Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data
The Flamelet Generated Manifold (FGM) method is widely employed in turbulent combustion simulations due to its high accuracy and computational efficiency. However, the model’s ability to capture turbulent combustion interactions is limited by the shape of the presumed probability density function (P...
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/13/3546 |
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| author | Guihua Zhang Jiayue Liu Yuxin Wu Guangxi Yue |
| author_facet | Guihua Zhang Jiayue Liu Yuxin Wu Guangxi Yue |
| author_sort | Guihua Zhang |
| collection | DOAJ |
| description | The Flamelet Generated Manifold (FGM) method is widely employed in turbulent combustion simulations due to its high accuracy and computational efficiency. However, the model’s ability to capture turbulent combustion interactions is limited by the shape of the presumed probability density function (PDF) of the mixture fraction and progress variable. To construct a conditional <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>β</mi></mrow></semantics></math></inline-formula> PDF with better performance, a systematic PDF modeling and analysis framework coupled with machine learning methods based on the sparse experimental data was proposed. A comparative analysis was conducted for five machine learning methods across two experimental datasets using this framework. The results demonstrate that the random forest algorithm represents the optimal choice when both training complexity and predictive performance are comprehensively considered. To expand the model’s applicable range, a data fusion strategy was applied in different machine learning methods. The effectiveness of data fusion is demonstrated by comparative analysis between single-dataset and fused-dataset models. The analysis of convex hull in low-dimensional space reveals the fundamental mechanism of data fusion in the FGM-PDF method, which is significantly important to construct a data-driven PDF model in sparse-data scenarios with much better performance. |
| format | Article |
| id | doaj-art-62b28ff5fb974df8901dc5218e298fe0 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-62b28ff5fb974df8901dc5218e298fe02025-08-20T03:50:21ZengMDPI AGEnergies1996-10732025-07-011813354610.3390/en18133546Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame DataGuihua Zhang0Jiayue Liu1Yuxin Wu2Guangxi Yue3Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, ChinaKey Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, ChinaKey Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, ChinaKey Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, ChinaThe Flamelet Generated Manifold (FGM) method is widely employed in turbulent combustion simulations due to its high accuracy and computational efficiency. However, the model’s ability to capture turbulent combustion interactions is limited by the shape of the presumed probability density function (PDF) of the mixture fraction and progress variable. To construct a conditional <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>β</mi></mrow></semantics></math></inline-formula> PDF with better performance, a systematic PDF modeling and analysis framework coupled with machine learning methods based on the sparse experimental data was proposed. A comparative analysis was conducted for five machine learning methods across two experimental datasets using this framework. The results demonstrate that the random forest algorithm represents the optimal choice when both training complexity and predictive performance are comprehensively considered. To expand the model’s applicable range, a data fusion strategy was applied in different machine learning methods. The effectiveness of data fusion is demonstrated by comparative analysis between single-dataset and fused-dataset models. The analysis of convex hull in low-dimensional space reveals the fundamental mechanism of data fusion in the FGM-PDF method, which is significantly important to construct a data-driven PDF model in sparse-data scenarios with much better performance.https://www.mdpi.com/1996-1073/18/13/3546probability density functionmachine learningflamelet generated manifolddata fusion |
| spellingShingle | Guihua Zhang Jiayue Liu Yuxin Wu Guangxi Yue Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data Energies probability density function machine learning flamelet generated manifold data fusion |
| title | Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data |
| title_full | Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data |
| title_fullStr | Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data |
| title_full_unstemmed | Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data |
| title_short | Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data |
| title_sort | novel data driven pdf modeling in fgm method based on sparse turbulent flame data |
| topic | probability density function machine learning flamelet generated manifold data fusion |
| url | https://www.mdpi.com/1996-1073/18/13/3546 |
| work_keys_str_mv | AT guihuazhang noveldatadrivenpdfmodelinginfgmmethodbasedonsparseturbulentflamedata AT jiayueliu noveldatadrivenpdfmodelinginfgmmethodbasedonsparseturbulentflamedata AT yuxinwu noveldatadrivenpdfmodelinginfgmmethodbasedonsparseturbulentflamedata AT guangxiyue noveldatadrivenpdfmodelinginfgmmethodbasedonsparseturbulentflamedata |