Published August 16, 2025 | Version Published
Journal Article

Assessment of flamelet/progress variable methods for supersonic combustion

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Lawrence Livermore National Laboratory
  • 3. ROR icon University of New Brunswick

Abstract

Tabulated chemistry models, including the flamelet/progress variable approach, have been successfully used for a variety of turbulent flame simulations. The progress variable describes the progress of reactions in a system and parameterizes a lookup table of thermochemical variables. This approach reduces the cost of simulations, transporting only one scalar (progress variable) instead of the many species mass fractions required for detailed chemistry. Originally developed for low Mach number flame simulations, recent works have focused on extensions of this approach to compressible flames, supersonic combustion, and detonations, with applications such as scramjet combustors and rotating detonation engines. Unlike low Mach simulations, compressible flow simulations require solving the energy transport equation, which is coupled to the equation of state. This leads to additional modeling challenges regarding the thermodynamics and its impact on the chemistry. The validity of modeling assumptions, for example the relationship between energy and temperature, also varies with the combustion regime. The present work provides a detailed assessment of the existing strategies for chemistry tabulation for compressible/supersonic combustion, including detonations. A priori analysis indicates that approximations which are reasonable for weakly compressible flames may break down for shock-induced combustion. The analysis identifies specific assumptions and approximations that do not hold for detonations, emphasizing that care must be taken when applying tabulated chemistry models outside their intended combustion regimes.

Copyright and License (English)

© 2025 The Combustion Institute. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Acknowledgement (English)

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Department of Energy Computational Science Graduate Fellowship under Award Number DE-SC0021110. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, release number LLNL-JRNL-2004828.

Contributions (English)

Alexandra Baumgart: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. Matthew X. Yao: Review & editing, Methodology, Investigation, Conceptualization. Guillaume Blanquart: Review & editing, Supervision, Methodology, Investigation, Conceptualization.

Additional details

Funding

United States Department of Energy
DE-SC0021110
United States Department of Energy
DE-AC52-07NA27344
Lawrence Livermore National Laboratory
LLNL-JRNL-2004828

Dates

Accepted
2025-07-23
Available
2025-08-16
Available online
Available
2025-08-16
Version of record

Caltech Custom Metadata

Caltech groups
Division of Engineering and Applied Science (EAS)
Publication Status
Published