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Studying HCCI Combustion and its Cyclic Variations Versus Heat Transfer, Mixing and Discretization using a PDF Based Approach

机译:使用基于PDF的方法研究HCCI燃烧及其循环变化与传热,混合和离散化

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The ability to predict cyclic variations is certainly useful in studying engine operating regimes, especially under unstable operating conditions where one single cycle may differ from another substantially and a single simulation may give rather misleading results. PDF based models such as Stochastic Reactor Models (SRM) are able to model cyclic variations, but these may be overpredicted if discretization is too coarse. The range of cyclic variations and the dependence of the ability to correctly assess their mean values on the number of cycles simulated were investigated. In most cases, the average values were assessed correctly on the basis of as few as 10 cycles, but assessing the complete range of cyclic variations could require a greater number of cycles. In studying average values, variations due too coarse discretization being employed are smaller than variations originating from changes in physical parameters, such as heat transfer and mixing parameters. Thus it feels safe to conclude that, even with such coarse discretization and fast execution, the findings obtained with use of the SRM are fundamentally correct. For studies of cyclic variations in engines, discretization needs to have a higher level of resolution to provide trustworthy results. In the case of high levels of turbulence and evenly distributed heat transfer, the in-cylinder conditions become homogeneous more quickly. The results indicate that in HCCI engines inhomogeneties tend to promote earlier ignition and more stable operating conditions as well as lesser cyclic variations. The pressure rate was shown to generally increase under homogeneous conditions, which could lead to unwanted noise and even to engine damage. According to calculations for HCCI engines, the level of turbulence and the heat transfer distribution had little impact on the duration of combustion or on the amount of HC and NO at EVO, except for HC which rocketed in the odd misfiring cycles.
机译:预测周期变化的能力在研究发动机工况时无疑是有用的,特别是在不稳定的工况下,其中一个单个循环可能与另一个循环明显不同,并且单个模拟可能会产生令人误解的结果。基于PDF的模型(例如随机反应堆模型(SRM))能够对循环变化进行建模,但是如果离散化过于粗糙,则可能会过度预测这些变化。研究了周期性变化的范围以及正确评估其平均值的能力对模拟循环数的依赖性。在大多数情况下,平均值可在少至10个周期的基础上正确评估,但评估完整的周期变化范围可能需要更多的周期。在研究平均值时,由于过于粗糙的离散化而导致的变化要小于源于物理参数(例如传热和混合参数)变化的变化。因此,可以得出结论,即使经过如此粗略的离散化和快速执行,使用SRM得出的结论从根本上也是正确的,因此可以得出结论。为了研究发动机的周期性变化,离散化需要具有更高的分辨率,以提供可信赖的结果。在高湍流和均匀分布的热传递的情况下,缸内条件会更快地变得均匀。结果表明,在HCCI发动机中,不均匀性往往会促进提前点火和更稳定的工作条件以及较小的循环变化。结果表明,在均匀条件下,压力速率通常会增加,这可能会导致不必要的噪音甚至损坏发动机。根据HCCI发动机的计算,湍流水平和传热分布对燃烧持续时间或EVO处的HC和NO的量影响很小,除了HC在奇数不点火循环中弹射外。

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