Efficient Approximation of Coupling manifolds for Decoupling Multidisciplinary Systems

What is it about?

Numerical simulation of high fidelity coupled multidisciplinary systems is often hindered by exorbitant expenses associated with the need to perform fixed point iterations during an analysis. In this paper, we present a novel methodology aimed at exploiting the common additive nature of input-output mappings for physical systems and the flexibility that may exist in accuracy requirements for an analysis at hand. The methodology consists of identifying sparse approximations to coupling variable fixed point sets in univariate and bivariate subspaces using l1-minimization with a specified error tolerance. Cut-high dimensional model representations are constructed from these approximations to provide surrogate models of the input to fixed points in the coupling variable space. The result is a fully decoupled system. The methodology is demonstrated on numerical examples and a fire detection satellite model and performs well in all cases.

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