A Model for Predicting Turbulent Burning Velocity by using Karlovitz Number and Markstein Number under EGR Conditions

Kei Yoshimura*, Kohei Ozawa, Kyohei Yamaguchi, Ratnak Sok, Jin Kusaka, Masaaki Togawa, Satoshi Tokuhara

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


The purpose of this paper is to build up a model for predicting turbulent burning velocity which can be used for One-Dimensional (1D) engine simulation. This paper presents the relationship between turbulent burning velocity, the Karlovitz number, and the Markstein number for building up the prediction model. The turbulent burning velocity was measured using a single-cylinder gasoline engine, which has an external Exhaust Gas Recirculation (EGR) system. In the experiment, various engine operating parameters, e.g. engine loads and EGR rates, and various engine specifications, i.e. different types of intake ports were tested. The Karlovitz number was calculated using Three-Dimensional Computational Fluid Dynamics (3D-CFD) and detailed chemical kinetics simulation with a premixed laminar flame model. The Markstein number was also calculated using detailed chemical kinetics simulation with the Extinction of Opposed-flow Flame model. The conditions of these simulations were set based on the experimental result. The experimental and calculation results show that the predicting accuracy of the turbulent burning velocity improves by using the Karlovitz number and the Markstein number in highly diluted conditions. It is hence important to take the effect of the flame stretch rate and the sensitivity of local burning velocity to flame stretch into account for the prediction.

Original languageEnglish
JournalSAE Technical Papers
Issue number2021
Publication statusPublished - 2021 Sept 21
EventSAE 2021 Powertrains, Fuels and Lubricants Digital Summit, FFL 2021 - Virtual, Online, United States
Duration: 2021 Sept 282021 Sept 30

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering


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