Cycle-to-cycle variations are important to consider in the development of spark-ignition engines to further increase fuel conversion efficiency. Direct numerical simulation and large eddy simulation can predict the stochastics of flows and therefore cycle-to-cycle variations. However, the computational costs are too high for engineering purposes if detailed chemistry is applied. Detailed chemistry can predict the fuels’ tendency to auto-ignite for different octane ratings as well as locally changing thermodynamic and chemical conditions which is a prerequisite for the analysis of knocking combustion. In this work, the joint use of unsteady Reynolds-averaged Navier–Stokes simulations for the analysis of the average engine cycle and the spark-ignition stochastic reactor model for the analysis of cycle-to-cycle variations is proposed. Thanks to the stochastic approach for the modeling of mixing and heat transfer, the spark-ignition stochastic reactor model can mimic the randomness of turbulent flows that is missing in the Reynolds-averaged Navier–Stokes modeling framework. The capability to predict cycle-to-cycle variations by the spark-ignition stochastic reactor model is extended by imposing two probability density functions. The probability density function for the scalar mixing time constant introduces a variation in the turbulent mixing time that is extracted from the unsteady Reynolds-averaged Navier–Stokes simulations and leads to variations in the overall mixing process. The probability density function for the inflammation time accounts for the delay or advancement of the early flame development. The combination of unsteady Reynolds-averaged Navier–Stokes and spark-ignition stochastic reactor model enables one to predict cycle-to-cycle variations using detailed chemistry in a fraction of computational time needed for a single large eddy simulation cycle.

中文翻译：

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火花点火发动机周期间变化的计算有效预测

在开发火花点火发动机以进一步提高燃料转换效率时，必须考虑循环之间的变化。直接数值模拟和大涡模拟可以预测流动的随机性，从而预测周期间的变化。然而，如果应用详细的化学，计算成本对于工程目的来说太高了。详细的化学成分可以预测燃料在不同辛烷值以及局部变化的热力学和化学条件下自燃的趋势，这是分析爆震燃烧的先决条件。在这项工作中，提出了联合使用非定常雷诺平均 Navier-Stokes 模拟来分析平均发动机循环和火花点火随机反应堆模型来分析循环之间的变化。由于混合和传热建模的随机方法，火花点火随机反应器模型可以模拟雷诺平均 Navier-Stokes 建模框架中缺少的湍流的随机性。通过施加两个概率密度函数，扩展了通过火花点火随机反应堆模型预测周期间变化的能力。标量混合时间常数的概率密度函数引入了湍流混合时间的变化，该变化是从非定常雷诺平均 Navier-Stokes 模拟中提取的，并导致整个混合过程的变化。炎症时间的概率密度函数解释了早期火焰发展的延迟或提前。