Time Series-based Particle Swarm Optimization Algorithm for Predicting Billet Temperature Profiles
The one-dimensional model of the billet in the heating furnace is taken as the research object.The forward problem of transient heat conduction based on the finite difference method and the particle swarm optimization algorithm based on the time sequence are used to con-struct the mathematical model of the inverse problem of one-dimensional transient heat conduction.First,the measured temperature at the dis-crete point of the billet is taken as the given condition,and the mean square deviation between the measured temperature and the temperature calculated by the forward heat conduction problem is taken as the objective function.The heat flux at the boundary is retrieved by particle swarm optimization algorithm.Secondly,the inverse heat flux value is brought into the forward heat conduction problem to obtain the values of simulated temperature at the discrete points.Finally,the general algorithm program is coded by Python.The results show that the simulated temperature at the discrete points is in good agreement with the measured values,which indicates that the inverse heat conduction problem model constructed can better reverse the boundary conditions of the billet surface.The billet temperature profiles can still be predicted actually when random errors exist.Later,the model can be further extended to the three-dimensional billet temperature prediction system in practical projects.
particle swarm optimizationinverse heat conduction problemheat transfer coefficientbillet temperature
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维普
