Optimal design of an engine mount using an enhanced genetic algorithm with simplex method

This study provides an analysis of the applications of optimization routines for designing fluid mounts. After summarizing the concept of fluid mounts and their dynamic characteristics, we review the importance of the notch and resonance peak that occur in dynamic stiffness of fluid mounts. Fluid mounts are tuned for specific application so that their notch frequency coincides with the disturbance frequency, by selecting the proper parameters for the mount. Additionally, the mount parameters are selected such that the notch remains as deep (close to zero) as possible and the resonance peak is kept as short as possible. The notch depth and resonance peak present opposing requirements for the selection of mount parameters in the sense that lowering one will result in increasing the other. Using a bond graph model, this study will evaluate the effect of various parameters on the mount notch depth and resonance peak height characteristics. The results show that different parameters can have a varying effect on the notch frequency and depth, as well as the resonance frequency and peak height. The results of the study are extended by examining the effectiveness of two different optimization methods—namely, the Enhanced Genetic Algorithm (EGA) and Sequential Quadratic Programming (SQP)—for selecting the combination of parameters that can yield the deepest notch and shortest resonance peak. Using two different design cases, the study shows that SQP exhibits much more sensitivity to the initial conditions that are selected for the mount parameters than EGA. Both methods, however, are able to converge to an optimal solution within the constraints that are selected for the parameters. For both cases, EGA is able to converge to the set of parameters that provide a deep notch and a short resonance peak.