TY - GEN
T1 - Multi-disciplinary conceptual modeling and optimization with uncertainty effects consideration
AU - Liu, Nan
AU - Manoochehri, Souran
AU - Yu, Chan
PY - 2006
Y1 - 2006
N2 - A multi-disciplinary modeling and design optimization formulation for uncertainty effects consideration is presented in this paper. The optimization approach considers minimization of uncertainty factors related to the overall system performance while satisfying target requirements specified in the form of constraints. The design problem is decomposed into two analysis systems; performance design and uncertainty effects analysis. Each design system can be partitioned into several subsystems according to the different functions they perform. Performance evaluation is considered by minimizing the variations between specified expected values of performance functions and their target values. Uncertainty effects analysis is defined by minimizing the ratio of the maximum variations caused by uncertainty factors over the expected function values. The entire problem has been treated as a multi-disciplinary design optimization (MDO) for maximum robustness and performance achievement. An electromechanical system is used as an example to demonstrate this optimization methodology.
AB - A multi-disciplinary modeling and design optimization formulation for uncertainty effects consideration is presented in this paper. The optimization approach considers minimization of uncertainty factors related to the overall system performance while satisfying target requirements specified in the form of constraints. The design problem is decomposed into two analysis systems; performance design and uncertainty effects analysis. Each design system can be partitioned into several subsystems according to the different functions they perform. Performance evaluation is considered by minimizing the variations between specified expected values of performance functions and their target values. Uncertainty effects analysis is defined by minimizing the ratio of the maximum variations caused by uncertainty factors over the expected function values. The entire problem has been treated as a multi-disciplinary design optimization (MDO) for maximum robustness and performance achievement. An electromechanical system is used as an example to demonstrate this optimization methodology.
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U2 - 10.1115/detc2006-99617
DO - 10.1115/detc2006-99617
M3 - Conference contribution
AN - SCOPUS:33751324392
SN - 079183784X
SN - 9780791837849
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - Proceedings of 2006 ASME International Design Engineering Technical Conferences and Computers and Information In Engineering Conference, DETC2006
T2 - 2006 ASME International Design Engineering Technical Conferences and Computers and Information In Engineering Conference, DETC2006
Y2 - 10 September 2006 through 13 September 2006
ER -