Heat transfer and optimization studies on layered porous stackings under an imposed pressure drop

This work reports the results of a study to augment the performance of layered porous heat sinks under forced or mixed convection. A two dimensional computational domain is used in the study which involves a rectangular channel consisting of a layered porous stacking. The temperature and flow fields are obtained for different configurations and inlet velocities with the Local Thermal Equilibrium (LTE) model using the commercially available software Fluent 14.0. The solver was validated with in-house experiments and the predictions from the solver were found to be in good agreement with the experimental results. The data obtained from the numerical experiments were used to train a BRANN (Bayesian-regularized artificial neural network). This, in turn, is used to drive a genetic algorithm (GA) to determine the optimal porosity distribution across the layers and the inlet velocity, maximizing the heat transfer with the simultaneous consideration of minimizing the pressure drop. The study reveals that factoring in the pressure drop during design results in a deviation from the optimal configuration obtained by minimizing only the hot-spot temperature. The optimal configuration has been determined and the benefit of an appropriate porosity distribution on the thermal performance has been investigated, giving qualitative insights into the performance of layered porous media.