PREDICTION OF TWO-PHASE FLOW BOILING CHARACTERESTICS IN MICROCHANNELS HEAT SINK BY ARTIFICIAL NEURAL NETWORK

The current study investigated flow boiling heat transfer, pressure drop in a copper multi-parallel microchannels heat sink using R134a as a working fluid. The evaporator consisted of 25 microchannels with dimensions of 300 µm wide, 700 µm deep and 209 µm separating wall thickness. It was made of oxygen-free copper by CNC machining and was 20 mm long and 15 mm wide and hydraulic diameter of 420 μm. Experimental operating conditions spanned the following ranges: wall heat flux (5–120) kW/m2, mass flux 50–300 kg/m2s, and system pressure 8.5–12.5 bar. The heat transfer coefficient increases with heat flux and system pressure but there is insignificant mass flux. This could be interpreted as a nucleate boiling dominant mechanism. The measured two-phase flow pressure drop increases with increasing heat flux and mass flux but decreases with increasing system pressure. The effect of system pressure depends on mass flux, therefore. no pressure effect was found at low mass flux while the heat transfer coefficient increased with pressure at the high mass flux values. Pressure drop was investigated as a variation of heat flux. Simulation with Artificial Neural Network (ANN) was performed to predict heat transfer coefficient and pressure drop using MATLAB- version R2014a software, at mass flux G (75, 125, 175, 225, 275) kg/m2.s and pressure Ps (8.5, 10.5, 12.5) bar. The predicted results were compared with the experimental data and showed good agreement.