in class software in each physics

• Provide integrate though autonomous environment for both physics

• Encompass FSI in a working env. (DoE, HPC, Custom., bidirectional CAD, optimization etc.)

• Combine multiphysics with multi scale and system level approach

• Sequential & simultaneous FSI in the same env.

• Modern complex devices involve both fluid and structure

• Optimizing product performance is getting more complex with multiphysics

• Ensuring product integrity despite FSI

• Reduction of design safety margin without compromising with safety

• FSI induces failure modes often missed during product design

Business Impact Typical Challenges ANSYS approach


Fluid Structure Savvy Devices Best Practice Summary

Fluid Structure Interaction: Accounting for interactions between fluid and structural aspects to better optimize the product performance while protecting it against real life failure modes to improve its reliability.

Thermal

Structural Fluid

Electromagnetic

Fluid

Structure

Interaction

Simulation

Data Mapping

Dat

a M

app

ing

Data Mapping

Dat

a M

app

ing

Verification & Validation

• Reliable model of the complete mutliphysics system

• Optimization DoE of advanced system

Capabilities: • Couple fluid and structure

component in a single environment

• Conduct system multiphysics investigations

Benefits: Maximize product

performance through optimized combination of fluid and structure

Prevent fluid induced structure failure or erosion

Efficiently evaluate large number of FSI device in a single environment


Designers want to improve cost, performance, reliability and time to market

Impact/Value Challenges ANSYS Approach

• Clinically validated model

• Coupled modeling whenever necessary (diagnosis)

• Sequential fluid structure modeling:

Advance transient structural model

Hemodynamic simulation after deployment

• Design exploration on combined system

• Customization for democratization

Product Development Time (Months)

-60%

After Before

• 60% reduction in time

Product Cost (&)

-30%

After Before

• 30% reduction in Product Cost

Induced stress during stent deployment (Abdominal Aortic Aneurysm)

Restenosis induced by local blood flow after stent deployment

Evaluation of aneurysm risk of rupture

(cerebral aneurysm)

• Intangible maximization of treatment reliability


Computer Based Models Accepted in the FDA Approval Process

Ch

alle

ng

es

So

luti

on

Im

pac

t

• FDA approval process

long and costly

• Requested measurement

difficult

• Any approval question

leads to more experiment

• Verification and Validation

• Validate new solutions with

same simulation protocol

• Virtual FDA testing on a

large number of patients

• Decrease of warranty

cost

• Improved customers

satisfaction

• Reduction of physical

testings

Animal Bench

Computational Human

Safety/Efficacy


FDA Analysis of Product Recalls from FDA Report “Understanding Barriers to Medical Device Quality”

“failures in product design and manufacturing process control caused more than half of all product recalls”

http://www.fda.gov/downloads/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDRH/CDRHReports/UCM277323.pdf


OSEL Regulatory Support

The FDA Speaks Our Language

http://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDRH/CDRHReports/ucm126739.htm



Publicly Available FDA Presentations Highlighting Computational Modeling

https://sites.google.com/site/cm4cvd/presentations/fda-presentation-on-computational-modelinghttp://www.imagwiki.nibib.nih.gov/mediawiki/images/f/f1/IMAG_&_MSM_2011_-_Morrison.pdfhttp://cstools.asme.org/csconnect/pdf/CommitteeFiles/36699.pdfhttp://xaviermedcon.com/wp-content/uploads/2012/05/Shuren - Keynote Presentation.pdf



Will provide reporting best practices for computational modeling studies

Recent publication in J. Biomech. may provide early insight

Sections:

Guidance on Reporting Methods for M&S*

2.4 Verification

2.5 Validation

2.6 Availability

* draft expected November 2012

2.1 Model Identification

2.2 Model structure

2.3 Simulation structure

http://www.jbiomech.com/article/S0021-9290(11)00720-2/abstract


The Full Benefit of Modeling is Obtained through a Systematic Use of Simulation Early

Ch

alle

ng

es

So

luti

on

Im

pac

t

• Problems identified too late

in the PDP

• Persistent uncertainty for

new concepts

• Baby step innovation, no

major breakthrough

• Bring simulation upfront in

the PDP

• Quick feasibility study for all

promising new concepts

• Develop comprehensive

model through the PDP

• Run 10 to 30 times more

prototypes

• Reduce design cost by a

factor 2 to 4

• Cut time to market by a

factor 2 to 5

Complex heat exchange situation involving

• Neonate body insufficient metabolic heat versus higher energy demand

• Heat exchange with surrounding air flow

• Radiant heat loss through the wall

• Water loss through transpiration and respiration

• Radiation from electromagnetic wave

Delicate multiphysics / multiphase situation

• Providing externally heated and humidified air

• Minimizing heat loss during any intervention

• Reducing radiant heat loss with double walled devices

• Fine controlling the heat exchange through convection and evaporation


Simulation Directly Impacts the Bottom Line

Problem: Optimizing the fill patterns and mixing in the Polonator gene sequencer*

Solution: Used FLUENT to understand the filling patterns, mixing, and residence times in the microfluidic circuit.

Outcome: Microfluidic circuit geometry optimized to get uniform mixing. They also scale-down the channel height without compromising efficiency.

Cost savings: Operating cost reduced by a factor 10.

“The tenfold decrease in channel height resulted in an operating cost reduction by a factor 10!”

* Desktop Engineering, August 30, 2009

“The time to get to final design would have doubled or tripled without CFD analysis.”

Channel geometry Speed contours Pressure contours


Designers Create Virtual Human Laboratory to Quickly Test New Concepts Early

Ch

alle

ng

es

So

luti

on

Im

pac

t

• Few disruptive innovations

• Prohibitive cost to try new

creative idea

• Difficulty to get sustainable

competitive advantages

• 3D multiphysics model:

human body and solution

• Virtually test any promising

idea

• Build confidence through

What if analysis and DoE

• Increased product

throughput

• Reduced risk of failing

approval process

• Brand impact as leading

company

Courtesy of Kleinstreuer & Zhang, NCSU

Rudimentary Delivery

• More than 50% of particles do not reach the trachea; less than 5% reach any specific target

• Massive waste of expensive medicine

• Endangering patients treated with aggressive API

Target Delivery

• Patient Specific upper airflow

• Ensure Laminar flow

• Back track particles reaching target(s)

• Identify precise injection location

• Design / adjust inhaler for proper injection


Designing the PediaFlowTM Ventricular Assist Device

Each year, over 35,000 children are born with congenital heart disease

• 1,000 will die before 10th birthday

PediaFlow increases pump's life span

It prevents recirculating flows

• Minimizes blood clot formation

Geometry adjustments reduce blood cell exposure to shear stress • Less damage to blood

New device delivers adequate flow rate to heart

Engineering simulation reduces design time

"CFD-based design optimization with ANSYS CFD solver reduces the design optimization cycle from years, compared to the traditional trial-and-error methods, to just several months."

J. WU, LaunchPoint Technologies, Inc.

PediaFlow Ventricular Assist Device New born virtual

cardiovascular system


A Robust Design Optimization Approach Boost Performance and Reduce Warranty Cost

Ch

alle

ng

es

So

luti

on

Im

pac

t

• Systematically testing every

configuration

• Identify stable optimums

• Consider routine and

extreme service conditions

• Automated Design of

Experiment

• Stability Analysis

• Reduced order Models

• Design for Six Sigma

• Extreme Reliability

• Get it Best the First Time

• Sustainable competitive

advantage

Position of the impellers has significant effect on mass transfer coefficient.

Design

point

Liquid

level

(in)

Bottom

impeller

offset

(in)

Top

impeller

offset

(in)

Gap

between

impeller

s (in)

Baffle

offset

(in)

Length

of

spargin

g area

(in)

Angula

r

velocity

(rpm)

Gas

mass

flow

rate

(kg/s)

Bubble

diamete

r (m)

Volume

average

d Kla-

hr

DP0 55 14 30 16 10 6 120 2e-3 0.003 81.96

DP1 55 14 35 21 10 6 120 2e-3 0.003 70.46

DP2 55 14 40 26 10 6 120 2e-3 0.003 70.05

DP3 55 18 30 12 10 6 120 2e-3 0.003 65.56

DP4 55 18 35 17 10 6 120 2e-3 0.003 67.13

DP5 55 18 40 22 10 6 120 2e-3 0.003 78.47

DP6 55 22 35 13 10 6 120 2e-3 0.003 75.85

DP7 55 22 40 18 10 6 120 2e-3 0.003 73.07

DP8 55 22 45 23 10 6 120 2e-3 0.003 81.80


In Silico Testing Validates New Prototypes on Large Cohort of Patients Before Clinical Testing

Ch

alle

ng

es

So

luti

on

Im

pac

t

• New products tested on

cohort of patients

• Major cost / unacceptable

risk of failure

• Long approval process

delays access to new care

• Working with large database

of patient specific data

• Virtually testing new concept

on virtual patients

• Enter formal approval when

all virtual tests successful

• Risk of failing approval

dramatically reduced

• Multiply submission of

disruptive solution

• Global time to market

significantly reduced

Validation on

120 + femurs


A Collaborative Simulation Environment Leverage all Available Resources Efficiently

Ch

alle

ng

es

So

luti

on

Im

pac

t

• Remotely located staff

• People with different

background

• Knowledge transfer btw

senior and junior engineers

• Centralized project,

remotely accessible

• Integrate platform where

various physics and multi

levels co-exist

• Customization & Automation

• Experts in each physics can

collaborate on single model

• Senior experts in modeling

coach junior analysts

• Virtual teams able to work

24h /day on a given project


Increasing Productivity Through Staff Efficiency

Business Initiative: Reduce Late Stage Design Fixes Design problems caught late in the product development process are costly to address, but for most companies are a cost of doing business. As a result many companies deploy engineering resources late in the development process to help fix design and manufacturing problems prior to product rollout. Oticon sees a better way of operating. They reallocated engineering resources to the early stages of the design process to explore the design space more fully and avoid decisions that might lead to problems later.

Action Taken Oticon was successfully using ANSYS tools to properly design the most important components but engineering resources were short to optimize the entire system what led to an unacceptable design.

Using the ANSYS’ Application Customization Toolkit (ACT) Oticon was able to integrate advanced acoustic capabilities and Oticon’s best practices in a designers accessible workflows. By making complex physics such as acoustic and frequency dependent material mechanical models, accessible to non-expert simulation users in an application specific environment, ACT enables them to perform design validations with ANSYS.

Key Results By adopting ACT driven multiphysics simulation, Oticon was able to democratize the use of simulation without compromising with the reliability of results. More than 75% of simulations traditionally done by experts can now be delegated to non-expert simulation users. Specifically, Oticon freed the simulation experts to model the full system, the complete hearing aid, rather than just some of its key components. This translates in significantly better performance leading to more competitive products and eventually will eradicate costly and time consuming late stage design fixes.

Martin Larsen Simulation Specialist Oticon

“ACT and ANSYS Workbench enable Oticon A/S to achieve our objective for design engineers to successfully employ simulation. Our goal is to leverage Workbench and the ACT environment: – Flexible and open – Ease of use (easier deployment) – Captures Oticon’s business specific (expertise & know how)”


Centralized HPC Scale Up and Hardware Optimization Support the SDPD* Vision

Ch

alle

ng

es

So

luti

on

Im

pac

t

• Some models request

parallel computing and DoE

• Distributed staff needs

access to resource

• Numerous large simulations

to be run by all facilities

• Centralized computational

resources accessible

remotely.

• Highly scalable HPC and

Access to Cloud

• Affordable DoE

• DoE across the company

even for limited local needs

• Sharing a pool of software

amongst facilities

• Democratization of

simulation towards patient

* Simulation Driven Product Development

Observed trend at ABB:

• Integration in design processes (non-expert users)


Simulation is Deployed at the System Level to Ensure Complete Robust Design Optimization

Ch

alle

ng

es

So

luti

on

Im

pac

t

• Interacting components

• Influence of the

environment

• Multiphysics, multiscale

products

• Integrated platform

• Interacting multiphysics

• 0D to 3D modeling

• Optimized system rather

than components

• Warranty cost reduction

• Accelerate PDP

especially for complex

products

Dynamically Linked HFSS/Designer Simulation


Cutting Time to Market by Three Months for Body-Worn Wireless Devices for New Applications.

Business Initiative: Amplify Engineering Productivity Designing external wireless design safe for the patient and robust in terms of reliable transmission. The radiated power of the device must be kept below levels that can create a health hazard. The device’s power consumption, size, aspect ratio and weight must be minimized to make it suitable for wearing. Yet the device must be designed to deliver a signal of sufficient power to the right location, with good reception by the target device — despite the fact that the human body may absorb a significant portion of the signal.

Action Taken Synapse uses the ANSYS HFSS 3-D full-wave electromagnetic (EM) simulator and the ANSYS human body model to evaluate the performance of various antenna designs by modeling the complete system, including the wireless device and antenna and their interactions with the human body.

Key Results Guided by simulation, electrical engineers typically can increase the range of the product by a factor of five, relative to the initial concept, while saving an estimated three months out of a traditional 12-month development cycle. The ability to evaluate designs without building physical prototypes typically helps Synapse engineers to increase the global performance of the antenna by a factor of five compared to the original design concept.

Bert Buxton, Senior Electrical Engineer Synapse Product Development

“Synapse uses ANSYS HFSS and the ANSYS human body model to evaluate performance of various antenna designs by modeling the complete system, including the wireless device and antenna and their interactions with the human body.”

Simulation output shows power

absorbed by foot and ground.


Smart Product Models Combine Hardware and Embedded Software Simulation

Ch

alle

ng

es

So

luti

on

Im

pac

t

• Reliability of software

interacting with hardware

• Interface design

• Certification

• Integration of soft and had

component in the model

• Automatic code

generation

• Certified software

• Acceleration of process

development

• Increased system

reliability

• More complex solution

IEC 62304 Relationship to Other Standards

derived

PROTOTYPE & DESIGN


Contents


• Case Studies:




• Industry Best Practice




Market Drivers Tightening Regulation

Ageing population

Accessing Care

Key Business Initiatives Boost innovation rate

Maximize Product Reliability

Reduce Cost

Industry Best Practices: Systematic use of simulation early

Simulation Assisted FDA Approval

Collaborative Simulation Environment

These evolutions apply across medical sectors; they are progressively reaching the clinical world

Wrap up

Rising health care cost

No mercy for failure

Reduce time to market

Enable remote personalized med.

Globalize resource

In Silico Testing

Virtual Human Laboratory

Certified Embedded Software


How quickly this Medical Revolution will succeed depend upon you and us.

Ind

uce

d In

no

vati

on

Rat

e

Time

In Vivo Surgery


In Silico Preventive personalized medecine



Thank you for your attention!

Let’s learn from others how they succeed. Let’s share and customize Best Practices To Maximize your ROI from our products

Documents

Entering the in silico era · 2015. 11. 13. · 1 © 2015 ANSYS, Inc. November 12, 2015 ANSYS Confidential Entering the in silico era Simulation Driven Product Development in the