Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration
This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled wi...
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2025-07-01
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| Online Access: | https://www.mdpi.com/1996-1073/18/14/3783 |
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| author | Gökhan Deveci Özgün Yücel Ali Bahadır Olcay |
| author_facet | Gökhan Deveci Özgün Yücel Ali Bahadır Olcay |
| author_sort | Gökhan Deveci |
| collection | DOAJ |
| description | This study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability. |
| format | Article |
| id | doaj-art-3c3d5b25fd5747ed8fa1c2e227dc5513 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-3c3d5b25fd5747ed8fa1c2e227dc55132025-08-20T03:58:30ZengMDPI AGEnergies1996-10732025-07-011814378310.3390/en18143783Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame ConfigurationGökhan Deveci0Özgün Yücel1Ali Bahadır Olcay2Department of Mechanical Engineering, Yeditepe University, Istanbul 34755, TurkeyDepartment of Chemical Engineering, Gebze Technical University, Kocaeli 41400, TurkeyPadnos College of Engineering, Grand Valley State University, Grand Rapids, MI 49504, USAThis study investigates the application of deep learning (DL) techniques for predicting temperature fields in the SM1 swirl-stabilized turbulent non-premixed flame. Two distinct DL approaches were developed using a comprehensive CFD database generated via the steady laminar flamelet model coupled with the SST k-ω turbulence model. The first approach employs a fully connected dense neural network to directly map scalar input parameters—fuel velocity, swirl ratio, and equivalence ratio—to high-resolution temperature contour images. In addition, a comparison was made with different deep learning networks, namely Res-Net, EfficientNetB0, and Inception Net V3, to better understand the performance of the model. In the first approach, the results of the Inception V3 model and the developed Dense Model were found to be better than Res-Net and Efficient Net. At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. The training and validation processes for both models were conducted using 80% of the CFD data for training and 20% for testing, which helped assess their ability to generalize new input conditions. In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. The dense network is appropriate for direct parametric analyses, while the image-based U-Net model provides a rapid and scalable option to utilize the cold flow CFD images. This framework can be further refined in future research to estimate more flow factors and tested against experimental measurements for enhanced applicability.https://www.mdpi.com/1996-1073/18/14/3783SM1 flamedeep learningRGB fusionCFDcombustion model |
| spellingShingle | Gökhan Deveci Özgün Yücel Ali Bahadır Olcay Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration Energies SM1 flame deep learning RGB fusion CFD combustion model |
| title | Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration |
| title_full | Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration |
| title_fullStr | Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration |
| title_full_unstemmed | Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration |
| title_short | Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration |
| title_sort | prediction of temperature distribution with deep learning approaches for sm1 flame configuration |
| topic | SM1 flame deep learning RGB fusion CFD combustion model |
| url | https://www.mdpi.com/1996-1073/18/14/3783 |
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