Uncertainty Propagation in Multi-Disciplinary Design Optimization of Undersea Vehicles 2008-01-0218
In this paper the development of statistical metamodels and statistical fast running models is presented first. They are utilized for propagating uncertainties in a multi-discipline design optimization process. Two main types of uncertainty can be considered in this manner: uncertainty due to variability in design variables or in random parameters; uncertainty due to the utilization of metamodels instead of the actual simulation models during the optimization process. The value of the new developments and their engagement in multi-discipline design optimization is demonstrated through a case study. An underwater vehicle is designed under four different disciplines, namely, noise radiation, self-noise due to TBL excitation, dynamic response due to propulsion impact loads, and response to an underwater detonation. The case study also demonstrates the value of the multi-discipline design optimization in identifying a system level optimum, and it emphasizes the importance of including uncertainties in the design optimization process.
Jim He, Geng Zhang, Nickolas Vlahopoulos
Michigan Engineering Services, LLC, University of Michigan
SAE World Congress & Exhibition
SAE International Journal of Materials and Manufacturing-V117-5EJ, Reliability and Robust Design in Automotive Engineering, 2008-SP-2170, SAE International Journal of Materials and Manufacturing-V117-5