Feifan Ji , Shuo Meng , Zhiyu Han , Guangyu Dong , Rolf D. Reitz
{"title":"Progress in knock combustion modeling of spark ignition engines","authors":"Feifan Ji , Shuo Meng , Zhiyu Han , Guangyu Dong , Rolf D. Reitz","doi":"10.1016/j.apenergy.2024.124852","DOIUrl":null,"url":null,"abstract":"<div><div>Knock combustion is one of the primary factors limiting thermal efficiency improvement in spark ignition (SI) engines. After a century of research, scholars have made significant progress in understanding knock phenomena. Numerical simulation techniques play a crucial role in engine research and development, and knock models are a vital component of engine combustion simulation. While various models with different complexities and predictive accuracies have been proposed, a comprehensive review addressing their evolution and performance remains necessary. This article endeavors to systematically evaluate the progress of knock models and offers a critical overview of up-to-date knock models, including their features, advantages, and limitations. It delves into problems in existing knock models and proposes potential solutions.</div><div>Among the most widely used models, those based on the Livengood-Wu (L-W) knock integral and autoignition reaction mechanism are extensively developed and applied. They are relatively simple and can predict knock onset time and knock intensity reasonably well. The combination of complex chemical reaction kinetics analysis and large-eddy simulation may be the most promising in capturing various aspects of knock combustion, including knock location. However, this method demands high computational resources, and its prediction is greatly affected by the simulation accuracy of autoignition reaction fronts. Machine learning can assist in developing empirical knock models by learning knock combustion characteristics from detailed physics-based modeling results or experimental data. These models have poor interpretability but could be very useful in engineering applications with sufficient accuracy. While many features of knock combustion can be characterized numerically, some details such as autoignition reaction front propagation and its impact, influence of turbulence modeling, and the effect of external random factors on knock modeling, still call for future research.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"378 ","pages":"Article 124852"},"PeriodicalIF":10.1000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306261924022359","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Knock combustion is one of the primary factors limiting thermal efficiency improvement in spark ignition (SI) engines. After a century of research, scholars have made significant progress in understanding knock phenomena. Numerical simulation techniques play a crucial role in engine research and development, and knock models are a vital component of engine combustion simulation. While various models with different complexities and predictive accuracies have been proposed, a comprehensive review addressing their evolution and performance remains necessary. This article endeavors to systematically evaluate the progress of knock models and offers a critical overview of up-to-date knock models, including their features, advantages, and limitations. It delves into problems in existing knock models and proposes potential solutions.
Among the most widely used models, those based on the Livengood-Wu (L-W) knock integral and autoignition reaction mechanism are extensively developed and applied. They are relatively simple and can predict knock onset time and knock intensity reasonably well. The combination of complex chemical reaction kinetics analysis and large-eddy simulation may be the most promising in capturing various aspects of knock combustion, including knock location. However, this method demands high computational resources, and its prediction is greatly affected by the simulation accuracy of autoignition reaction fronts. Machine learning can assist in developing empirical knock models by learning knock combustion characteristics from detailed physics-based modeling results or experimental data. These models have poor interpretability but could be very useful in engineering applications with sufficient accuracy. While many features of knock combustion can be characterized numerically, some details such as autoignition reaction front propagation and its impact, influence of turbulence modeling, and the effect of external random factors on knock modeling, still call for future research.
期刊介绍:
Applied Energy serves as a platform for sharing innovations, research, development, and demonstrations in energy conversion, conservation, and sustainable energy systems. The journal covers topics such as optimal energy resource use, environmental pollutant mitigation, and energy process analysis. It welcomes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gap between research, development, and implementation. The journal addresses a wide spectrum of topics, including fossil and renewable energy technologies, energy economics, and environmental impacts. Applied Energy also explores modeling and forecasting, conservation strategies, and the social and economic implications of energy policies, including climate change mitigation. It is complemented by the open-access journal Advances in Applied Energy.