Hybrid modeling of the nonlinear behaviors for magnetorheological energy absorber. (1st April 2023)

• Record Type: Journal Article
• Authors: Shou, Mengjie; Liao, Changrong; Yang, Ping-an; Huang, Xin; Wu, Decheng; Zhou, Zhihao; Luo, Jiufei; Li, Rui
• Is Part Of: International journal of mechanical sciences. Volume 243(2023)
• Publication Date: 2023-04-01
• Subjects: Magnetorheological energy absorber -- Support vector regression -- Adaptive differential evolution -- Impact loading; Mechanical engineering -- Periodicals; Génie mécanique -- Périodiques; Mechanical engineering; Maschinenbau; Mechanik; Zeitschrift; Periodicals; 621.05
• DOI: 10.1016/j.ijmecsci.2022.107820
• Languages: English
• ISSNs: 0020-7403

Abstract

Abstract: Precise forecast of strong nonlinear behaviors of magnetorheological energy absorber (MREA) plays a significant role in its semi-active control and engineering applications. This study proposes a hybrid model based on the support vector regression (SVR) and adaptive differential evolution (i.e., JADE) algorithm for MREA, called the JADE-SVR model. The JADE algorithm is employed to find the optimal hyperparameters of SVR to improve prediction accuracy and generalization ability. The JADE-SVR model is developed using the training data randomly partitioned from an impact test database, and then applied to predict the nonlinear behaviors of MREA in the test set, including force–time, force–displacement and force–velocity characteristics. It is demonstrated that the JADE-SVR model is capable of forecasting strong nonlinear behaviors of MREA under various impact conditions. Moreover, the JADE-SVR model is compared with other SVR-based models to validate its prediction accuracy and convergence rate further. In addition to four statistical indicators, a novel desirability function is used to assess the overall performance of these models. The results show that the JADE-SVR model has the optimal values for all the performance indices, and the largest overall desirability of 0.6274. Therefore, it can be concluded that the JADE-SVR model is the best one for forecasting the dynamic behaviors of MREA under high-impact loadings. Graphical abstract: Highlights: A new SVR-based model is proposed for forecasting strong nonlinear behaviors of MREA. The hyperparameters of SVR are optimized by using the adaptive differential evolution algorithm. A comprehensive comparison of the four SVR-based models is conducted using a novel desirability function. The proposed JADE-SVR model has the best prediction performance compared with other models.