An efficient dynamic response optimization using the design sensitivities approximated within the estimate confidence radius
In order to reduce the expensive CPU time for design sensitivity analysis in dynamic response optimization, this study introduces the design sensitivities approximated within estimated confidence radius in dynamic response optimization with ALM method. The confidence radius is estimated by the linear approximation with Hessian of quasi-Newton formula and qualifies the approximate gradient to be validly used during optimization process. In this study, if the design changes between consecutive iterations are within the estimated confidence radius, then the approximate gradients are accepted. Otherwise, the exact gradients are used such as analytical or finite differenced gradients. This hybrid design sensitivity analysis method is embedded in an in-house ALM based dynamic response optimizer, which solves three typical dynamic response optimization problems and one practical design problem for a tracked vehicle suspension system. The optimization results are compared with those of the conventional method that uses only exact gradients throughout optimization process. These comparisons show that the hybrid method is more efficient than the conventional method. Especially, in the tracked vehicle suspension system design, the proposed method yields 14 percent reduction of the total CPU time and the number of analyses than the conventional method, while giving similar optimum values.
Key WordsDynamic Response Optimization Approximate Design Sensitivities
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- Haug E. J. and Arora J. S., 1979,Applied Optimal Design. Wiley-Interscience: New York, pp. 339–354.Google Scholar
- Kim M.-S and Choi, D-H., 2000, “A New Penalty Parameter Update Rule in the Augmented Lagrange Multiplier Method for Dynamic Response Optimization,”KSME International Journal; Vol. 14, No. 10, pp. 1122–1130.Google Scholar
- Kodiyalam Srinivas 1997, “Application of Optimization and Approximation Methods for Design Automation of Aerospace Structures,”Multidisciplinary Design Optimization: State of the Art (Alexandrov NM, Hussaini MY Eds) SIAM, Philadelpia, pp. 411–431.Google Scholar