Efficient optimal design-under-uncertainty of passive structural control devices De, Subhayan; Wojtkiewicz, Steven F.; Johnson, Erik A.
This paper proposes a computationally efficient framework for design optimization under uncertainty for structures with local nonlinearities. To reduce the high computational cost of Monte Carlo simulation of such problems, an exact model reduction to a low-order Volterra integral equation is used to accelerate each simulation, and variance-reduced sampling is used to reduce the number of simulations required for the uncertainty quantification. This optimization framework is applied to a benchmark cable-stayed bridge problem, designing one pair of passive tuned mass dampers given a pair of uncertain passive power law dampers, providing significant gains in computational efficiency, two orders of magnitude, compared to traditional approaches.
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