Abstract
This article proposes an analytical methodology for the optimal design of a magnetorheological (MR) valve constrained in a specific volume. The analytical optimization method is to identify geometric dimensions of the MR valve, and to determine whether the performance of the valve has undergone major improvement. Initially, an enhanced radial MR valve structure with effective annular and radial composite flow paths was designed. After describing the schematic configuration and operating principle of the proposed MR valve, a mathematical model of the pressure drop was derived on the basis of the Bingham model of a MR fluid. Sequentially, the multi-objective optimization problem had been formulated for the constructed approximate model exploiting the NSGA-II algorithm to find the global optimum geometrical dimensions of the enhanced radial MR valve. Meanwhile, influences of the geometrical design variables of the MR valve were analytically investigated by mapping finite element analysis numerical responses with response surface techniques. Lastly, the experimental test rig was setup to explore the pressure drop and dynamic response time of the initial and optimal MR valve, as well as the dynamic performance of the enhanced radial MR valve controlled cylinder system under different excitation conditions. The experimental results revealed that under the applied current of 1.6 A, the pressure drop and power consumption of the optimal MR valve improved significantly with values of 4.46 MPa and 16.84 W, respectively, when compared to 4.03 MPa and 27.65 W of their respective initial values. Additionally, the average response time efficiency improved by 14.29%, with its optimal value being 81 ms and initial value as 94.5 ms. Moreover, the damping force of the optimal MR valve-controlled cylinder system was 4.34 kN, which was 12.44% larger than the initial one of 3.86 kN at the applied current of 1.6 A.
Funder
National Natural Science Foundation of China
Key Project of Natural Science Foundation of Jiangxi Province of China
Subject
Control and Optimization,Control and Systems Engineering
Cited by
5 articles.
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