Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries
Abstract This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, th...
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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/s41598-024-83211-y |
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| author | Harish Venu Manzoore Elahi M. Soudagar Tiong Sieh Kiong N. M. Razali Hua-Rong Wei Armin Rajabi V. Dhana Raju T. M. Yunus Khan Naif Almakayeel Erdem Cuce Huseyin Seker |
| author_facet | Harish Venu Manzoore Elahi M. Soudagar Tiong Sieh Kiong N. M. Razali Hua-Rong Wei Armin Rajabi V. Dhana Raju T. M. Yunus Khan Naif Almakayeel Erdem Cuce Huseyin Seker |
| author_sort | Harish Venu |
| collection | DOAJ |
| description | Abstract This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions. Key parameters, including spray penetration, droplet size distribution, and evaporation rates, are modeled and validated against experimental data. The findings reveal that nanoparticle-enhanced fuels, coupled with LSTM-based predictive analytics, lead to superior combustion performance and lower pollutant formation. This interdisciplinary approach provides a robust framework for designing next-generation CI engines with improved efficiency and sustainability. Diesel engine performance and emissions were found to be influenced by variations in combustion chamber geometry, underwent validation through simulation using Diesel-RK. Re-entrant bowl profile in quaternary blend is found to exhibit 31.3% higher BTE and 8.65% lowered BSFC than the conventional HCC bowl at full load condition. Emission wise, re-entrant bowl induced 90.16% lowered CO, 59.95% lowered HC and 15.48% lowered smoke owing to improved spray penetration and faster burning of soot precursors. However, the NOx emissions of DBOPN-TRCC were found to be higher. The simulation outcomes, derived from Diesel-RK, were subsequently compared with empirical data obtained from real-world experiments. These experiments were systematically carried out under identical operating conditions, employing different piston bowl geometries. |
| format | Article |
| id | doaj-art-cebce30feec84d8e8fbf92accc483a3a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cebce30feec84d8e8fbf92accc483a3a2025-08-20T02:40:33ZengNature PortfolioScientific Reports2045-23222025-01-0115112810.1038/s41598-024-83211-yNanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometriesHarish Venu0Manzoore Elahi M. Soudagar1Tiong Sieh Kiong2N. M. Razali3Hua-Rong Wei4Armin Rajabi5V. Dhana Raju6T. M. Yunus Khan7Naif Almakayeel8Erdem Cuce9Huseyin Seker10Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITENCollege of Engineering, Lishui UniversityInstitute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITENInstitute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITENDepartment of Photoelectric Engineering, Lishui UniversityInstitute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITENDepartment of Mechanical Engineering, Lakireddy Bali Reddy College of EngineeringDepartment of Mechanical Engineering, College of Engineering, King Khalid UniversityDepartment of Industrial Engineering, King Khalid UniversityDepartment of Mechanical Engineering, Faculty of Engineering and Architecture, Recep Tayyip Erdogan UniversityDepartment of Information Systems, College of Computing and Informatics, The University of SharjahAbstract This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions. Key parameters, including spray penetration, droplet size distribution, and evaporation rates, are modeled and validated against experimental data. The findings reveal that nanoparticle-enhanced fuels, coupled with LSTM-based predictive analytics, lead to superior combustion performance and lower pollutant formation. This interdisciplinary approach provides a robust framework for designing next-generation CI engines with improved efficiency and sustainability. Diesel engine performance and emissions were found to be influenced by variations in combustion chamber geometry, underwent validation through simulation using Diesel-RK. Re-entrant bowl profile in quaternary blend is found to exhibit 31.3% higher BTE and 8.65% lowered BSFC than the conventional HCC bowl at full load condition. Emission wise, re-entrant bowl induced 90.16% lowered CO, 59.95% lowered HC and 15.48% lowered smoke owing to improved spray penetration and faster burning of soot precursors. However, the NOx emissions of DBOPN-TRCC were found to be higher. The simulation outcomes, derived from Diesel-RK, were subsequently compared with empirical data obtained from real-world experiments. These experiments were systematically carried out under identical operating conditions, employing different piston bowl geometries.https://doi.org/10.1038/s41598-024-83211-yLSTMDiesel-RKNano additivesPerformanceEmissionCombustion |
| spellingShingle | Harish Venu Manzoore Elahi M. Soudagar Tiong Sieh Kiong N. M. Razali Hua-Rong Wei Armin Rajabi V. Dhana Raju T. M. Yunus Khan Naif Almakayeel Erdem Cuce Huseyin Seker Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries Scientific Reports LSTM Diesel-RK Nano additives Performance Emission Combustion |
| title | Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries |
| title_full | Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries |
| title_fullStr | Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries |
| title_full_unstemmed | Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries |
| title_short | Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries |
| title_sort | nanotechnology and lstm machine learning algorithms in advanced fuel spray dynamics in ci engines with different bowl geometries |
| topic | LSTM Diesel-RK Nano additives Performance Emission Combustion |
| url | https://doi.org/10.1038/s41598-024-83211-y |
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