Study on Fast Temporal Prediction Method of Flame Propagation Velocity in Methane Gas Deflagration Experiment Based on Neural Network

Xueqi Wang, Boqiao Wang, Kuai Yu, Wenbin Zhu, Jinnan Zhang and Bin Zhang ()
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Xueqi Wang: China Petroleum Safety and Environmental Protection Technology Research Institute, Dalian 116031, China
Boqiao Wang: Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Kuai Yu: China Petroleum Safety and Environmental Protection Technology Research Institute, Dalian 116031, China
Wenbin Zhu: Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Jinnan Zhang: Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Bin Zhang: Marine Engineering College, Dalian Maritime University, Dalian 116026, China

Energies, 2024, vol. 17, issue 18, 1-29

Abstract: To address the challenges of high experimental costs, complexity, and time consumption associated with pre-mixed combustible gas deflagration experiments under semi-open space obstacle conditions, a rapid temporal prediction method for flame propagation velocity based on Ranger-GRU neural networks is proposed. The deflagration experiment data are employed as the training dataset for the neural network, with the coefficient of determination (R 2 ) and mean squared error (MSE) used as evaluation metrics to assess the predictive performance of the network. First, 108 sets of pre-mixed methane gas deflagration experiments were conducted, varying obstacle parameters to investigate methane deflagration mechanisms under different conditions. The experimental results demonstrate that obstacle-to-ignition source distance, obstacle shape, obstacle length, obstacle quantity, and thick and fine wire mesh obstacles all significantly influence flame propagation velocity. Subsequently, the GRU neural network was trained, and different activation functions (Sigmoid, Relu, PReLU) and optimizers (Lookahead, RAdam, Adam, Ranger) were incorporated into the backpropagation updating process of the network. The training results show that the Ranger-GRU neural network based on the PReLU activation function achieves the highest mean R 2 value of 0.96 and the lowest mean MSE value of 7.16759. Therefore, the Ranger-GRU neural network with PReLU activation function can be a viable rapid prediction method for flame propagation velocity in pre-mixed methane gas deflagration experiments under semi-open space obstacle conditions.

Keywords: methane gas deflagration; deflagration experiment; neural network; optimizer; activation function (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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