Optimization of elliptical pin-fin microchannel heat sink based on artificial neural network

Conjugate fluid-solid heat transfer in a pin-fin microchannel heat sink is an effective way to dissipate heat from the heating surface with high heat flux. The introduction of fins increases the heat exchange area and enhances flow turbulence, while it increases the flow resistance at the mean time. The thermal-hydraulic performance of heat sink is affected by fin shape, density and flow parameters. In this paper, contrived numerical simulations of the flow and heat transfer process in elliptical pin-fin microchannel heat sink are carried out, including 2033 cases with different fin sizes, numbers and flow velocities. The simulation results show that the flow velocity and fin transverse width are the main factors affecting heat transfer and fluid flow. Three artificial neural networks are established to predict the average temperature, the temperature non-uniformity of heating surface and the pressure drop of microchannel. The predicted results show that the pump power and heating surface temperature are contradictory objectives. A microchannel with the optimal thermal-hydraulic performance is selected. It has numerous fins which are longer in the flow direction. The empirical correlations for Nusselt number and friction coefficient of the optimal microchannel are proposed.