Multi objective optimization of parameters of mining double disk magnetic coupler based on multi field coupling energy and entropy generation analysis

The transmission device of traditional coal mine electromechanical equipment has some defects such as pollution medium, low transmission efficiency, large floor space, high maintenance cost, and short service life, resulting in high failure rate of coal mine electromechanical system, which seriously affects the safe and efficient production of mechanized mining, excavation, lifting, transportation and drainage. The mining double disc magnetic coupler has the advantages of high efficiency speed regulation, strong adaptability and low operation cost. It is a revolutionary energy saving transmission device. Electromagnetic temperature stress coupling energy interaction is the complex problem, which has become one of the hot topics in the field of magnetic coupling. Aiming at the problems of safe operation and efficient transmission of mining double disc magnetic coupler under different working conditions, the multiobjective optimization method based on multifield coupling and entropy generation minimization theory is proposed. Based on the thermodynamic model of mining double disk magnetic coupler, taking the improvement of transmission efficiency and heat dissipation as the optimization objective, taking input motor speed, number of heat dissipation fins, length of heat dissipation fins, and air gap length as parameter variables, the multi objective optimization model is established. In order to reduce the test cost, the heat dissipation and entropy production corresponding to the combination of parameters and variables are obtained by the finite element method, and its effectiveness is verified by physical experiments. Based on the simulation data of four factors and three levels, the improved Gaussian regression model is used to predict heat dissipation and entropy production. The performance of the model is compared with the other two models, which proves the advantages of the improved model in accuracy and response time. The Pareto front of parameter variables is optimized by using a multiobjective particle swarm optimization algorithm with the goal of minimum entropy production and maximum heat dissipation. The physical test results verify the effectiveness of the algorithm.