Anzeige
Erschienen in:
2006 | OriginalPaper | Buchkapitel
Adjoint Differentiation of a Structural Dynamics Solver
verfasst von : Mohamed Tadjouddine, Shaun A. Forth, Andy J. Keane
Erschienen in: Automatic Differentiation: Applications, Theory, and Implementations
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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 eficiency, 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 3.0. 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.