Abstract

A rapid prediction framework for multi-physics field distributions in coal mine airways of variable lengths is presented. The framework integrates a Computational Fluid Dynamics model, a Proper Orthogonal Decomposition model, and machine learning techniques. The study first obtains multi-physics field distributions of temperature, velocity, species mass fraction, etc., in mining airways using CFD simulations under various operating parameters. It then constructs a POD model to decompose the high-dimensional raw snapshot data into mean field and pulsation field components, performing singular value decomposition on the pulsation field to obtain POD spatial modes and corresponding POD coefficients. Machine learning algorithms, including GA-BPNN and Bayes-XGBoost, are employed to construct predictive models of the POD coefficients. The results show that after fitting the relationship between operating parameters and POD coefficients, the multi-physics field distribution within the training parameter range can be rapidly predicted. When the cumulative energy contribution of POD modes exceeds 0.99 of the total energy, the Bayes-XGBoost model achieves minimum R2 values of 0.9448, 0.9999, and 0.9996 for velocity, temperature, and oxygen mass fraction predictions, respectively. This work provides a practical engineering solution for real-time prediction of multi-physical fields in variable-length mine airways, and achieves fast and accurate prediction within the training parameter range.

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