Internal combustion engines have been improved for many decades. Yet, complex phenomena are now resorted to, for which any optimum might be unstable: noise, low-temperature heat release timing, stratification, pollutant sweet spots, and so on. In order to make reliable statements on an improvement, one must specify the uncertainty related to it. Still, uncertainty quantification is generally missing in the piston engine experimental literature. Therefore, we detailed a mathematical methodology to obtain any engine parameter uncertainty and then used it to derive the uncertainty expressions of the physical quantities of the most generic homogeneous-charge compression–ignition research engine (mass-flow-induced mixture with C u H y O x N z S w fuel). We then applied those expressions on an existing hydrogen homogeneous-charge compression–ignition test bench. This includes the uncertainty propagation chain from sensor specifications, user calibrations, intake control, in-cylinder processes, and post-processing techniques. Directly measured physical quantities have uncertainties of around 1%, depending on the sensor quality (e.g. pressure, volume), but indirectly measured quantities relying on modelled parameters have uncertainties higher than 5% (e.g. wall heat losses, in-cylinder temperature, gross heat release, pressure rise rate). Other findings that such an analysis can bring relate, for example, to the physical quantities driving the uncertainty and to the ones that can be neglected. In the case of the homogeneous-charge compression–ignition engine considered, the effects of blow-by, bottle purity and air moisture content were found negligible; the post-processing for effective compression ratio, effective in-cylinder temperature, and top dead centre offset were found essential; and the pressure and volume uncertainties were found to be the main drivers to a large extent. The obtained numeric values serve the general purpose of alerting the experimenter on uncertainty order of magnitudes. The developed methodology shall be used and adapted by the experimenter willing to study the uncertainty propagation in their setup or willing to assess the adequacy of a sensor performance.

中文翻译：

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从原始测量到后处理数据的不确定性量化：通用方法及其在均质充量压燃发动机中的应用

内燃机已经改进了几十年。然而，现在诉诸复杂的现象，对于这些现象，任何优化都可能是不稳定的：噪音、低温放热时间、分层、污染物最佳点等。为了对改进做出可靠的陈述，必须详细说明与之相关的不确定性。尽管如此，活塞发动机实验文献中通常缺少不确定性量化。因此，我们详细介绍了一种获得任何发动机参数不确定性的数学方法，然后用它推导出最通用的均质充量压燃研究发动机（质量流诱导混合物与 C u H y 的物理量的不确定性表达式） O x N z S w 燃料）。然后，我们将这些表达式应用到现有的氢均质充量压燃试验台上。这包括来自传感器规格、用户校准、进气控制、缸内过程和后处理技术的不确定性传播链。直接测量的物理量的不确定性约为 1%，具体取决于传感器质量（例如压力、体积），但依赖建模参数的间接测量量的不确定性高于 5%（例如壁热损失、缸内温度、总热量）释放，压力上升率）。例如，这种分析可以带来的其他发现与驱动不确定性的物理量以及可以忽略的物理量有关。在考虑均质充气压燃发动机的情况下，窜气的影响，发现瓶子纯度和空气水分含量可以忽略不计；发现有效压缩比、有效缸内温度和上止点偏移的后处理是必不可少的；并且发现压力和体积的不确定性在很大程度上是主要驱动因素。获得的数值用于警告实验者不确定性数量级的一般目的。开发的方法应由愿意研究其设置中的不确定性传播或愿意评估传感器性能的充分性的实验者使用和调整。并且发现压力和体积的不确定性在很大程度上是主要驱动因素。获得的数值用于警告实验者不确定性数量级的一般目的。开发的方法应由愿意研究其设置中的不确定性传播或愿意评估传感器性能的充分性的实验者使用和调整。并且发现压力和体积的不确定性在很大程度上是主要驱动因素。获得的数值用于警告实验者不确定性数量级的一般目的。开发的方法应由愿意研究其设置中的不确定性传播或愿意评估传感器性能的充分性的实验者使用和调整。