향상된 유전알고리듬을 이용한 유체마운트의 최적화

Abstract

When designing fluid mounts, design parameters can be varied in order to obtain a desired notch frequency and notch depth. The notch frequency is a function of the mount parameters and is typically selected by the designer to occur at the vibration disturbance frequency. Since the process of choosing these parameters can involve some trial and error, it seems to be a great application for obtaining optimal performance of the mount. Many combinations of parameters are possible to give us the desired notch frequency, but the question is which combination provides the lowest depth. Therefore. an automatic optimal technique is needed to optimize the performance of the fluid mount. In this study. the enhanced genetic algorithm (EGA) is applied to minimizing transmissibility of a fluid mount at the desired notch frequency, and at the notch and resonant frequencies. The EGA is modified genetic algorithm to search global and local optimal solutions of multi-modal function optimization. Furthermore. to reduce the searching time as compare to conventional genetic algorithm and Increase the precision of the solutions, the modified simplex method is combined with the algorithm. The results show that the performance of the optimized mount by using the hybrid algorithm is better than that of the conventional fluid mount.