A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Decoupled approach to multidisciplinary design optimization under uncertainty
Abstract We propose solution methods for multidisciplinary design optimization (MDO) under uncertainty. This is a class of stochastic optimization problems that engineers are often faced with in a realistic design process of complex systems. Our approach integrates solution methods for reliability-based design optimization (RBDO) with solution methods for deterministic MDO problems. The integration is enabled by the use of a deterministic equivalent formulation and the first order Taylor’s approximation in these RBDO methods. We discuss three specific combinations: the RBDO methods with the multidisciplinary feasibility method, the all-at-once method, and the individual disciplinary feasibility method. Numerical examples are provided to demonstrate the procedure.
Decoupled approach to multidisciplinary design optimization under uncertainty
Abstract We propose solution methods for multidisciplinary design optimization (MDO) under uncertainty. This is a class of stochastic optimization problems that engineers are often faced with in a realistic design process of complex systems. Our approach integrates solution methods for reliability-based design optimization (RBDO) with solution methods for deterministic MDO problems. The integration is enabled by the use of a deterministic equivalent formulation and the first order Taylor’s approximation in these RBDO methods. We discuss three specific combinations: the RBDO methods with the multidisciplinary feasibility method, the all-at-once method, and the individual disciplinary feasibility method. Numerical examples are provided to demonstrate the procedure.
Decoupled approach to multidisciplinary design optimization under uncertainty
Chiralaksanakul, Anukal (author) / Mahadevan, Sankaran (author)
2007
Article (Journal)
English
Decoupled approach to multidisciplinary design optimization under uncertainty
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