論文

2021年7月

A predictive combustion model for one-dimensional gasoline engine simulation

INTERNATIONAL JOURNAL OF ENGINE RESEARCH
  • Yoshihiro Nomura
  • ,
  • Seiji Yamamoto
  • ,
  • Makoto Nagaoka
  • ,
  • Stephan Diel
  • ,
  • Kenta Kurihara
  • ,
  • Rio Shimizu
  • ,
  • Eiji Murase

22
8
開始ページ
2560
終了ページ
2574
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1177/1468087420945905
出版者・発行元
SAGE PUBLICATIONS LTD

A new predictive combustion model for a one-dimensional computational fluid dynamics tool in the multibody dynamics processes of gasoline engines was developed and validated. The model consists of (1) a turbulent burning velocity model featuring a flame radius-based transitional function, steady burning velocity that considers local quenching using the Karlovitz number and laminarization by turbulent Reynolds number, as well as turbulent flame thickness and its quenching model near the liner wall, and (2) a knock model featuring auto-ignition by the Livengood-Wu integration and ignition delay time obtained using a full-kinetic model. The proposed model and previous models were verified under a wide range of operating conditions using engines with widely different specifications. Good agreement was only obtained for combustion characteristics by the proposed model without requiring individual calibration of model constants. The model was also evaluated for utilization after prototyping. Improved accuracy, especially of ignition timing, was obtained after further calibration using a small amount of engine data. It was confirmed that the proposed model is highly accurate at the early stage of the engine development process, and is also applicable for engine calibration models that require higher accuracy.

リンク情報
DOI
https://doi.org/10.1177/1468087420945905
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000559221200001&DestApp=WOS_CPL
ID情報
  • DOI : 10.1177/1468087420945905
  • ISSN : 1468-0874
  • eISSN : 2041-3149
  • Web of Science ID : WOS:000559221200001

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