Compared to continuous abrasive jet and high-pressure water jet, pulsed abrasive water jet can intermittently generate high water hammer pressure and is commonly used in applications such as coal mining and petroleum engineering for rock-breaking purposes. Pulse frequency is one of the key factors affecting the evolution of abrasive acceleration and jet impact energy, thereby influencing the system's energy transfer efficiency and cutting ability. The equation of motion for abrasive acceleration is established based on the two-phase flow theory, and the variation law of abrasive velocity with pulse frequency is solved. Fluent-EDEM (Extended Discrete Element Method) was used to simulate the abrasive water jet flow field structure and abrasive acceleration process under different pulse frequency conditions, and the intrinsic influence mechanism of pulse frequency on impact energy was clarified. Combined with rock-breaking experiments using pulsed abrasive water jets, the following conclusions are drawn: as the pulse frequency increases, the inertia of the abrasive makes it unable to respond in a timely manner to the high pulsation frequency of water, resulting in a reduction in the exchange rate of intermittent energy and a gradual decrease in the maximum velocity of the abrasive. This trend is more pronounced at a higher pulse frequency. Within the same time period, the cumulative impact energy of the pulsed abrasive water jet decreases first and then increases with the increase in pulse frequency, indicating the existence of an optimal impact frequency that maximizes the cumulative impact energy of the pulsed abrasive water jet. Under fixed jet pressure conditions and the same erosion time, the pulsed abrasive water jet with a frequency of 30 Hz exhibits the best rock-breaking capability.

With the continuous increase in coal mining depth, gas-related disasters have become a critical constraint for the safe and efficient extraction of coal resources. To address prominent issues such as the lack of scientific evaluation in extraction design, insufficient dynamic monitoring during the process, and delayed effectiveness assessment, this study establishes a full-cycle evaluation framework for gas extraction, encompassing the entire process from design and construction to implementation. A Bayesian optimization-based Random Forest Regression model (BO-RFR) is proposed for the preliminary evaluation of extraction schemes, while a reduced-order prediction model integrating deep neural networks and convolutional autoencoders (DNN-CAE) is developed to enable dynamic prediction and reconstruction of residual gas pressure fields. Field tests demonstrated that the BO-RFR model achieved a residual gas pressure prediction error of less than 0.02 MPa, while the DNN-CAE dynamic evaluation model yielded an average mean squared error (MSE) of 2.73 × 10^–5 and mean absolute error (MAE) of 0.00493, confirming the high accuracy and reliability of the proposed method in engineering applications. The results of this study can provide scientific decision-making support for coal mine gas control, enabling precise control and intelligent management of the extraction process. This holds significant practical value for enhancing the safety of coal mine production.

There has been extensive research on the partial differential equations governing the theory of gas flow in coal mines. However, the traditional Proper Orthogonal Decomposition–Radial Basis Function (POD-RBF) reduced-order algorithm requires significant computational resources and is inefficient when calculating high-dimensional data for coal mine gas pressure fields. To achieve the rapid computation of gas extraction pressure fields, this paper proposes a model reduction method based on deep neural networks (DNNs) and convolutional autoencoders (CAEs). The CAE is used to compress and reconstruct full-order numerical solutions for coal mine gas extraction, while the DNN is employed to establish the nonlinear mapping between the physical parameters of gas extraction and the latent space parameters of the reduced-order model. The DNN-CAE model is applied to the reduced-order modeling of gas extraction flow–solid coupling mathematical models in coal mines. A full-order model pressure field numerical dataset for gas extraction was constructed, and optimal hyperparameters for the pressure field reconstruction model and latent space parameter prediction model were determined through hyperparameter testing. The performance of the DNN-CAE model order reduction algorithm was compared to the POD-RBF model order reduction algorithm. The results indicate that the DNN-CAE method has certain advantages over the traditional POD-RBF method in terms of pressure field reconstruction accuracy, overall structure retention, extremum capture, and computational efficiency.