Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders
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 whe...
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2024-11-01
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| author | Tianxuan Hao Lizhen Zhao Yang Du Yiju Tang Fan Li Zehua Wang Xu Li |
| author_facet | Tianxuan Hao Lizhen Zhao Yang Du Yiju Tang Fan Li Zehua Wang Xu Li |
| author_sort | Tianxuan Hao |
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| description | 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. |
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| institution | Kabale University |
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| spelling | doaj-art-cea1d6b7ceb442ee919d8fa3a8d5c4a62024-11-26T18:06:45ZengMDPI AGInformation2078-24892024-11-01151173310.3390/info15110733Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional AutoencodersTianxuan Hao0Lizhen Zhao1Yang Du2Yiju Tang3Fan Li4Zehua Wang5Xu Li6College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaCollege of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaCollege of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaSchool of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan 467036, ChinaCollege of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaCollege of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaCollege of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, ChinaThere 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.https://www.mdpi.com/2078-2489/15/11/733coal minegas extractiongas flow modelreduced-order modeldeep neural networksconvolutional autoencoders |
| spellingShingle | Tianxuan Hao Lizhen Zhao Yang Du Yiju Tang Fan Li Zehua Wang Xu Li Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders Information coal mine gas extraction gas flow model reduced-order model deep neural networks convolutional autoencoders |
| title | Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders |
| title_full | Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders |
| title_fullStr | Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders |
| title_full_unstemmed | Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders |
| title_short | Reduced-Order Model of Coal Seam Gas Extraction Pressure Distribution Based on Deep Neural Networks and Convolutional Autoencoders |
| title_sort | reduced order model of coal seam gas extraction pressure distribution based on deep neural networks and convolutional autoencoders |
| topic | coal mine gas extraction gas flow model reduced-order model deep neural networks convolutional autoencoders |
| url | https://www.mdpi.com/2078-2489/15/11/733 |
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