| 摘要:综述低速二冲程柴油机扫气模型的研究进展，重点分析在当前低速机冲程缸径比不断增大趋势下，现有扫气模型面临的挑战及未来发展方向。研究表明，伴随超长冲程设计的普及，现有的扫气模型在模拟此类机型的高效扫气过程方面暴露出局限性，尤其是在准确预测缸内残余废气质量分数分布、扫气效率及对扫气系统结构参数敏感性方面。回顾经典扫气模型以及基于人工智能的神经网络模型等，指出这些模型虽在一定程度上反映了扫气过程的基本特性，但在应对超长冲程低速机特有的流场特征和扫气机制时，模型的适应性和准确性不足，尤其是未能充分考虑扫气倾斜角、气缸几何形状对扫气效果的具体影响，以及如何在模型中准确刻画缸内复杂的流动结构。指出未来扫气模型的发展须侧重于构建能准确反映超长冲程直流扫气特性的模型，包括但不限于考虑扫气系统结构参数对扫气性能影响、实现对缸内残余废气质量分数分布预测、结合机器学习提升模型的通用性和计算效率等，对于拓展模型的应用范围贡献了有价值的参考和启示。 |
| 关键词: 低速柴油机 直流扫气 超长冲程 性能预测模型 |
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| Review of Low-speed Two-stroke Diesel Engine Scavenging Models |
| WANG Yingyuan,ZHANG Ruiping,WEI Jiangshan,WANG Yuanding,YANG Mingyang,DENG Kangyao |
| Shanghai Institute of Space Propulsion， Shanghai 201112， China;Shanghai Engineering Research Center of Space Engine， Shanghai 201112， China;Key Laboratory for Power Machinery and Engineering， Ministry of Education， Shanghai Jiao Tong University， Shanghai 200240， China |
| Abstract:The research progress of low-speed two-stroke diesel engine scavenging models is reviewed，and key analysis is conducted on the challenges and future development direction of the existing scavenging models under the current trend of increasing stroke-to-bore ratio of low-speed engines. It is shown that，along with the popularization of ultra-long-stroke design，the existing scavenging models have revealed their limitations in simulating the efficient scavenging process of low-speed diesel engines，especially in accurately predicting the mass fraction distribution of residual exhaust gases in the cylinder，the scavenging efficiency，and the sensitivity to the structural parameters of the scavenging system.The classical scavenging models and artificial intelligence-based neural network models are reviewed，and it is pointed out that although these models reflect the basic characteristics of the scavenging process to a certain extent，they are not sufficiently adaptive and accurate in dealing with the flow field characteristics and scavenging mechanism unique to ultra-long-stroke low-speed engines. In particular，these models fail to fully consider the specific effects of scavenging incline angle and cylinder geometry on the scavenging effect，and how to accurately portray the complex flow structure inside the cylinder in the model. This review points out that the future development of scavenging models should focus on constructing models that can accurately reflect the characteristics of ultra-long stroke uniflow scavenging，including but not limited to：considering the influence of the structural parameters of the scavenging system on the performance of scavenging，realizing the prediction of the distribution of the residual exhaust gases in the cylinder，and combining with the machine learning to improve the versatility and computational efficiency of the model，which contributes valuable references and insights for expanding the scope of the model application. |
| Key words: low-speed diesel engine uniflow scavenging ultra-long stroke performance prediction model |
