汽油机无油层的多维激光诊断与数值分析

Göran Josefsson , Ingemar Magnusson , Frank Hildenbrand , Christof Schulz , Volker Sick
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引用次数: 18

摘要

采用激光诊断成像技术对一台商用汽油机进行了详细的缸内数据采集。利用粒子成像测速仪(PIV)获得了平均流场和湍流强度。采用平面激光诱导荧光(LIF)技术测量瞬时定量no浓度场。从NO的实验图像也可以推断出火焰的传播。利用三维计算机代码SPEEDSTAR对燃烧过程和NO生成过程进行了模拟。总的来说,实验数据和计算结果之间的总体一致性是令人鼓舞的,但仍有一些问题有待解决。平均流量预测得很好,而湍流量的预测则不太令人满意。火焰传播的计算结果与实测结果吻合较好。计算得到的NO浓度在空间分布和绝对数量密度方面与测量值接近。正如预期的那样,最高的NO浓度出现在燃烧开始最早的地区。空气中一氧化氮的浓度比废气中一氧化氮的浓度高4倍。实验结果与计算结果的比较清楚地表明,尽管三维计算机模型可以预测与测量一致的缸内过程的主要特征,但诸如精确的流动模式和火焰发展等细节很难捕捉,并且严重依赖于所使用的一些模型参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multidimensional laser diagnostic and numerical analysis of no formation in a gasoline engine

Laser diagnostic imaging techniques were used to obtain detailed in-cylinder data from a commercial gasoline engine. Mean flowfields and turbulence intensities were acquired using particle imaging veloci-metry (PIV). Instantaneous quantitative NO-concentration fields were measured using planar laser-induced fluorescence (LIF). From experimental images of NO, also the flame propagation could be deduced. Combustion and NO formation were simulated with a 3-D computer code SPEEDSTAR. The overall agreement between experimental data and computational results is encouraging in general, with remaining issues to be solved. The mean flow is well predicted, whereas the prediction of turbulence quantities is less satisfactory. Calculated results for flame propagation are in good agreement with measurements. NO concentrations resulting from calculations are close to those measured, both in respect to their spatial distribution and absolute number densities. As could be expected, the highest NO concentrations are found in regions where combustion started earliest. Local concentrations of NO are found be up to 4 times higher than those in the exhaust. The comparison of experimental results with calculations clearly shows that, although the 3-D computer model can predict major features of the in-cylinder processes in agreement with measurements, details such as the exact flow pattern and flame development are difficult to capture and depend critically on some of the models parameters used.

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