Lead-bismuth reactor subchannel program optimization neural network training dataset

In the training set file: the working condition parameter, i.e., inlet mass flow rate M, and the exit channel temperature calculated for the subchannels, totaling 43 variables, are used as inputs to the neural network (columns 1-43 in the file). The optimized target value of the exit temperature for each subchannel, i.e., the CFD-calculated exit channel temperature, totaling 42 output variables (42 columns in the back of the file). A total of 30 rows of data representing 30 training groups.In the test set file: the working condition parameter, i.e., inlet mass flow rate M, and the subchannel-calculated outlet channel temperature, totaling 43 variables, are used as inputs to the neural network (columns 1-43 in the file). The exact value of the exit temperature of each subchannel, i.e., the CFD-calculated exit channel temperature, totaling 42 output variables (42 columns in the back of the file). A total of 5 rows of data representing 5 test groups.