Multi-parameter retrieval in a porous fin using binary-coded genetic algorithm

Abstract

In this paper, the implementation of the binary-coded Genetic Algorithm (GA) for multi-parameter retrieval through inverse analysis is demonstrated. A porous rectangular fin with constant thermo-physical parameters is investigated. The porous fin involves Fourier law of heat conduction along with natural convection and surface radiation phenomena. Due highly nonlinear phenomenon owing to the radiative effect and because of the associated complexity in the gradient evaluation, gradient-free method based on the GA has been used for unknown parameter retrieval. The analysis is done for satisfying a given temperature distribution on the fin surface generated using a well-validated forward solver based on the Runge-Kutta method. It is observed from the simulated experiments that for simultaneous multi-parameter retrieval, the GA yields multiple combinations of unknown parameters satisfying a particular distribution of temperature.