遗传神经网络对水平通道流动沸腾传热系数的预测

分别采用3层反向传播神经网络(BPN)和遗传神经网络(GNN)预测从常规通道到微通道尺度范围内的管内流动沸腾传热系数,GNN的精度优于BPN的精度(均方根误差分别为17.16%和20.50%)。输入参数为含气率、质量流密度、热流密度、管径和物性,输出参数为传热系数。基于GNN预测结果,进行了参数趋势分析。对常规通道,传热系数随压力的增大而增大;对微通道,低压时传热系数受压力影响很小,高压、低含气率时,传热系数随压力的增大而增大,高压、高含气率时,传热系数随压力的增大而减小。传热系数随质量流密度、热流密度的增大而增大。随含气率的增大,传热系数先增大后减小;微通道发生烧干时的含气率较低。传热系数随管径的减小而增大;管径越小,越易发生烧干。

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.