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Optimal design of magnetorheological damper with multiple axial fluid flow channels using BP neural network and particle swarm optimization methodologies

Guoliang Hu, Haonan Qi, Miao Chen, Lifan Yu, Gang Li, Han Zheng

2021International Journal of Applied Electromagnetics and Mechanics17 citationsDOI

Abstract

In this paper, a magnetorheological (MR) damper with multiple axial fluid flow channels is developed to solve the conflicts between limitation of size dimension and improvement of damping performance. By setting symmetrical excitation coils at both ends of the MR damper, the effective fluid flow channels of the proposed MR damper are significantly lengthened. In order to investigate the distributions of magnetic flux lines and magnetic flux density of the MR damper, the finite element model of the MR damper is established by using ANSYS software. Moreover, an optimization method combining BP neural network and particle swarm optimization (PSO) is proposed to improve the magnetic field utilization of the designed damper, and the damping performances of initial and optimal MR dampers are also experimentally tested. The test results show that the output damping force of initial and optimal MR dampers is 3.13 kN and 5.98 kN respectively under the applied current of 1.8 A, increasing by 91.1%, and the dynamic adjustable range is 11.5 and 16.1 respectively, increasing by 40.0%. It is found that the damping performance of the proposed MR damper is significantly improved.

Topics & Concepts

Magnetorheological fluidDamperParticle swarm optimizationMagnetorheological damperControl theory (sociology)Artificial neural networkStructural engineeringFlow (mathematics)Finite element methodEngineeringMechanicsComputer sciencePhysicsMachine learningControl (management)Artificial intelligenceVibration Control and Rheological FluidsHydraulic and Pneumatic SystemsStructural Engineering and Vibration Analysis
Optimal design of magnetorheological damper with multiple axial fluid flow channels using BP neural network and particle swarm optimization methodologies | Litcius