Dynamic characteristics modeling and optimization for hydraulic engine mounts based on deep neural network coupled with genetic algorithm.

There is a coupling effect between the liquid and the solid in the hydraulic engine mount (HEM). The accurate estimation and optimization of the dynamic characteristics including dynamic stiffness and lag angle for HEM in the frequency domain are still intractable problems. To this end, a novel model of a deep neural network (DNN)-based on dynamic modeling method is developed by using dataset to estimate the dynamic characteristics, and coupled with a genetic algorithm (GA) for the optimization design. Here, the dataset can be divided into two parts. One part is the input of DNN model and contains feature parameters from simulation; other part is the output of DNN model and composed of dynamic stiffness and lag angle from experiment. They are applied to train, test and validate the DNN model. Besides, the conventional model based on the lumped parameter is also presented to achieve the dynamic characteristics and used for comparison. The performed experiments demonstrate that the estimation accuracy of the DNN model is higher than that of the lumped parameter model. Finally, an optimal design method for the lag angle corresponding to frequency is proposed by combining the DNN model and the GA under the prescribed cost function and constraint conditions. The optimization results are approximately close to the desired values and verify the effectiveness of the proposed method which can improve the isolation performance of HEM. • A novel model of a deep neural network (DNN)-based on dynamic modeling method is developed by using dataset to estimate the dynamic characteristics for hydraulic engine mount (HEM). • A unique data driven-based approach by integrating genetic algorithms into DNN for optimizing HEM is presented. • The integration algorithm allows for fast and effective optimization of dynamic characteristics, providing a practical solution for real-world industrial applications. [ABSTRACT FROM AUTHOR]