Passive vibration suppression of flexible space structures via optimal geometric redesign

Abstract

A computational framework is presented for the design of large flexible space structures with non-periodic geometries to achieve passive vibration suppression. The present system combines an approximation model management framework (AMMF) developed for evolutionary optimization algorithms (EAs) with reduced basis approximate dynamic reanalysis techniques. A coevolutionary genetic search strategy is employed to ensure that design changes during the optimization iterations lead to low-rank perturbations of the structural system matrices, for which the reduced basis methods give high-quality approximations. The k-means algorithm is employed for cluster analysis of the population of designs to determine design points at which exact analysis should be carried out. The fitness of the designs in an EA generation is then approximated using reduced basis models constructed around the points where exact analysis is carried out. Results are presented for the optimal design of a two-dimensional cantilevered space structure to achieve passive vibration suppression. It is shown that significant vibration isolation of the order of 50 dB over a 100-Hz bandwidth can be achieved. Further, it is demonstrated that the AMMF can potentially arrive at a better design compared to conventional approaches when a constraint is imposed on the computational budget available for optimization.

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ORCID for Andrew J. Keane: orcid.org/0000-0001-7993-1569

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