Adjoint Differentiation of a Structural Dynamics Solver.

Date

2006-12-01T00:00:00Z

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Article

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Free to read from

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Mohamed Tadjouddine, Shaun A. Forth & Andy J. Keane, Adjoint Differentiation of a Structural Dynamics Solver. Automatic Differentiation: Applications, Theory, and Implementations, Bücker, M.; Corliss, G.; Hovland, P.; Naumann, U.; Norris, B. (Eds.), Lecture Notes in Computational Science & Engineering, Volume 50, p309-319, 2006

Abstract

The design of a satellite boom using passive vibration control by Keane [J. of Sound and Vibration, 1995, 185(3),441-453] has previously been carried out using an energy function of the design geometry aimed at minimising mechanical noise and vibrations. To minimise this cost function, a Genetic Algorithm (GA) was used, enabling modification of the initial geometry for a better design. To improve efficiency, it is proposed to couple the GA with a local search method involving the gradient of the cost function. In this paper, we detail the generation of an adjoint solver by automatic differentiation via ADIFOR. This has resulted in a gradient code that runs in 7.4 times the time of the function evaluation. This should reduce the rather time-consuming process (over 10 CPU days by using parallel processing) of the GA optimiser for this problem.

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Github

Keywords

reverse mode AD, hybrid GA-local search, structural dynamics, performance

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