Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This article contributes to this fast evolving body of knowledge by focusing on the evaluation of NOx emission prediction capability of a probability density function–based stochastic reactor engine models for a Diesel engine. The research implements a systematic approach to the study of the stochastic reactor engine model performance, underpinned by a detailed space-filling design of experiments (DoE)-based sensitivity analysis of both external and internal parameters, evaluating their effects on the accuracy in matching physical measurements of both in-cylinder conditions and NOx output. The approach proposed in this article introduces an automatic stochastic reactor engine model calibration methodology across the engine operating envelope, based on a multi-objective optimization approach. This aims to exploit opportunities for internal stochastic reactor engine model parameters tuning to achieve good overall modelling performance as a trade-off between physical in-cylinder measurements accuracy and the output NOx emission predictions error. The results from the case study provide a valuable insight into the effectiveness of the stochastic reactor engine model, showing good capability for NOx emissions prediction and trends, while pointing out the critical sensitivity to the external input parameters and modelling conditions.

中文翻译：

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柴油发动机应用的零维随机反应堆建模评估

在发动机开发过程的早期，从缸内燃烧模拟中以高保真度预测发动机输出排放仍然是一项重大挑战。本文侧重于评估基于概率密度函数的柴油发动机随机反应堆发动机模型的 NOx 排放预测能力，从而为这一快速发展的知识体系做出贡献。该研究采用了一种系统方法来研究随机反应堆发动机模型的性能，以详细的空间填充实验设计 (DoE) 为基础，对外部和内部参数进行敏感性分析，评估它们对匹配物理精度的影响。测量缸内状况和 NOx 输出。本文中提出的方法基于多目标优化方法，介绍了一种跨发动机运行包线的自动随机反应堆发动机模型校准方法。这旨在利用内部随机反应堆发动机模型参数调整的机会，以实现良好的整体建模性能，作为物理缸内测量精度和输出 NOx 排放预测误差之间的权衡。案例研究的结果提供了对随机反应堆发动机模型有效性的宝贵见解，显示了对 NOx 排放预测和趋势的良好能力，同时指出了对外部输入参数和建模条件的关键敏感性。基于多目标优化方法。这旨在利用内部随机反应堆发动机模型参数调整的机会，以实现良好的整体建模性能，作为物理缸内测量精度和输出 NOx 排放预测误差之间的权衡。案例研究的结果提供了对随机反应堆发动机模型有效性的宝贵见解，显示了对 NOx 排放预测和趋势的良好能力，同时指出了对外部输入参数和建模条件的关键敏感性。基于多目标优化方法。这旨在利用内部随机反应堆发动机模型参数调整的机会，以实现良好的整体建模性能，作为物理缸内测量精度和输出 NOx 排放预测误差之间的权衡。案例研究的结果提供了对随机反应堆发动机模型有效性的宝贵见解，显示了对 NOx 排放预测和趋势的良好能力，同时指出了对外部输入参数和建模条件的关键敏感性。