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.
Uncertainty Propagation in Multi-Disciplinary Design Optimization of Undersea Vehicles
Sae Int. J. Mater. Manf
Sae International Journal of Materials and Manufacturing
SAE World Congress & Exhibition ; 2008
Sae International Journal of Materials and Manufacturing ; 1 , 1 ; 70-79
2008-04-14
10 pages
Aufsatz (Konferenz)
Englisch
Uncertainty Propagation in Multi-Disciplinary Design Optimization of Undersea Vehicles
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