Prediction of flow boiling heat transfer coefficient in horizontal channel by genetic neural network

The three-layer back propagation network (BPN) and genetic neural network (GNN) were developed to predict the flow boiling heat transfer coefficient (HTC) in conventional and micro channels. The precision of GNN is higher than that of BPN (with root mean square errors of 17.16% and 20.50%, respectively). The inputs include vapor quality, mass flux, heat flux, diameter and physical properties and the output is HTC. Based on the trained GNN, the influences of input parameters on HTC were analyzed. HTC increases with pressure in conventional channels. The pressure has a negligible effect at low pressure region on HTC for micro channels. However, at high pressure region, HTC increases in low vapor quality region, while decreases in the high vapor quality region with the increase of pressure. HTC increases with the mass flux and heat flux, and HTC initially increases and then decreases as vapor quality increases. HTC increases inversely with the decrease of diameter. Dry-out arises at a lower quality in micro channels than that in conventional channels and more easily occurs in a smaller channel.