Original Date: 01/27/1997
Revision Date: 01/18/2007
Information : Product and Process Design Modeling
Lawrence Livermore National Laboratory (LLNL) has more than two decades of experience in code development, design, and analysis of finite element software for powerful simulation capabilities. LLNL-developed simulations, available to external users, include manufacturing, structural mechanics, biomechanics, heat transfer, and fluid mechanics.
DYNA, NIKE, and TOPAZ are a family of similar explicit and implicit finite element codes for dynamic and quasi- static structural mechanics and heat transfer. By applying simulation methods to product and process design modeling, LLNL has focused its active research and development on parallelization techniques for these finite element codes and on algorithm developments to enhance simulation capabilities. Figure 3-2 illustrates the significant performance benefits from parallelization.
LLNL recently completed research on rigid body mechanics simulations and advanced nonlinear modeling for large bridge structures. Increased computational speed and memory reduction improvements provide solutions with speed and robustness. Primary analysis solutions for LLNL’s Lasers and Defense and Nuclear Technologies Directorates include optical laser components, support structures, nuclear components, shipping containers, and manufacturing process simulations.
LLNL has substantial expertise in applying simulation techniques for product and process design modeling. Active collaborations outside LLNL include superplastic forming (Boeing); sheet metal forming (Vehicle Maintenance/Technologies Enabling Agile Manufacturing [TEAM] consortium); biomechanics modeling (National Highway Traffic Safety Administration); blade-off simulations (Federal Aviation Administration); seismic safety analysis (California Department of Transportation); sheet metal springback predictions and composite modeling (Partnership for Next Generation Vehicle); and vehicle/road barrier interactions (Federal Highway Administration).
Figure 3-2. Significant Performance Benefits from Parallelization
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