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An Improved Rate of Heat Release Model for Modern High-Speed Diesel Engines

刊名：	Journal of engineering for gas turbines and power: Transactions of the ASME
作者：	Dowell, Peter G.（Univ Bath, Dept Mech Engn, Bath BA2 7AY, Avon, England） Akehurst, Sam（Univ Bath, Dept Mech Engn, Bath BA2 7AY, Avon, England） Burke, Richard D.（Univ Bath, Dept Mech Engn, Bath BA2 7AY, Avon, England）
刊号：	720B0002
ISSN：	0742-4795
出版年：	2017
年卷期：	2017, vol.139, no.9
总页数：	14
分类号：	TK
关键词：	combustion modeling；rate of heat release；ignition delay；rate of injection
参考中译：
语种：	eng
文摘：	To meet the increasingly stringent emissions standards, diesel engines need to include more active technologies with their associated control systems. Hardware-in-the-loop (HiL) approaches are becoming popular where the engine system is represented as a real-time capable model to allow development of the controller hardware and software without the need for the real engine system. This paper focusses on the engine model required in such approaches. A number of semi-physical, zero-dimensional combustion modeling techniques are enhanced and combined into a complete model, these include-ignition delay, premixed and diffusion combustion and wall impingement. In addition, a fuel injection model was used to provide fuel injection rate from solenoid energizing signals. The model was parameterized using a small set of experimental data from an engine dynamometer test facility and validated against a complete data set covering the full engine speed and torque range. The model was shown to characterize the rate of heat release (RoHR) well over the engine speed and load range. Critically, the wall impingement model improved R-2 value for maximum RoHR from 0.89 to 0.96. This was reflected in the model's ability to match both pilot and main combustion phasing, and peak heat release rates derived from measured data. The model predicted indicated mean effective pressure and maximum pressure with R-2 values of 0.99 across the engine map. The worst prediction was for the angle of maximum pressure which had an R-2 of 0.74. The results demonstrate the predictive ability of the model, with only a small set of empirical data for training-this is a key advantage over conventional methods. The fuel injection model yielded good results for predicted injection quantity (R-2 = 0.99) and enabled the use of the RoHR model without the need for measured rate of injection.