A novel deep learning approach for investigating liquid fuel injection in combustion system
Abstract The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the relat...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00248-2 |
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| author | Syed Azeem Inam Abdullah Ayub Khan Noor Ahmed Tehseen Mazhar Tariq Shahzad Sunawar Khan Mamoon M. Saeed Habib Hamam |
| author_facet | Syed Azeem Inam Abdullah Ayub Khan Noor Ahmed Tehseen Mazhar Tariq Shahzad Sunawar Khan Mamoon M. Saeed Habib Hamam |
| author_sort | Syed Azeem Inam |
| collection | DOAJ |
| description | Abstract The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. |
| format | Article |
| id | doaj-art-fd09d02b0b7d4b028ef8cf0b865a7ce8 |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-fd09d02b0b7d4b028ef8cf0b865a7ce82025-08-20T03:07:43ZengSpringerDiscover Artificial Intelligence2731-08092025-04-015111410.1007/s44163-025-00248-2A novel deep learning approach for investigating liquid fuel injection in combustion systemSyed Azeem Inam0Abdullah Ayub Khan1Noor Ahmed2Tehseen Mazhar3Tariq Shahzad4Sunawar Khan5Mamoon M. Saeed6Habib Hamam7Department of Artificial Intelligence and Mathematical Science, Sindh Madressatul Islam UniversityDepartment of Computer Science and Information Technology, Shaheed Benazir Bhutto UniversityDepartment of Computer Science, Shaheed Zulfiqar Ali Bhutto Institute of Science and TechnologyDepartment of Computer Science, School Education Department, Government of PunjabDepartment of Computer Engineering, COMSATS University Islamabad, Sahiwal CampusDepartment of Software Engineering, Islamia University of BahawalpurDepartment of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences (UMS)Faculty of Engineering, Uni de MonctonAbstract The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system.https://doi.org/10.1007/s44163-025-00248-2Deep learningFully Connected Neural NetworkExtra Tree RegressorCryogenic propellantsR-squaredLiquid fuel injection |
| spellingShingle | Syed Azeem Inam Abdullah Ayub Khan Noor Ahmed Tehseen Mazhar Tariq Shahzad Sunawar Khan Mamoon M. Saeed Habib Hamam A novel deep learning approach for investigating liquid fuel injection in combustion system Discover Artificial Intelligence Deep learning Fully Connected Neural Network Extra Tree Regressor Cryogenic propellants R-squared Liquid fuel injection |
| title | A novel deep learning approach for investigating liquid fuel injection in combustion system |
| title_full | A novel deep learning approach for investigating liquid fuel injection in combustion system |
| title_fullStr | A novel deep learning approach for investigating liquid fuel injection in combustion system |
| title_full_unstemmed | A novel deep learning approach for investigating liquid fuel injection in combustion system |
| title_short | A novel deep learning approach for investigating liquid fuel injection in combustion system |
| title_sort | novel deep learning approach for investigating liquid fuel injection in combustion system |
| topic | Deep learning Fully Connected Neural Network Extra Tree Regressor Cryogenic propellants R-squared Liquid fuel injection |
| url | https://doi.org/10.1007/s44163-025-00248-2 |
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