Machine learning-based prediction of species mass fraction and flame characteristics in partially premixed turbulent jet flame
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912357868896256 |
|---|---|
| author | Shateri, Amirali Yang, Zhiyin Xie, Jianfei |
| author_facet | Shateri, Amirali Yang, Zhiyin Xie, Jianfei |
| contents | This study explores the integration of machine learning (ML) techniques with large eddy simulation (LES) for predicting species mass fraction and flame characteristics in partially premixed turbulent jet flames. The LES simulations, conducted using STAR-CCM+ software, employed the Flamelet Generated Manifold (FGM) approach to effectively capture the interactions between the turbulence and chemical reactions, providing high-fidelity data on flame behaviour and pollutant formation. The simulation was based on the Sandia Flame D specification, utilizing a detailed mesh to accurately represent flow features and flame dynamics. To enhance real-time prediction capabilities, three ML models, Neural Networks (NN), Linear Regression (LR), and Decision Tree Regression (DTR), were trained on the LES data. Comparative analysis using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Pearson Coefficient (PC), and R-squared (R2) identified the NN model as the most effective one. The NN model demonstrated high accuracy in predicting species mass fractions and flame patterns, significantly outperforming traditional LES solvers in terms of computational efficiency. The study also highlighted the considerable computational speedup achieved by the NN model, making it approximately 17.25 times faster than traditional LES solvers. Despite some limitations, such as handling large dataset fluctuations, the ML models have shown promise for future applications in combustion simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning-based prediction of species mass fraction and flame characteristics in partially premixed turbulent jet flame Shateri, Amirali Yang, Zhiyin Xie, Jianfei Fluid Dynamics This study explores the integration of machine learning (ML) techniques with large eddy simulation (LES) for predicting species mass fraction and flame characteristics in partially premixed turbulent jet flames. The LES simulations, conducted using STAR-CCM+ software, employed the Flamelet Generated Manifold (FGM) approach to effectively capture the interactions between the turbulence and chemical reactions, providing high-fidelity data on flame behaviour and pollutant formation. The simulation was based on the Sandia Flame D specification, utilizing a detailed mesh to accurately represent flow features and flame dynamics. To enhance real-time prediction capabilities, three ML models, Neural Networks (NN), Linear Regression (LR), and Decision Tree Regression (DTR), were trained on the LES data. Comparative analysis using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Pearson Coefficient (PC), and R-squared (R2) identified the NN model as the most effective one. The NN model demonstrated high accuracy in predicting species mass fractions and flame patterns, significantly outperforming traditional LES solvers in terms of computational efficiency. The study also highlighted the considerable computational speedup achieved by the NN model, making it approximately 17.25 times faster than traditional LES solvers. Despite some limitations, such as handling large dataset fluctuations, the ML models have shown promise for future applications in combustion simulations. |
| title | Machine learning-based prediction of species mass fraction and flame characteristics in partially premixed turbulent jet flame |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2505.01201 |