A multiple model adaptive inverse method for nonlinear heat transfer system with temperature-dependent thermophysical properties

A multiple model adaptive inverse (MMAI) method is proposed to estimate the boundary heat flux distribution of nonlinear heat transfer system with temperature-dependent thermophysical properties. In the temperature space of system, the nonlinear heat transfer system is divided into several linear subspaces, and for each subspace, the linear prediction submodel of the temperature at the measurement point is established. Furthermore, according to the instantaneous matching degree between each prediction submodel and the actual heat transfer system, the prediction submodels then are weighted and synthesized to gain the global prediction model of the nonlinear heat transfer system. Finally, based on the global prediction model, the boundary heat fluxes are simultaneously estimated through rolling optimization. Numerical experiments are performed to study the effects of system nonlinearity and measurement errors on the inversion results. Comparisons with the existing dynamic matrix control inverse method and the adaptive sequential function specification method are also conducted, and they all show the validity of the inverse method established in this paper.