Optimal Approximations of Coupling in Multidisciplinary Models

Abstract

Design of complex engineering systems requires coupled analyses of the multiple disciplines affecting system performance. The coupling among disciplines typically contributes significantly to the computational cost of analyzing a system, and can become particularly burdensome when coupled analyses are embedded within a design or optimization loop. In many cases, disciplines may be weakly coupled, so that some of the coupling or interaction terms can be neglected without significantly impacting the accuracy of the system output. However, typical practice derives such approximations in an ad hoc manner using expert opinion and domain experience. In this thesis, we propose a new approach that formulates an optimization problem to find a model that optimally balances accuracy of the model outputs with the sparsity of the discipline couplings. An adaptive sequential Monte Carlo sampling-based technique is used to efficiently search the combinatorial model space of different discipline couplings. Finally, an algorithm for optimal model selection is presented and combined with three tractable approaches to quantify the accuracy of the system outputs with approximate couplings. These algorithms are applied to identify the important discipline couplings in three engineering problems: a fire detection satellite model, a turbine engine cycle analysis model, and a lifting surface aero-structural model.