Integrating Stochastic Engineering Models in a Distributed Environment

The main objective of this project is to develop an efficient stochastic-optimization approach that can be applied to large-scale high-complexity Army military vehicle applications in a high-speed distributed computer hardware environment. Specifically, the proposed work will integrate the refined stochastic response models (stochastic field expansion approximations) developed by GP Technologies Inc. with the RBDO Probabilistic Measure Approach (PMA) developed by the University of Iowa. The research project team will explore and refine the stochastic response analysis part of the UOI RBDO approach based on state-of-the-art stochastic field/network modeling and artificial intelligence concepts. The proposed development will also include fast Dynamic Monte Carlo simulation algorithms, plus Bayesian inference for optimal model fitting and selection. Phase I will also provide a probabilistic-design methodology for incorporating the data and modeling uncertainties in the RBDO problem and will explore the application of other non-deterministic approaches to RBDO including multi-objective problems. In Phase I the proposed RBDO methodology will be applied to the Army M1A1 tank road arm component via ANSYS FEA code. The RBDO approach will operate in the distributed environment of hardware available at the U.S. Army. The parallel programming task will be conceptually initiated in Phase I and completed in Phase II.