Simultaneous Inverse Identification of Transient Thermal Properties and Heat Sources Using Sparse Sensor Information

Abstract

A study is presented herein on the simultaneous inverse identification of transient internal heat generation, transient thermal diffusivity, and constant convection coefficients. The formulations of the direct and inverse problems are presented in the context of finite-element analysis and nonlinear optimization, respectively. A real-coded genetic algorithm was used to solve the inverse problem because of the global convergence properties of this optimization technique. It was found through numerical studies that heat generation and thermal conductivity as functions of time can be simultaneously and consistently estimated from sparse sensor information, as long as convection coefficients are known. However, treating convection coefficients as unknown may significantly affect the accuracy of the estimated thermal diffusivity function and to a lesser extent, the accuracy of the heat generation function. The numerical experiments showed, however, that the Biot number can be accurately estimated when the convection coefficients are not well known. These results can have wide and important implications in problems related to monitoring and quality control of structures.