Fast prediction of mine flow field based on convolution neural network

Abstract

Accurately describing the flow state of mine gas is the basis for the prevention and control of mine dust and toxic gases, computational fluid dynamics (CFD) method is often used to solve mine flow field, but CFD method is usually an iterative process with high calculation cost, time-consuming and high memory requirements. In order to realize the fast solution of mine flow field, a prediction model of mine flow field based on convolutional neural network (CNN) is proposed. The geometry flow field simulated by lattice Boltzmann method (LBM) is used as the training data. CNN is used to extract the basic characteristics of the flow field in the training data, establish the mapping relationship between the geometric boundary and the flow field, and quickly predict the mine flow field. The simulation experiment and the particle image velocimetry (PIV) test experiment of the air window flow field were carried out respectively. In LBM simulation experiment, the average values of mean square error (MSE), mean absolute error (MAE), R2, explained variance score (EVC), Pearson correlation coefficient (PCC) and cosine similarity between CNN predicted values and LBM simulated values of 10 groups of test samples are 0.2633, 0.2449, 0.9595, 0.9697, 0.9827, 0.9933, respectively. It shows that the accuracy of CNN flow field prediction model is similar to that of LBM simulation, and the computational speed of CNN model is increased by three orders of magnitude compared with LBM simulation. In PIV test experiment, the predicted values of CNN model are basically consistent with the PIV experiment results, no matter the velocity distribution of a section or the flow trend of the overall flow field. The LBM simulation experiment and PIV test experiment strongly prove the reliability and generalization ability of CNN model for predicting mine flow field.