Post on 30-Dec-2015
description
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
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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
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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)
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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
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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)
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• 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
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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
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Low Shear
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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
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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
Organization of Talk• Three programs within OSC
– Blue Collar Computing– Instrument and Analytics Services– Ralph Regula School for Computational Science
• Some Lessons Learned
• Questions
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OSC Instrumentation and Analytics Services
• Remote instrumentation uses OSC’s state-wide resources– Networking, Storage, HPC, Analytics (web service)
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• 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)
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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
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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
Organization of Talk• Three programs within OSC
– Blue Collar Computing– Instrument and Analytics Services– Ralph Regula School for Computational Science
• Some Lessons Learned
• Questions
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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
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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
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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
Organization of Talk• Three programs within OSC
– Blue Collar Computing– Instrument and Analytics Services– Ralph Regula School for Computational Science
• Some Lessons Learned
• 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
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OSC Partnerships:Academic & Non-Profit Partners
LawrenceLivermoreNational Laboratory
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OSC Partnerships:Business & Industry
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Questions?
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Login page
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Weld Geometry Selection
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Bead Placement Tool
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Elliptical Bead Placement
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Parabolic bead placement
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Complex Bead Placement
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Bead Finder
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Bead Finder Image Selection
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Bead Finder: Region Selection
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…after image recognition
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Find Bead bitmap
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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.
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IAP Research - Electromagnetic Interference Signature Analysis
ElectricalSubsystem
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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
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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.
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Alpha STAR Corp - Chassis Weight Reduction for HMMWV
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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
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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
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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
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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
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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
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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
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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
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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
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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