Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data

Guihua Zhang, Jiayue Liu, Yuxin Wu () and Guangxi Yue
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Guihua Zhang: Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
Jiayue Liu: Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
Yuxin Wu: Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
Guangxi Yue: Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China

Energies, 2025, vol. 18, issue 13, 1-22

Abstract: 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 β 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.

Keywords: probability density function; machine learning; flamelet generated manifold; data fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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