Prediction of critical quenching diameter for deflagration and detonation based on deep learning algorithms

NIE Zhongheng; WANG Li; GAO Wei; JIANG Haipeng

doi:10.11883/bzycj-2025-0218

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NIE Zhongheng, WANG Li, GAO Wei, JIANG Haipeng. Prediction of critical quenching diameter for deflagration and detonation based on deep learning algorithms[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0218

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NIE Zhongheng, WANG Li, GAO Wei, JIANG Haipeng. Prediction of critical quenching diameter for deflagration and detonation based on deep learning algorithms[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0218

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Prediction of critical quenching diameter for deflagration and detonation based on deep learning algorithms

doi: 10.11883/bzycj-2025-0218

NIE Zhongheng^{1, 2
,},
WANG Li²,
GAO Wei¹,
JIANG Haipeng^{1
,
,}

1.
Dalian University of Technology, Dalian, Liaoning Province, 116024, China
2.
Beijing Institute of Aerospace Testing Technology, Beijing 100074, China

Received Date: 2025-07-14
Rev Recd Date: 2026-03-28

Available Online: 2026-04-09

Abstract

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 δ_MAE (mean absolute error, MAE) of 0.068, a δ_MSE (mean squared error, MSE) of 0.008, and an coefficient of determination R² of 0.928. The performance notably surpassed the CNN results (δ_MAE=0.079, δ_MSE=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.
- Pipeline explosion,
- Deep learning,
- Critical quenching diameter prediction,
- Flame arrester

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References(37)

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