Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning
Abstract In this paper, the usage of a predictive surrogate model for the estimate of flow variables in the transient phase of coolant injection from the nose cone by combining the Long Short-Term Memory (LSTM) and Proper Orthogonal Decomposition (POD) technique. The velocity, pressure, and mass fra...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87926-4 |
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| author | A. B. Ali M. Yasiri Pooya Ghodratallah Kamal Sharma Nimesh Raj S. Abdul Ameer Mohammed Yaseen Abdullah Abbas Hameed Abdul Hussein Mohammed Y. Al-Khuzaie M. Ahmedi |
| author_facet | A. B. Ali M. Yasiri Pooya Ghodratallah Kamal Sharma Nimesh Raj S. Abdul Ameer Mohammed Yaseen Abdullah Abbas Hameed Abdul Hussein Mohammed Y. Al-Khuzaie M. Ahmedi |
| author_sort | A. B. Ali |
| collection | DOAJ |
| description | Abstract In this paper, the usage of a predictive surrogate model for the estimate of flow variables in the transient phase of coolant injection from the nose cone by combining the Long Short-Term Memory (LSTM) and Proper Orthogonal Decomposition (POD) technique. The velocity, pressure, and mass fraction of the counterflow jet is evaluated via this hybrid technique and the source of discrepancy of a predictive surrogate model with Full order model is explained in this study. The POD modes for the efficient prediction of the different flow variables are defined. The performance of the POD + LSTM for different ranges of training and test is evaluated and it is found that the performance of this hybrid technique is acceptable when 80% of the available data is training test. The predictive errors of coolant mass fraction and axial velocity is higher due to the complexity of the vortex in the recirculation region. |
| format | Article |
| id | doaj-art-325c9755c4ba4f67acb78749d48fa371 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-325c9755c4ba4f67acb78749d48fa3712025-02-02T12:19:30ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-025-87926-4Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learningA. B. Ali0M. Yasiri1Pooya Ghodratallah2Kamal Sharma3Nimesh Raj4S. Abdul Ameer5Mohammed Yaseen Abdullah6Abbas Hameed Abdul Hussein7Mohammed Y. Al-Khuzaie8M. Ahmedi9Departmet of Chemical Engineering, Al-Amarah UniversityDepartmet of Chemical Engineering, Al-Amarah UniversityDepartment of Civil Engineering, College of Engineering, Cihan University-ErbilDepartment of Mechanical Engineering, Institute of Engineering and Technology, GLA UniversityCentre of Research Impact and Outcome, Chitkara UniversityDepartment of Automobile Engineering, College of Engineering, Al-Musayab, University of BabylonAl-Noor University CollegeAhl Al Bayt UniversityCollege of Technical Engineering, National University of Science and TechnologyDepartment of Chemical Engineering, Al-Amarah UniversityAbstract In this paper, the usage of a predictive surrogate model for the estimate of flow variables in the transient phase of coolant injection from the nose cone by combining the Long Short-Term Memory (LSTM) and Proper Orthogonal Decomposition (POD) technique. The velocity, pressure, and mass fraction of the counterflow jet is evaluated via this hybrid technique and the source of discrepancy of a predictive surrogate model with Full order model is explained in this study. The POD modes for the efficient prediction of the different flow variables are defined. The performance of the POD + LSTM for different ranges of training and test is evaluated and it is found that the performance of this hybrid technique is acceptable when 80% of the available data is training test. The predictive errors of coolant mass fraction and axial velocity is higher due to the complexity of the vortex in the recirculation region.https://doi.org/10.1038/s41598-025-87926-4Supersonic flowCoolant jetMachine learningPODLSTMNose cone |
| spellingShingle | A. B. Ali M. Yasiri Pooya Ghodratallah Kamal Sharma Nimesh Raj S. Abdul Ameer Mohammed Yaseen Abdullah Abbas Hameed Abdul Hussein Mohammed Y. Al-Khuzaie M. Ahmedi Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning Scientific Reports Supersonic flow Coolant jet Machine learning POD LSTM Nose cone |
| title | Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning |
| title_full | Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning |
| title_fullStr | Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning |
| title_full_unstemmed | Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning |
| title_short | Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning |
| title_sort | prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning |
| topic | Supersonic flow Coolant jet Machine learning POD LSTM Nose cone |
| url | https://doi.org/10.1038/s41598-025-87926-4 |
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