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Enhanced heat release analysis for advanced multi-mode combustion engine experiments

机译:增强的热量释放分析,可用于先进的多模式内燃机实验

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Advanced combustion strategies, such as Homogeneous-Charge Compression-Ignition (HCCI) and Spark-Assisted HCCI or Spark-Assisted Compression-Ignition (SACI) hold considerable promise for improving engine efficiencies while maintaining low pollutant emissions, yet few models exist that accurately include the important chemical and physical mechanisms of these advanced combustion strategies. Further, experimental data from advanced combustion engine experiments are not well represented using conventional spark ignited analytical tools. This paper presents new methods for advanced combustion analysis that integrate previous analytical methods with new algorithms to capture the unique features of advanced combustion strategies like SACI. The new analytical capabilities were incorporated into a program which was designated the Advanced Combustion Engine Heat Release Analysis (ACE-HRA) tool. The models developed and applied in ACE-HRA were assessed by comparison with high fidelity engine simulations of HCCI and SACI. The high fidelity simulations provided data sets with detailed predictions of heat release rates, temperatures, auto-ignition timing, flame speeds and other key parameters not resolved or measured in engine experiments. The sensitivity of ACE-HRA estimates to model input data was quantified for important engine performance parameters. The sensitivity analysis showed that estimates for in-cylinder masses have the largest overall impact on the ACE-HRA results (e.g. ±10% variation led to changes on the order of ±5-10% in peak rate of heat release, burn duration and peak temperature). Noticeable differences in peak heat release rate and ringing intensity were also observed when comparing cycle-by-cycle analysis against ensemble-average analysis, which has implications on how the results are interpreted and applied in modeling work. After validating ACE-HRA with the high-fidelity simulations, ACE-HRA was applied to interpret the data from a recent experimental study of SACI combustion. The ACE-HRA methods were used to infer the effects of flame propagation on in-cylinder gradients and cycle-to-cycle variability, and to provide quantitative estimates for the associated changes in the end-gas burn rate. The trends observed, such as the decrease in burn rate for later auto-ignition and higher burn fraction by flame, provide more in-depth understanding of SACI combustion and demonstrate the insight that can be revealed by the new ACE-HRA tool.
机译:先进的燃烧策略,例如均质压缩点火(HCCI)和火花辅助HCCI或火花辅助压缩点火(SACI),在提高发动机效率的同时保持较低的污染物排放具有广阔的前景,但很少有模型能够准确地包括这些先进燃烧策略的重要化学和物理机制。此外,使用常规的火花点火分析工具不能很好地表示来自高级内燃机实验的实验数据。本文介绍了用于高级燃烧分析的新方法,该方法将以前的分析方法与新算法集成在一起,以捕获高级燃烧策略(如SACI)的独特功能。新的分析功能已合并到一个程序中,该程序被称为高级内燃机发热量分析(ACE-HRA)工具。通过与HCCI和SACI的高保真引擎仿真比较,评估了在ACE-HRA中开发和应用的模型。高保真度模拟为数据集提供了放热率,温度,自动点火正时,火焰速度和发动机实验中未解决或未测量到的其他关键参数的详细预测。 ACE-HRA估计值对模型输入数据的敏感性已针对重要的发动机性能参数进行了量化。敏感性分析表明,缸内质量的估计值对ACE-HRA结果有最大的总体影响(例如,±10%的变化导致散热峰值速率,燃烧持续时间和峰值温度)。将逐周期分析与整体平均分析进行比较时,还观察到了峰值放热速率和振铃强度的显着差异,这对结果的解释和在建模工作中的应用产生了影响。在通过高保真度模拟验证ACE-HRA之后,ACE-HRA被用于解释来自SACI燃烧的最新实验研究的数据。 ACE-HRA方法用于推断火焰传播对缸内梯度和循环间变化的影响,并为最终气体燃烧率的相关变化提供定量估计。观察到的趋势,例如后来自动点火的燃烧速率降低和火焰导致的更高燃烧分数,提供了对SACI燃烧的更深入了解,并证明了新ACE-HRA工具可以揭示的见解。

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