Prediction of critical quenching diameter based on deep learning algorithms

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 the Transformer model's statistically significant superiority over the CNN model across all key performance metrics. Specifically, the model achieved a Mean Absolute Error (MAE) of 0.068, a Mean Squared Error (MSE) of 0.008, and an R² coefficient of determination of 0.928. The performance notably surpassed the CNN results (MAE = 0.079, 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 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 of requiring minimal data preprocessing prior to deployment offers substantial 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 enabling accurate prediction of the critical quenching diameter under varied scenarios, this work delivers a valuable tool for enhancing inherent safety in industries handling combustible gases within confined pipeline systems.