Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport
A new deep-learning-based surrogate model is developed and applied for predicting dynamic temperature, pressure, gas rate, oil rate, and water rate with different boundary conditions in pipeline flow. The surrogate model is based on the multilayer perceptron (MLP), batch normalization and Parametric...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.979168/full |
| _version_ | 1811205528975572992 |
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| author | Feng Qin Zhenghe Yan Peng Yang Shenglai Tang Hu Huang |
| author_facet | Feng Qin Zhenghe Yan Peng Yang Shenglai Tang Hu Huang |
| author_sort | Feng Qin |
| collection | DOAJ |
| description | A new deep-learning-based surrogate model is developed and applied for predicting dynamic temperature, pressure, gas rate, oil rate, and water rate with different boundary conditions in pipeline flow. The surrogate model is based on the multilayer perceptron (MLP), batch normalization and Parametric Rectified Linear Unit techniques. In training, the loss function for data mismatch is considered to optimize the model parameters with means absolute error (MAE). In addition, we also use the dynamic weights, calculated by the input data value, to increase the contribution of smaller inputs and avoid errors caused by large values eating small values in total loss. Finally, the surrogate model is applied to simulate a complex pipeline flow in the eastern part of the South China Sea. We use flow and pressure boundary as the input data in the numerical experiment. A total of 215690 high-fidelity training simulations are performed in the offline stage with commercial software LeadFlow, in which 172552 simulation runs are used for training the surrogate model, which takes about 240 min on an RTX2060 graphics processing unit. Then the trained model is used to provide pipeline flow forecasts under various boundary conduction. As a result, it is consistent with those obtained from the high-fidelity simulations (e.g., the media of relative error for temperature is 0.56%, pressure is 0.79%, the gas rate is 1.02%, and oil rate is 1.85%, and water is 0.80%, respectively). The online computations from our surrogate model, about 0.008 s per run, achieve speedups of over 1,250 relative to the high-fidelity simulations, about 10 s per run. Overall, this model provides reliable and fast predictions of the dynamic flow along the pipeline. |
| first_indexed | 2024-04-12T03:33:12Z |
| format | Article |
| id | doaj.art-6452fba5eda44aaa8fb2ca18776c5473 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-12T03:33:12Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-6452fba5eda44aaa8fb2ca18776c54732022-12-22T03:49:30ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-09-011010.3389/fenrg.2022.979168979168Deep-learning-based surrogate model for fast and accurate simulation in pipeline transportFeng QinZhenghe YanPeng YangShenglai TangHu HuangA new deep-learning-based surrogate model is developed and applied for predicting dynamic temperature, pressure, gas rate, oil rate, and water rate with different boundary conditions in pipeline flow. The surrogate model is based on the multilayer perceptron (MLP), batch normalization and Parametric Rectified Linear Unit techniques. In training, the loss function for data mismatch is considered to optimize the model parameters with means absolute error (MAE). In addition, we also use the dynamic weights, calculated by the input data value, to increase the contribution of smaller inputs and avoid errors caused by large values eating small values in total loss. Finally, the surrogate model is applied to simulate a complex pipeline flow in the eastern part of the South China Sea. We use flow and pressure boundary as the input data in the numerical experiment. A total of 215690 high-fidelity training simulations are performed in the offline stage with commercial software LeadFlow, in which 172552 simulation runs are used for training the surrogate model, which takes about 240 min on an RTX2060 graphics processing unit. Then the trained model is used to provide pipeline flow forecasts under various boundary conduction. As a result, it is consistent with those obtained from the high-fidelity simulations (e.g., the media of relative error for temperature is 0.56%, pressure is 0.79%, the gas rate is 1.02%, and oil rate is 1.85%, and water is 0.80%, respectively). The online computations from our surrogate model, about 0.008 s per run, achieve speedups of over 1,250 relative to the high-fidelity simulations, about 10 s per run. Overall, this model provides reliable and fast predictions of the dynamic flow along the pipeline.https://www.frontiersin.org/articles/10.3389/fenrg.2022.979168/fulldeep learningmultilayer perceptronsurrogate modelpipeline simulationdynamic weights |
| spellingShingle | Feng Qin Zhenghe Yan Peng Yang Shenglai Tang Hu Huang Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport Frontiers in Energy Research deep learning multilayer perceptron surrogate model pipeline simulation dynamic weights |
| title | Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport |
| title_full | Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport |
| title_fullStr | Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport |
| title_full_unstemmed | Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport |
| title_short | Deep-learning-based surrogate model for fast and accurate simulation in pipeline transport |
| title_sort | deep learning based surrogate model for fast and accurate simulation in pipeline transport |
| topic | deep learning multilayer perceptron surrogate model pipeline simulation dynamic weights |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.979168/full |
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