Abstract:
This paper investigates an inverse conductive convective problem working on a hybrid differential evolution nonlinear programming (DE-NLP) algorithm. Thermophysical parameters such as the thermal conductivity and the heat transfer coefficient have been estimated for satisfying a given temperature distribution. The objective function to be minimized is represented by the least squares of error between the randomly-guessed and the exact temperature distributions. The estimations have been found to be in good agreement. Results show that DE-NLP algorithm successfully estimates various possible combinations of thermal conductivity and heat transfer coefficient which satisfy the given temperature distribution.
