Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers
Torsional vibration dampers effectively mitigate torsional oscillations and additional stresses in diesel engine crankshaft systems, ensuring operational safety and reliability. Traditional damper selection principles, grounded in dual-pendulum dynamic models, focus on minimizing maximum torsional a...
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MDPI AG
2025-05-01
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| author | Zhongxu Tian Zhongda Ge |
| author_facet | Zhongxu Tian Zhongda Ge |
| author_sort | Zhongxu Tian |
| collection | DOAJ |
| description | Torsional vibration dampers effectively mitigate torsional oscillations and additional stresses in diesel engine crankshaft systems, ensuring operational safety and reliability. Traditional damper selection principles, grounded in dual-pendulum dynamic models, focus on minimizing maximum torsional angles but fail to accurately characterize vibration behaviors in multi-cylinder engines. This study addresses this limitation by investigating dynamic modeling and numerical methods for an eight-cylinder diesel crankshaft system. A torsional vibration model was developed using Cholesky decomposition and the Jacobi sweep method for free vibration analysis, followed by dynamic response calculations through model decoupling and modal superposition. Parameter optimization of the damper was achieved via the NSGA-II multi-objective algorithm combined with a Bayesian-hyperparameter-optimized BP neural network. The results show that high-inertia-ratio dampers effectively suppress vibration and stress, while low-inertia-ratio configurations require approximately 20% elevated tuning ratios beyond theoretical parameters to achieve an additional 5% stress reduction, albeit with amplified torsional oscillations. Additionally, the study critically evaluates the numerical reliability of conventional dual-pendulum-based tuning ratio selection methods. This integrated approach enhances the precision of damper parameter matching for multi-cylinder engine applications. |
| format | Article |
| id | doaj-art-0ba1cd3296a74a1abfe7493975281c09 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-0ba1cd3296a74a1abfe7493975281c092025-08-20T02:33:38ZengMDPI AGApplied Sciences2076-34172025-05-011510563910.3390/app15105639Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional DampersZhongxu Tian0Zhongda Ge1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 200120, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 200120, ChinaTorsional vibration dampers effectively mitigate torsional oscillations and additional stresses in diesel engine crankshaft systems, ensuring operational safety and reliability. Traditional damper selection principles, grounded in dual-pendulum dynamic models, focus on minimizing maximum torsional angles but fail to accurately characterize vibration behaviors in multi-cylinder engines. This study addresses this limitation by investigating dynamic modeling and numerical methods for an eight-cylinder diesel crankshaft system. A torsional vibration model was developed using Cholesky decomposition and the Jacobi sweep method for free vibration analysis, followed by dynamic response calculations through model decoupling and modal superposition. Parameter optimization of the damper was achieved via the NSGA-II multi-objective algorithm combined with a Bayesian-hyperparameter-optimized BP neural network. The results show that high-inertia-ratio dampers effectively suppress vibration and stress, while low-inertia-ratio configurations require approximately 20% elevated tuning ratios beyond theoretical parameters to achieve an additional 5% stress reduction, albeit with amplified torsional oscillations. Additionally, the study critically evaluates the numerical reliability of conventional dual-pendulum-based tuning ratio selection methods. This integrated approach enhances the precision of damper parameter matching for multi-cylinder engine applications.https://www.mdpi.com/2076-3417/15/10/5639torsional vibrationcrankshaft systemneural networkadditional stressbi-objective optimization |
| spellingShingle | Zhongxu Tian Zhongda Ge Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers Applied Sciences torsional vibration crankshaft system neural network additional stress bi-objective optimization |
| title | Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers |
| title_full | Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers |
| title_fullStr | Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers |
| title_full_unstemmed | Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers |
| title_short | Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers |
| title_sort | parameter matching multi objective optimization for diesel engine torsional dampers |
| topic | torsional vibration crankshaft system neural network additional stress bi-objective optimization |
| url | https://www.mdpi.com/2076-3417/15/10/5639 |
| work_keys_str_mv | AT zhongxutian parametermatchingmultiobjectiveoptimizationfordieselenginetorsionaldampers AT zhongdage parametermatchingmultiobjectiveoptimizationfordieselenginetorsionaldampers |