SBIR-STTR Award

GP Technologies, Inc. proposes an innovative vehicle reliability approach for military vehicle that employs the most recent advances in engineering and numerical analysis software using high-performance computing. The proposed reliability approach uses a fast stochastic physics-based model simulation, including finite element and progressive damage models, that is integrated with refined stochastic response approximation techniques based on high-order stochastic field models. Bayesian framework is applied for handling modeling uncertainties, lack of data and stochastic model updating based on new evidence from test data or field observations. The proposed reliability approach includes the key multi-physics, including nonlinear behaviors and interactions between damage mechanisms, such low-cycle-fatigue-high-cycle fatigue and corrosion-fatigue interactions that can affect significantly predicted failure risks. Multi-scale aspects are integrated from the overall vehicle system behavior, to subsystem and component behavior, and down to very local material meso-scale fracture mechanics behavior. The proposed vehicle reliability approach bases on a fast stochastic finite element analysis technology that uses parallel graph-partitioning and parallel algebraic multi-grid gradient combined with gradient conjugate solvers. In Phase I, the proposed reliability approach will be demonstrated on the HMMWV front-wheel low-control arm subsystem. The effects of modeling uncertainties, due to finite element modeling and limited number of analysis runs will be also investigated.

Benefits:
The commercial potential is very high since better tools for more accurate prediction of vehicle reliability will be always in high demand. The developed new technology can be applied to air and ground vehicles, and more generally to any high-tech system that needs a high reliability since its failure is too costly for society.

Keywords:
vehicle reliability, high-perfomance computing, stochastic damage, stochastic finite elements, dynamics, maintenance, meso-scale fracture mechanics

The proposed work will provide a complete vehicle reliability prediction approach and tool. The proposed approach integrates in a high-performance computing environment last advances in engineering analysis and stochastic modeling. It uses fast stochastic physics-based model simulation, including advanced finite element and progressive damage modeling, integrated with refined stochastic response approximation techniques. Bayesian framework is applied for handling modeling uncertainties, lack of data and model updating based on new evidence from test and field observations. A new conceptual “physics-based” Bayesian updating technique is included. The proposed approach includes the key multi-physics, including nonlinear behaviors and complex interactions between damage mechanisms, such LCF-HCF and corrosion-fatigue interactions that could affect significantly predicted failure risks. Multi-scale aspects are integrated from the overall vehicle behavior (using ADAMS), to subsystem and component behavior, and down to local meso-scale fracture mechanics behavior. The heart of the proposed reliability approach is an extremely fast parallel stochastic finite element technology that uses an efficient graph-partitioning and a parallel algebraic multi-grid preconditioner combined with gradient conjugate solvers. The proposed reliability approach will be demonstrated on the HMMWV front-wheel low-control arm subsystem. The effects of modeling uncertainties due to finite element modeling and limited data will be also considered.

Keywords:
Vehicle Reliability, High-Perfomance Computing, Stochastic Damage, Stochastic Finite Elements, Dynamics, Maintenance, Meso-Scale Fracture Mechanics