A localized thickened flame model for simulations of flame propagation and autoignition under elevated pressure conditions

Hiroshi Terashima, Yutaka Hanada, Soshi Kawai

Research output: Contribution to journalArticlepeer-review

Abstract

The present study proposes a localized thickened flame (LTF) model for the accurate prediction of flame propagation and autoignition timing. The unresolved-scale terms appeared in spatially-filtered governing equations due to thin flame structures are constructed under a physical constraint in which laminar flame speed is maintained. A high-order derivative is introduced to dynamically localize the effects of the LTF in the regions of unresolved propagating flame. The model is also designed such that the thickened flame is resolved by the same number of grid points for any grid size used. Therefore, a user-specified constant in the model does not need to be adjusted depending on the employed grid size. Laminar flame propagation problems are used to validate the performance of the proposed LTF model and determine the appropriate value of the user-specified constant. The results using a one-dimensional constant-volume reactor demonstrate that the LTF successfully captures the accurate flame propagation behaviors under elevated pressure conditions, while not affecting the end-gas autoignition timing, even on relatively coarse grid resolutions. The high-order derivative in the LTF serves as a dynamic parameter for detecting the thinning flame under elevated pressure conditions.

Original languageEnglish
JournalProceedings of the Combustion Institute
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Autoignition
  • Flame propagation
  • Knocking combustion
  • Thickened flame model

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Mechanical Engineering
  • Physical and Theoretical Chemistry

Fingerprint Dive into the research topics of 'A localized thickened flame model for simulations of flame propagation and autoignition under elevated pressure conditions'. Together they form a unique fingerprint.

Cite this