将拉格朗日-欧拉综合火花点火模型应用于不同工作条件

IF 1.1 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY
Samuel J. Kazmouz, R. Scarcelli, Matthew Bresler
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引用次数: 0

摘要

提高发动机效率对减少排放至关重要,这也是汽车制造商的首要任务。增压和高稀释工作等非常规模式具有提高发动机效率的潜力,但却存在稳定性和周期性变化的问题。为了帮助工程师设计点火系统,减少此类发动机运行模式下的周期性变化,必须建立可靠、准确的火花点火模型。本文使用拉格朗日-欧勒火花点火(LESI)模型模拟燃烧容器内惰性或反应横流在不同压力、流速和稀释率下的放电、火花通道伸长和点火。首先,简要回顾了模型公式。然后,介绍实验和模拟设置。结果表明,除了惰性情况下的火花通道伸长或反应情况下的火焰前沿增长外,该模型还能预测二次回路电压、电流和功率信号。结果还将模拟火花通道和火焰增长图与实验中不同时刻的 Schlieren 图像进行了比较。这项工作凸显了 LESI 预测各种工作条件下放电和点火特性的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of a Comprehensive Lagrangian–Eulerian Spark-Ignition Model to Different Operating Conditions
Increasing engine efficiency is essential to reducing emissions, which is a priority for automakers. Unconventional modes such as boosted and highly dilute operation have the potential to increase engine efficiency but suffer from stability concerns and cyclic variability. To aid engineers in designing ignition systems that reduce cyclic variability in such engine operation modes, reliable and accurate spark-ignition models are necessary. In this article, a Lagrangian–Eulerian spark-ignition (LESI) model is used to simulate electrical discharge, spark channel elongation, and ignition in inert or reacting crossflow within a combustion vessel, at different pressures, flow speeds, and dilution rates. First the model formulation is briefly revisited. Then, the experimental and simulations setups are presented. The results showcase the model’s ability to predict the secondary circuit voltage, current, and power signals, in addition to the spark channel elongation, for the inert cases, or flame front growth, for the reacting cases. The results also compare simulation spark channel and flame growth plots to experimental Schlieren images at different instants in time. This work serves to highlight LESI’s ability to predict the characteristics of discharge and ignition across a variety of operating conditions.
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来源期刊
SAE International Journal of Engines
SAE International Journal of Engines TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
2.70
自引率
8.30%
发文量
38
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