爆炸与冲击2026，Vol.46Issue(9)：22-33,12.DOI:10.11883/bzycj-2025-0218
基于深度学习算法预测燃爆的临界淬熄直径
Prediction of critical quenching diameter for deflagration and detonation based on deep learning algorithms
摘要
Abstract
The research addresses the safety imperative of preventing flammable gas explosions in enclosed pipelines by establishing a predictive model for the critical quenching diameter within porous media flame arresters.A novel predictive framework was developed based on a comprehensive nine-dimensional feature space incorporating gas composition parameters(e.g.,hydrogen equivalence ratio),pipeline geometry dimensions(length-to-diameter ratio),initial thermodynamic conditions(pressure),and porous medium structural characteristics(thickness,material thermal conductivity).A systematic investigation was conducted to identify the optimal hyperparameter configurations for both convolutional neural network(CNN)and Transformer architectures.Rigorous validation demonstrated that the Transformer model exhibits statistically significant superiority over the CNN model across all key performance metrics.Specifically,the model achieved a δMAE(mean absolute error,MAE)of 0.068,a δMSE(mean squared error,MSE)of 0.008,and an coefficient of determination R2 of 0.928.The performance notably surpassed the CNN results(δMAE=0.079,δMSE=0.012,R2=0.906).Beyond evaluation indicators,detailed error distribution analysis confirmed the Transformer's enhanced predictive accuracy and reduced susceptibility to outliers.The superior performance is attributed to the Transformer's intrinsic self-attention mechanism,which excels at dynamically identifying and weighting critical interdependencies among the diverse input features governing the complex quenching process.The capability enables more precise capture of the nonlinear phenomena defining the quenching limit.Furthermore,robustness testing involving diverse data normalization schemes revealed that the Transformer model exhibits greater stability.This resilience stems from its inherent layer normalization mechanism,which effectively decouples feature dependencies and mitigates sensitivity to input scaling variations.Consequently,the Transformer architecture is established as the definitive optimal model for this critical safety prediction task.Its significant advantage lies in the minimal data preprocessing required before deployment,a feature that greatly enhances its practical utility.The model provides robust quantitative decision-making support essential for formulating effective gas explosion mitigation strategies and optimizing the safety design parameters of pipeline flame arresters.By accurately predicting the critical quenching diameter across various scenarios,this work delivers a valuable tool for enhancing inherent safety in industries handling combustible gases within confined pipeline systems.关键词
管道爆炸/深度学习/临界淬熄直径预测/阻火器Key words
pipeline explosion/deep learning/critical quenching diameter prediction/flame arrester分类
数理科学引用本文复制引用
聂仲恒,汪丽,高伟,姜海鹏..基于深度学习算法预测燃爆的临界淬熄直径[J].爆炸与冲击,2026,46(9):22-33,12.基金项目
国家重点研发计划(2024YFC3082600) （2024YFC3082600）
国家自然科学基金(52574230) （52574230）
