遺伝的アルゴリズムによる受動形ジャイロ制振機構の最適設計 : 不規則励振におけるモンテカルロ最適化 Design Optimization of Passive Gyroscopic Damper by Genetic Algorithms : Monte Carlo Optimization under Random Excitations
In this study, genetic algorithms (GAs) are utilized as a framework for Monte Carlo optimizations of the passive gyroscopic damper (PGD). The PGD is an effective damping mechanism for rotational vibrations, however, it shows considerable nonlinearity under strong excitations. The response to the random excitations can not be precisely analyzed by any conventional approximate method. The Monte Carlo approach exploits numerical simulations using pseudorandom numbers as the excitations and a numerical integration algorithm. The Monte Carlo estimations of the station ary response to white noise excitations are combined with the GAs. The quasi-continuous generation model with an algebraic crossover is customized for noisy Monte Carlo estimations, and it realizes a stochastic hillclimber. The gimbal spring constant and the giambal damper coefficient are optimized to minimize the stationary variance of the main system angle, as in the previous study of the same authors, wherein the Fokker-Planck method was employed together with a nonlinear programming algorithm. The GA solutions are again verified by the Monte Carlo estimations to prove their superiority to the previous ones.
- 日本機械学會論文集. C編
日本機械学會論文集. C編 62(595), 829-836, 1996-03-25