Estimation of feasible materials and thermal conditions in a trapezoidal fin using genetic algorithm

Abstract

A genetic algorithm based inverse analysis is done to predict unknown parameters in a trapezoidal extended surface for satisfying a given temperature distribution. An inverse method is adopted to estimate six unknowns involving thermal, surface, and geometric parameters, which helps to identify feasible fin materials, necessary dimensions along with other requirements. Various controlling parameters of genetic algorithm along with random measurement errors have been investigated. Fin efficiencies have been also compared. For satisfying a prescribed temperature distribution, this study shows that many feasible materials exist which may satisfy a given temperature profile, which shall be useful in selecting any material from the available choices depending upon the relevant dimensions, convective and surface requirements. This study also shows that fin dimensions along with the coefficient of thermal conductivity influence the temperature distribution more than other parameters. The maximum variation in the efficiency among the predicted parameters is found to be within 9%.