The Ohio Supercomputer Center Blue Collar Computing Initiative

The Ohio Supercomputer Center Blue Collar Computing InitiativeStanley C. Ahalt, Ph.D.Executive Director

April 21, 2009

Organization of Talk• Three programs within OSC

– Blue Collar Computing Initiative– Instrument and Analytics Services– Ralph Regula School for Computational Science

• Some Lessons Learned

• Questions

3

Blue Collar Computing: a focused industrial solution

Blue Collar Computing (BCC) provides industrial clients with supercomputing resources, training, and expertise to enhance their competitiveness

OSC introduced the idea of Blue Collar Computing at SC2004

Invited Talk: Towards a High Performance Computing Economy: Blue Collar Computing

Presented by: Stanley C. Ahalt, Ph.D., Ohio Supercomputer Center Pittsburgh, Pa., November 6-12, 2004

20

Nu

mb

er

of

Us

ers

Nu

mb

er

of

Ap

plic

ati

on

s

1 4 64

National Productivity Opportunity

Entry Level

HPCUsers

World Class/Leadership Computing

Nodes 1,000 10,000+

Leading HPC Users (Heroes)

Industry Competitiveness Transformation Challenge

National Labs, University HPC Centers & Commercial HPC Services

Experienced Industry HPC Users

Filling the Expertise Gap Filling the Expertise Gap

NeverEverUsers

Adapted from OSC GraphicsCouncil and USC ISI Proprietary

Moving Users ForwardMoving Users Forward

Blue Collar Computing: Filling in the missing middle

• Two classes of industrial clients:– Experienced HPC users who

need access to larger systems for specific tasks (“peaking” facility)

– Users new to HPC who want to solve a specific problem and typically do not want to deal with the complexity of using HPC

• BCC approach to novice – and some experienced – users is to develop industry-specific portals in collaboration with industry trade groups and industry-focused consulting firms

Blue Collar Computing now focused on novice and experienced industrial uses

Edison Welding Institute: An Ohio Success Story

EWi creates high-paying manufacturing technology-based jobs in Ohio: 136 current employees >65% engineers and

technicians >50% advanced degrees >7 years average tenure $84,000 average compensation

$0

$50,000,000

$100,000,000

$150,000,000

$200,000,000

$250,000,000

$300,000,000

$350,000,000

Total Return on Ohio’s Investment in EWi: $305,005,000

149 Ohio companies 234 Ohio manufacturing plants 6 Ohio regions

EWi fuels manufacturing technology advancement for companies throughout Ohio:

E-Weld Predictor: Partnership between OSC and Edison Welding Institute (EWi)

9

E-Weld Predictor is accessedusing a web browser

E-Weld Predictor Impact

Previously E-Weld

Expertise Needed Ph.D. B.S.

Run Time 52 days 4-5 days

Solution Time 6-8 months 1-2 months

E-Weld Predictor Example Output

16

E-Weld Updated Features• New weld geometries

– Bead on plate, Bevel Groove, Compound Bevel

• Weld bead customization– Weld engineer can shape and place weld beads for use

in virtual prototype

• Automatic bead recognition– Weld engineer can use image of existing weld cross-

section to create a weld bead

Modeling and Simulation in the Design Process

Customer

Needs

(Re-) Design

the Tire

Build a

Prototype

Test the

PrototypeOK?

Use Experience

& Empirical Rules

Release to

ProductionNo

(Re-) Design

the Part

Build a

Prototype

Test the

PrototypeOK?

Yes

Customer

Needs

(Re-) Design

the Part

Generate a

Numerical Model

Analyse

the Model OK?

Use Experience

& Numerical Results

Build a

Prototype

Test the

Prototype

OK?

Release to

Production

No

No

Yes

Yes

Explore Digitally……Confirm Physically

From Loren Miller (Goodyear)And Tom Lange (P&G)

18

• Polymers and plastics is a large industry sector in Ohio (2800 companies, 175,000 employees), est. $49B industry

• Many are Tier 2 and Tier 3 suppliers• Many have adopted automation (“lights

out” operation) in the manufacturing process

• Most do not use modeling and simulation

• The polymer portal will provide:– Access to expertise in polymer

science and engineering– Computational resources and

software for modeling and simulation– Databases with relevant material

properties– Access to Advanced instrumentation – Training– Vendor relevant material– Business intelligence and strategy

Polymer Portal - Partnership between OSC and Polymer Ohio

19

Polymer Portal Computational Application: Prediction of Nanofiber Composite Processing• Problem: Carbon nanofibers are added to compound

before mixing and extrusion to improve material properties. The mixing breaks up the nanofibers, and this affects the final material properties

• Proposed solution: Use multi-physics modeling and simulation to identify optimum processing routes for nanotechnology based fiber composites

20

0 1 2 3 4 5100

101

102

103

104

105

106

107

108

109

1010

1011

Vol

ume

resi

stiv

ity [

.cm

]

CNF[wt%]

Low Shear

High Shear

DESCRIPTION• SOCOM has recently awarded a Phase I SBIR to

Alpha STAR Corporation to design, develop, fabricate and test structural driveline and chassis components to decrease the fixed mass of the Expanded Capacity Vehicle HMMWV.

• Components for future production and current retrofit (aftermarket) will be conceptually evaluated.

• Solution will include an advanced composite material to decrease weight.

TECHNICAL APPROACH• A Value Analysis Value Engineering workshop will

be conducted to rank potential candidate redesigns.

• GenesisTM, an analysis and optimization FEA package from Vanderplaats R&D, will be used to perform the shape and design optimization to maximize weight savings most efficiently.

• GENOATM, a Progressive Failure Analysis FEA package from Alpha STAR, will be utilized to ensure the redesigns will possess the fatigue or durability /reliability requirements for the field.

• SOCOM will perform the field testing.

MILESTONES AND DELIVERABLES• Phase I Month 3 – Run VAVE and Identify and Rank

potential candidates for redesign. • Phase I Month 6 – Develop conceptual designs and

business plan for technology commercialization

• Phase II – Fabricate prototypes, test, confirm, and kick-off production.

Desktop-Only Approach• Limited desktop computing

resources, although greater in recent years, are limited to Quad CoProcessors and 8 GB RAM on the best of systems (64 bit OS).

• This results in having to produce subassemblies, make assumptions on boundary conditions, optimize under these conditions, then plug subassembly change back into whole assembly for verification.

DARPA Pilot: HMMWV CHASSIS WEIGHT REDUCTION

OSC Approach

• Genesis v10 is designed to fully exploit large scale SMP systems such as SGI Altix 3000.

• Full 64-bit support means Genesis can optimize full vehicle models rather than only components.

• Genesis can exploit dozens of processors using SMP parallel to optimize large systems 10x faster than the fastest desktop workstations

• GENOA performs:• Composite Material

characterization and uncertainty evaluation during service

• Life assessment of the structure, durability and damage tolerance analysis/optimization

• Reliability based evaluation/optimization

• Building block verification for certification

23

Status Quo and Opportunities• Current methods of replacing steel components with

composite redesigns include:– Analysis of individual steel components

• Obtain strength and stiffness matrices• Requires assumptions of boundary conditions and loading paths

– Redesign with composite properties including shape and size optimization

• Because metals are higher in moduli, geometry changes are often required to match part stiffness

– Testing composite redesign in subsystem model to measure effects on neighboring parts

• Optimization and testing with HPC– Enables composite design optimization across entire subsystems– Eliminates need for assumptions on boundary conditions and

loading paths


– Blue Collar Computing– Instrument and Analytics Services– Ralph Regula School for Computational Science


• Questions

24

25

OSC Instrumentation and Analytics Services

• Remote instrumentation uses OSC’s state-wide resources– Networking, Storage, HPC, Analytics (web service)

26

• Growing need for managing and analyzing data in many science and engineering areas

• Collaboration across geographically distributed teams essential for most research areas

• Cross-Collaboration between academia and industry increasingly important

• Many funding agencies require cyber-enabling research instruments and sensors

• For most researchers, developing, deploying and maintaining the required IT infrastructure takes away from doing the science

• OSC is well-positioned to provide the SHARED cyber-infrastructure that Ohio researchers can take for granted and simply use: Networking, Computing, Storage

Motivation for Instrument & Analytic Services (IAS)

27

Remote Operation of Scanning Electron Microscope: Partnership between Timken Steel, Stark State University, Ohio State University and OSC

– Timken steel able to develop and improve products through remote use of high powered electron microscope

– Timken leveraged OSCnet connection between the OSU Center for Accelerated Maturation of Materials and Stark State

Portal for CAMM data harvesting and analysis

28

Home

Images

Filter Setup

Help

BatchAnalysis

Results

Welcome John Doe CAMM Image Storage & Analysis Portal

Select Images SubmitStart Select Filter

Logout Services About Contact

Parallel processing of image data sets• Parallel computation using Mathworks Distributed

Computing Engine

• 396 files processed in 6 minutes on 16 processors




• Questions

31

32

Training and Education for Faculty & Students

• Frequent free workshops assist faculty and graduate students with advanced research

– Since July 1, 2005: 171 OSU faculty, researchers and students from 52 departments have attended 27 computational science workshops taught at OSC

• New programs promote STEM education through project-based, interdisciplinary materials

– Undergraduate minor in computational science

– Certificate programs for workforce development

• Collaborative efforts connect OSU with statewide and national communities

– NSF supercomputing centers– DOD shared resource centers– DOE national labs

33

RRSCS Minor Program Overview

• Undergraduate minor program– 6-8 courses per year– 2-year degree: minor in

computational science

• Instructional modules created from a matrix, competencies

• Opportunities for other faculty to fill in with new modules, where necessary

Competencies for Undergraduate Minor

Simulation and Modeling

Programming and Algorithms

Differential Equations and Discrete Dynamical Systems

Numerical Methods

Optimization

Parallel Programming

Scientific Visualization

One discipline specific course

Capstone Research/InternshipExperience

Discipline Oriented Courses

34

Expanding RRSCS to Reach More Students and Current Employees• On March 31, 2008 NSF awarded $1M to OSC,

OSU, and U Akron for workforce education in cyberinfrastructure

• Associate degree in science with concentration in computational science– Grant from NSF with Owens, Sinclair, and

Stark State

• Funding from Board of Regents – Choose Ohio First Bioinformatics Scholarship

Program awarded March 18, 2008– Summer Academy in Computational Science

and Engineering awarded February 19, 2008

Education: Building a workforce competent in computational science

Teacher professional development programs;

workshops for middle and high school students

Model of disease transmission in

human population

Middle and high school students

and teachers

Cause and effect relationships and simple modeling

principles

PLTW training course for teachers; course given to

students

Modeling simple physics phenomena:

statics, gravity, pendulum

Ohio PLTW students and

teachers

Applying models to engineering and

architecture fields

Certificate programUndergraduate minor

programOSC training program

Using commercial computational

package or service to test strength of new

container design

Current workforce

College graduates

Understand use of modeling for business and

research

Certificate and graduate programs; OSC training

courses

Applying protein folding simulations to discover candidates

for new drugs

Engineers/scientists in

university and business

Expert in Applications

ProgramsExampleAudienceLevel




• Questions

36

IBM Global Business Services

© Copyright IBM Corporation 2006

OSC BCC Economic Competitiveness Assessment | 04/19/2023 37 of 18

Phase Three Executive Summary

HPC focus must change from economic development to facilitating economic competitiveness

New metrics must be developed to measure improvements in economic competitiveness so HPC contributions can be identified

Supercomputing centers must market to and better support corporate users

Economic development focus has changed from the creating jobs to technology development and commercialization for long-term sustainable competitive advantage

Government should concentrate on creating collaborative networks between public and private sector players, fostering innovation and commercialization of new ideas, attracting investment in knowledge-based industries, and providing access to critical services for new value-add jobs

Ohio should shift its focus from the traditional measure of job creation within manufacturing industries to efforts that support the development of science and technology resources within the state

Joining and Biosciences are still the top priorities for BCC

IBM Global Business Services

© Copyright IBM Corporation 2006

OSC BCC Economic Competitiveness Assessment | 04/19/2023 38 of 18

Traditional Economic Development

Focuses on growth of jobs in industrial enterprises

- Manufacturing- Distribution- Transportation

Sensitive to transportation, site selection, labor

Facing tough competition from low labor cost regions

Government assists with zoning, site selection, hard infrastructure and tax concessions

Collaborative Economic Competitiveness

Focuses on intellectual capital driven industries

- Research- Technology - Services

Sensitive to access to ideas, collaboration, venture capital

Less susceptible to globalization Government assists with value

networks to promote collaboration and access to critical services

Ohio must define and implement new strategies to be successful. Competitiveness in innovation and technology driven strategy will play a

vital role in creating an impact on economic development in the state.

Economic development has begun to transform as markets and industries become more and more competitive

Some Lessons Learned as an Academic Supercomputer Center

• It is now clear that HPC can improve Economic Competitiveness – across the industrial spectrum. Eventually we will develop a healthy “industrial” HPC ecosystem. Economic Competitiveness feeds Economic Development.

• “BCC” is a social experiment. The technology is not the most challenging issue, except at the adoption stage.

• Economic development is both a contact sport and a team sport. OSC does not have a “sales” force, and teaming is critical.

• Success appears to require that you work the problem along many facets (HPC, portals, software, consulting, instruments, training) and along the entire pipeline (small to large, novice to expert). This is hard to staff!

• Choose the right partners – they will help you reach the community and help you understand requirements (CoC, Ewi, Goodyear, IBM, NCSA, Nimbis, P&G)

• Academia and industry can use HPC as a connector – both benefit!• Commercial software and licensing is a HUGE bottleneck• Engage with your state economic development agency early. OSC is now

required to do economic development – it’s the law.• Workforce development is very important. (leads to insatiable appetites!)• Clearly understand your costs, your value proposition, and your market if you

want to provide industrial support. You have to have capabilities that people want.

• We welcome partnerships.39

40

OSC Partnerships:Academic & Non-Profit Partners

LawrenceLivermoreNational Laboratory

41

OSC Partnerships:Business & Industry

42

Questions?

43

Login page

44

Weld Geometry Selection

45

Bead Placement Tool

46

Elliptical Bead Placement

47

Parabolic bead placement

48

Complex Bead Placement

49

Bead Finder

50

Bead Finder Image Selection

51

Bead Finder: Region Selection

52

…after image recognition

53

Find Bead bitmap

54

Import “found bead” into weld

Impact

• Quantification of use of HPC for defining EMI signature of electrical systems in the DoD supply chain.

• Simulation of full system will optimize performance and reduce delay in product implementation across range of DoD and commercial applications.

• IAP demonstration of 300X improvement

• 6X increase in components

• 50X increase in frequency

Pilot Description

• IAP employs 30 people and is a recognized leader in the field of electromagnetics. IAP provides products and technology to SPD Electrical Systems, a division of L-3 Communications. SPD is a major supplier of electrical systems to the US Navy.

• IAP designs power processing equipment for the Power Node Control Center (PNCC) that maintains power in emergencies. Definition of the electromagnetic interference (EMI) signature is critical for sustained operation, current desktop simulation is limited to subsystem performance

• This pilot will improve power system design and operation by optimizing the EMI signature which will be obtained by transitioning from sub-system to full system modeling.

Technical Approach

• Supported by AltaSim, IAP will access HPC based hardware and software that simulates EMI signature of electrical power systems.

• A case study of IAP will be performed, and a value-stream mapping to quantify the benefit of HPC within the DoD supply chain will be completed.

Milestones and Deliverables

• Month 6 (January 2009) - Quantify benefit of HPC for EMI. Optimize Simulink-based solution. Implement and benchmark HPC-based analysis.

• Month 9 (April 2009) – Extend simulation to incorporate analysis of full power system. Demonstrate advanced parallel solution method.

• Month 12 (July 2009) - Demonstrate success of HPC for simulation of EMI response of PNCC

Opportunities

• Application of HPC allows simulation of EMI signature of full system thereby reducing prototype testing and optimizing performance.

• Prototype testing is time consuming and expensive increasing delays and expense of product implementation.

• Increased survivability, lower mass, cost and volume achieved through optimized performance.

Desktop-Only Approach• Limited computing resources do not

allow analysis of EMI signature of full system.

• Simulation of individual modules within system is limited to simple circuit representations

HPC Demonstration• Apply HPC to simulate EMI

signature of fully integrated electrical power supply system.

QU

AN

TIT

ATIV

E IM

PAC

TEN

D-O

F-PH

ASE G

OA

L

STA

TU

S Q

UO

NEW

IN

SIG

HTS

IAP Research - Electromagnetic Interference Signature Analysis

ElectricalSubsystem

Analysis Only

Team - Roles and Responsibilities

Service provider of advanced modeling and simulation for

virtual prototyping

Product development for power systems

OEM Supplier of shock-hardened switchgear – Power Node Control Center

Service provider of high performance computing expertise and resources

Application• Power Node Control Center

(PNCC)• Provide continuous power to

critical systems• DDG 102 - Radar room• New approach provides

miniaturization – Increased power density– Increased conductive EMI

• Developed by IAP/SPD Electrical Systems

Challenge• High frequency switching produces EMI that affects

critical control systems• Eliminate/Reduce EMI

– Circuit design for conductive– Shielding for radiated

• Current solutions are testing/evaluation with predictive design methods

– Experience based– Extensive testing– Non-optimum solution

• Current analytical applications used as guidance due to limitations:

– Desktop MATLAB/Simulink– Frequency range - 2.5 MHz– Model size – Simple module – Model complexity – Single module– Simplifying assumptions –

Parasitic and frequency dependence

• Current analysis times:– Simple module > 7.5h– Complex module cannot be analyzed – PNCC cannot be analyzed (range from 3-10

modules)

EMI Solution – Current work flow

Design Analyze Manufacture Test Pass

ExemptionFail

Re-designAnalyze

~$40K

~$200K/4 months ?

Design-Analyze-Test-Redesign-Manufacture

Analysis limited : Exemptions : Non-optimum design : Time consuming

Analysis Targets

• Increase accuracy– Extend maximum frequency to 30 MHz

• Increase size – Analyze entire PNCC subsystem

• Increase speed– PNCC analysis time < 12 hours

• Eliminate assumptions– Parasitic effects– Frequency dependence

• Increase complexity– Incorporate interactions of multiple modules

EMI Solution – Future work flow

Analysis driven : No exemptions : Optimized design : Process control

Design Analyze/Optimize

Manufacture Test Pass

Design-Analyze-Manufacture

Analysis Analysis

62

Impact

• The >10X simulation speedup provided by HPC enables:

• Full vehicle models rather than only components.

• Composite Material characterization and uncertainty evaluation during service.

• Life assessment of the structure, durability and damage tolerance analysis/optimization.

• Reliability based evaluation/optimization.

• Building block verification for certification .

Pilot Description• Alpha STAR Corporation has a recently awarded

SOCOM Phase I SBIR to design, develop, fabricate and test structural driveline and chassis components to decrease the fixed mass of the Expanded Capacity Vehicle HMMWV.

• The solution will include an advanced composite material to decrease weight. Components for future production and current retrofit (aftermarket) will be conceptually evaluated.

Technical Approach• A Value Analysis Value Engineering workshop will be

conducted to rank potential candidate redesigns.

• Alpha STAR will access HPC hardware at the Ohio Supercomputer Center. The GenesisTM FEA package will be used for design optimization. The GENOATM Progressive Failure Analysis FEA package, will be used to ensure the redesigns will meet the fatigue or durability/reliability requirements

• A case study will be completed and Alpha will define a value proposition to quantify the benefit of HPC for the HMMWV redesign.

Milestones and Deliverables• Month 3 (October 2008) – Complete value analysis to

identify and rank potential candidates for redesign. • Month 6 (January 2009) – Develop conceptual designs

and implement preliminary models.

• Month 9 (April 2009) – Demonstrate concept design models using software hosted at OSC.

• Month 12 (July 2009) – Release case study report.

Opportunities

• Application of HPC to optimize product performance by eliminating subsystem modeling in isolation.

• Subsystem modeling in isolation leads to additional design cycles and expensive physical prototype testing.

• Whole-vehicle modeling provides increased design yields and reduced operational costs.

Modeling Analysis Limited to Subsystem by Desktop-Only

• Limited computing results in having to produce subassemblies, make assumptions on boundary conditions, optimize under these conditions, then plug subassembly change back into whole assembly for verification.

HPC Demonstration• Simulate subsystem in whole-

vehicle model using HPC.

QU

AN

TIT

ATIV

E I

MPA

CT

EN

D-O

F-P

HA

SE G

OA

L

NEW

IN

SIG

HTS

Alpha STAR Corp - Chassis Weight Reduction for HMMWV

STA

TU

S Q

UO

63

First generation: ECV-HMMWV Phase 1 SBIR• AlphaSTAR won Army SBIR in 2007 for ECV-HMMWV

– Expanded Capacity Vehicle - High-Mobility, Multi-Purpose Wheeled Vehicle

– Utility vehicles have added anti-ballistic and anti-IED protection, leading to increased vehicle weight

– Additional armor/equipment negatively impacts power train, suspension, steering and chassis components

• Goals– Reduce weight by 2,500 lbs– Cost less than $5 per lb weight saved– Maintain or exceed current performance

• Structural integrity• Durability/Reliability• Vehicle Dynamics

64

HPC ISP Pilot• Polymer composite virtual prototyping applied to HMMWV

chassis weight reduction– SBIR effort demonstrated potential of virtual prototyping approach

• Integrated set of capabilities– Modeling and simulation– Optimization (e.g., material composition within component)– Durability and reliability testing– High performance computing for full subsystem models

• Reducing weight impacts critical problems– Durability and longevity for heavily armored vehicles– Improving vehicle fuel efficiency

65

Participants• AlphaSTAR

– 19 years of successful industry experience in advanced composites structural simulation, design and test

– Forensic and failure simulation of large scale industry structures (e.g., US Space Shuttle Accident Investigation, Shuttle Return to Flight, Airbus 310, Boeing Delta Rocket tank)

– GENOA: software for durability/reliability testing of composites

• CompositesDoc– Spin-off from National Composite Center Design branch in 2006– Primary contractor to OSC; responsible for design optimization

• Ohio Supercomputer Center– 20 years of High Performance Computing (HPC) capacity and

expertise to educational and research communities – HPC hosting/consulting and program management

66

Virtual Prototyping Method

• Design objectives– Minimization of mass or

cost (or weighted function of both)

• Design variables– Ply thickness, fiber

orientation, position of nodes

• Design constraints– Maximum and minimum

deflection (stiffness), strength/failure index

FEM computing challenges: durability and damage, tolerance, optimization, and reliability

Problem Problem Goal Problem SizeComputin

g Time

Space Shuttle Foam Analysis

Predict Fracture Toughness and

Reliability360 GB of data > 2 Days

Composite Truck Chassis

Predict Residual Strength and Life 600,000 FEM 2 Weeks

AutoComposite Structure

Crash Analysis 400,00 FEM 8 Hours

HMWWV Structure System Durability and Reliability Analysis Ultra-large Multiple

Weeks

Army Composite Bridge

Predicted Strength and Life After Battle Damage

Repair

100,00 FEM 2 Days

Delphi/Delco Microelectronic

Chip

Thermal Aging Analysis >1,000,000 FEM > 2 Weeks

Siemens combustor liner

Fatigue Life Prediction Large > 2 Weeks

PFA: broadly applicable technology that requires HPC

68

Analysis Environment

• NASTRAN, MSC • Genesis, Vanderplaats R&D• Genoa, Alpha STAR• Glenn, OSC HPC cluster

• Upgrade in Summer, 2009• Total: 9,500 cores, 75 TF,

24 TB RAM

69

Schedule Update• VAVE value analysis (complete)

– Internal analysis complete

• Component model: Control Arm (complete)– Develop and optimize model– Reliability and durability tests

• Subsystem model: Front Suspension System – Optimize model– Reliability and durability tests– HPC impact analysis

• Case study report

70

Control Arm Optimization

MaterialWeight

(lb)

Material Cost

($)

Ultimate Load

(lb)

Normalized Stiffness

AISI-4130 Steel Alloy(original design) 32.4 24.8 7,482 1

Glass/Steel Hybrid 12.1 18 3,559 0.39

Nanocarbon Composites/Steel

Hybrid11.43 284 8,000 0.75

Carbon Composite/Steel Hybrid 11.9 101

(~8000 lb)

.75

Optimized Carbon Composite/Steel Hybrid 9.82 145 17,775 1.02

Damage Growth at 12000 lb

Fracture at 13000 lb

Volume of control arm before optimization: 106.9 in^3Volume of control arm after optimization: 132 in^3

Metallic and Hybrid Composite Design

Failure Mechanism contribution

Optimized Shape

Optimized Control Arm with minimum weight, & comparable steel stiffness

Hybrid Control ArmDamage Initiation at 8000 lb

72

Control Arm Test Results

0.999 Reliability is achieved applied load is kept under 4,399 lb

Reliability and Sensitivity Analysis

Probabilistic Sensitivities

Optimized Hybrid Control Arm considers material, geometry and fabrication

Void

Fiber content

Fiber stiffness

CompositeThickness

74

Summary• Polymer composite components can help create lower

weight vehicles for unique defense applications (additional armor) and general purpose use (increased fuel efficiency)

• The team has successfully applied virtual prototyping to component level design

– Control arm: reduced weight by 3.3x, increased load by 2.3x at a cost of $5.75/lb.

• HPC to be used extensively for simulation of entire front suspension system

• HPC-based virtual prototyping capability can benefit numerous applications beyond HMMWV

Documents

The Ohio Supercomputer Center Blue Collar Computing Initiative