Numerical simulation and optimization with artificial neural network of two-phase nanofluid flow in a circular heatsink with cylindrical pin-fins

Abstract

This paper designs and simulates a circular heatsink (HSK) with a novel geometry. A number of cylindrical pin-fins (PIFs) are placed on the HSK. The flow of alumina/water nanofluid (NF) enters from the middle of the HSK, passes through the PIFs, goes toward the outer curvature, and exits from the HSK. Constant thermal flux is applied at the bottom of the HSK. The variables include the length of PIFs changing from 5 to 20 mm, the distance between PIFs varying from 10 to 15 mm, and the diameter of PIFs changing from 1 to 4 mm. The effect of these variables on the maximum of the HSK, the average temperature (T-AV) of the HSK is examined. Finally, numerical optimization is done on the results using machine learning and artificial intelligence in terms of the minimum HSK temperature. The flow of NF is simulated using a two-phase model and the finite element method (FEM). An increment in the length of the PIFs from 5 to 20 reduces the T-AV by 11.42 K. An increment in the diameter of the PIFs from 1 to 4 reduces the T-AV of the HSK by 5.98 K.