题目：Quantifying Uncertainty in Kinetic Simulation of Engine Autoignition

时间：2020年1月7日 10:00-11:30

地点：suncitygroup太阳新城官网 F310会议室

报告人：Assoc. Prof. Yi Yang (University of Melbourne)

邀请人：韩东 特别研究员（内燃机研究所）

Biography

Dr Yi Yang is an Associate Professor of Mechanical Engineering at the University of Melbourne. His research includes combustion chemistry, low emission fuels, advanced combustion engines, and fuel/engine interactions. He received his PhD from the Pennsylvania State University in 2008 and conducted postdoctoral research at Sandia National Laboratories before joining the University of Melbourne in 2012. He is a member of the Combustion Institute and the Society of Automotive Engineers. He is an Associate Editor of the SAE International Journal of Engines and currently serves on the board of the Combustion Institute – Australia and New Zealand as the Section Secretary.

Abstract

Combustion chemistry models have been developed with inherent uncertainties in them. Whether a model developed using fundamental combustion experiments can reproduce practical combustion processes within typical levels of measurement uncertainty is an open question. In this talk, we quantify the uncertainty of engine autoignition simulation using the deterministic, bound-to-bound data collaboration approach. A case study is reported for autoignition of n-pentane in a standard octane rating experiment using a two-zone combustion model coupled with a detailed chemistry model. The results show that the prediction uncertainty of a comprehensively tested n-pentane model is substantially higher than the measurement uncertainty of engine experiments. In-cylinder thermochemical conditions, such as the temperature at the intake valve closure and the heat transfer coefficient, are found to be less important than reaction rate coefficients in determining the model uncertainty. The large prediction uncertainty can be reduced by constraining the model with fundamental experiments including ignition delays and species concentrations, and more significantly, with autoignition timings from well-calibrated engine experiments. Finally, the model prediction uncertainties are evaluated in terms of octane number measurement errors using two sets of CFR engine experiments, from which the uncertainty of the most constrained model is found to be comparable to the tolerance allowed for standard octane number tests.