Adjoint differentiation of a structural dynamics solver
Adjoint differentiation of a structural dynamics solver
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.
3540284036
309-319
Tadjouddine, M.
c06f5d28-30c9-4a25-ab06-c3c269978d02
Forth, S.A.
54814c48-5f67-4e64-b6c1-d8131866fb7a
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
2006
Tadjouddine, M.
c06f5d28-30c9-4a25-ab06-c3c269978d02
Forth, S.A.
54814c48-5f67-4e64-b6c1-d8131866fb7a
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Tadjouddine, M., Forth, S.A. and Keane, A.J.
(2006)
Adjoint differentiation of a structural dynamics solver.
Bücker, M., Corliss, G., Hovland, P., Naumann, U. and Norris, B.
(eds.)
In Automatic Differentiation: Applications, Theory, and Implementations.
vol. 50,
Springer.
.
Record type:
Conference or Workshop Item
(Paper)
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.
Text
tadj_06.pdf
- Accepted Manuscript
More information
Published date: 2006
Venue - Dates:
AD2004: The 4th International Conference on Automatic Differentiation, Chicago, Gleacher Center, Chicago, USA, 2004-07-19 - 2004-07-23
Identifiers
Local EPrints ID: 23888
URI: http://eprints.soton.ac.uk/id/eprint/23888
ISBN: 3540284036
PURE UUID: 84de2124-ad69-4aba-99cf-f3c2c75c1043
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Date deposited: 17 Mar 2006
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
M. Tadjouddine
Author:
S.A. Forth
Editor:
M. Bücker
Editor:
G. Corliss
Editor:
P. Hovland
Editor:
U. Naumann
Editor:
B. Norris
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