Analytical modeling and sensitivity analysis of the temperature distribution in the planar scanning induction heating based on 2D moving heat source

摘要

This paper presents an analytical modeling method of the temperature evolution in the planar scanning induction heating, which can be applied into various heat treatment processes. Due to the uneven distribution and hard control of the electromagnetic fields in the planar induction coils, it is difficult to achieve the precisely prediction and control of the temperature evolution. In view of the low computational efficiency and the complexity in the establishment of the general finite element numerical simulation models, in this work, analytical models of this planar scanning induction heating enhanced by the magnetic flux concentrator are established by using the moving points and line heat source. The magnetic field and the eddy power density in the workpiece generated by the induction coils are calculated by Maxwell equations and then can be applied into the analytical model of the temperature evolution using the moving points and line heat source. Several sets of the corresponding finite element simulation tests and experimental tests are conducted to validate the analytical calculation results. In addition, sensitivity analysis of the main factors influencing the temperature evolution is conducted. By comparison, it is investigated that the final temperature in the surface of the heat workpiece by the analytical model in this work has a good match with the finite element simulation and the experimental results and the errors are reasonable and acceptable considering the inevitable factors. It is also obviously that the computing time of the analytical model is much less than the finite element simulation model. Thus, it is believed that the analytical model established in this paper can be used to predict the temperature evolution in the planar scanning induction heating and extends this heating method to a broader application, which has less computational time and experimental complexity.