Abstract Combustion chemistry models have been developed with inherent uncertainties in them. Whether a model that is developed using fundamental combustion experiments is capable to reproduce practical combustion processes within typical levels of measurement uncertainty is an open question. This paper quantifies the uncertainty of engine autoignition simulation using the uncertainties in the selected chemical kinetic model and then minimizes the model prediction uncertainty using various experiments. The method adopts a deterministic framework of uncertainty quantification, termed bound-to-bound data collaboration, and applies it to simulate the autoignition of n-pentane in a standard octane rating experiment. The results show that simulation of the end-gas autoignition using a comprehensively tested n-pentane model coupled with a two-zone engine combustion model yields an uncertainty substantially higher than that of engine experiment (as indicated by the cycle-to-cycle variation of the autoignition timing measurement). In-cylinder thermochemical conditions are found to be less important than the kinetic parameters in determining the model uncertainty. The large model uncertainty can be reduced by constraining the simulation with consistent experimental data and their measurement uncertainties, including those from fundamental experiments that measure ignition delays, species concentrations, flame speeds, and more significantly from autoignition experiments in well-calibrated engines.

中文翻译：

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量化发动机自燃动力学模拟中的不确定性

摘要 燃烧化学模型的开发具有固有的不确定性。使用基本燃烧实验开发的模型是否能够在典型的测量不确定性水平内重现实际燃烧过程是一个悬而未决的问题。本文使用所选化学动力学模型中的不确定性量化发动机自燃模拟的不确定性，然后使用各种实验最小化模型预测的不确定性。该方法采用不确定性量化的确定性框架，称为绑定到绑定数据协作，并将其应用于模拟标准辛烷值实验中正戊烷的自燃。结果表明，使用经过全面测试的正戊烷模型与两区发动机燃烧模型相结合的尾气自燃模拟产生的不确定性大大高于发动机实验的不确定性（如循环到循环的变化所示）自燃时间测量）。在确定模型不确定性时，发现缸内热化学条件不如动力学参数重要。通过使用一致的实验数据及其测量不确定性来约束模拟，可以减少较大的模型不确定性，包括来自测量点火延迟、物种浓度、火焰速度的基础实验的数据，更重要的是来自校准良好的发动机的自动点火实验的数据。