Prediction of Heat Generation in a Porous Fin from Surface Temperature

Abstract

The present paper demonstrates a nondestructive inverse methodology aimed at estimating the volumetric rate of internal heat generation within a porous fin using the golden section search method (GSSM) algorithm. Distinct from previously reported studies, the present study considers the nonlinear dependency of the heat generation on the temperature in the inverse solution in addition to accounting for all temperature-dependent heat dissipation modes. Using simulated local temperature distribution, an inverse problem is solved using an implicit fourth-order Runge–Kutta procedure in conjunction with the GSSM algorithm. A case study of Inconel is demonstrated with numerical examples. The influence of random measurement errors is also studied. For uncontaminated temperature data, an exact estimation of the internal heat generation rate is accomplished, whereas, even with noisy data, satisfactory estimation of the heat generation rate is also realized as revealed by the temperature reconstructions.