Adjoint Differentiation of a Structural Dynamics Solver
Mohamed Tadjouddine, Shaun A. Forth & Andy J. Keane
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, Springer, 2006. ISBN 3-540-28403-6
Presented at
AD2004: The 4th International Conference on Automatic Differentiation, July
19th-23rd University of Chicago, Gleacher Center, Chicago.
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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