The invention belongs to the field of thermal power
generating unit cold end optimization, particularly relates to an
air cooling island finned tube temperature field prediction method based on a
physical information neural network, and aims to realize accurate, efficient and robust prediction of an
air cooling island finned tube temperature field. The method comprises the steps that operation data are collected and preprocessed based on a direct
air cooling unit condenser sensor, and a
data set is obtained; according to a heat conduction and fluid flow
control equation, a
loss function is constructed in combination with initial and boundary conditions, and a
physical information neural network containing a forward flow region and reverse flow region full-connection neural network and embedded with physical prior constraints is established; then training the network by using a
data set, calculating the required heat exchange area and the actual heat exchange area of the downstream region through a
heat balance equation during training, and selecting to start the downstream region network or combine the two-region network according to the sizes of the required heat exchange area and the actual heat exchange area; and finally, deducing the temperature distribution of a single-row air cooling unit by using the trained network, finely adjusting
model parameters if the prediction deviation exceeds a threshold value, and expanding to a multi-row structure to obtain the temperature field distribution of the whole finned tube of the air cooling island.