Abstract
This work deals with the prediction of unknown parameters and identifying feasible materials for satisfying a given temperature distribution in a rectangular fin geometry involving variable thermal conductivity and surface heat transfer coefficient. The unknown parameters which have been estimated in this work are the thermal conductivity, coefficient of variable conductivity and fin dimensions. For achieving the required objective, an inverse problem is solved by minimization of least squares error using a hybrid evolutionary-nonlinear programming algorithm. Due to correlated nature of the unknowns, many feasible combinations of materials have been found with varying dimensions, which is proposed to offer a wide range of flexibility in selecting the fin material. The present study is proposed to be useful in situations where only few discrete temperatures are available, as it helps to find out feasible materials alongwith relevant dimensions which will yield a prescribed temperature distribution.
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