Published February 2025 | Published
Journal Article Open

Tabulated chemistry approach for detonation simulations

  • 1. ROR icon California Institute of Technology

Abstract

Chemistry modeling in detonations typically relies on two broad approaches: simplified models with one- or two-step chemistry, and detailed chemistry. These approaches require choosing between computational efficiency or physical accuracy. To reduce the cost of chemistry while maintaining accurate physics, tabulated chemistry has been used extensively for flames/deflagrations in the low Mach number framework. In the simplest tabulated chemistry model for premixed flames, a progress variable, describing the progress of reactions in the system, is transported in the simulation. This progress variable is then used to look up all other species, transport properties, and thermodynamic variables from a pre-computed table. The present work extends the tabulated chemistry method to detonations. Even in non-reacting compressible flow simulations, the enthalpy and specific heat capacity are required; to describe these thermodynamic variables, the temperature is selected as a second table coordinate. The two table coordinates are able to capture virtually all variations in the progress variable source term. The Zel’dovich–von Neumann–Döring (ZND) model is found to be the most appropriate one-dimensional problem for generation of the table. The ZND tabulation approach is validated for both one-dimensional stable and pulsating and two-dimensional regular and irregular detonations in various H₂-O₂ mixtures. The tabulated chemistry simulations are able to reproduce the detailed chemistry results in terms of propagation speed, cellular structures, and source term statistics. For hydrogen detonations, the computational cost of scalar transport is reduced by a factor of 9 and the cost of the chemistry is reduced by a factor of 17. More substantial computational savings are expected for hydrocarbon fuels.

Copyright and License

© 2024 The Combustion Institute. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Acknowledgement

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 research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award DDR-ERCAP0026889. This work used Stampede3 at TACC through allocation MCH230009 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation, USA grants #2138259#2138286#2138307#2137603, and #2138296

Additional Information

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Contributions

Alexandra Baumgart: Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Matthew X. Yao: Writing – review & editing, Software, Resources, Methodology, Conceptualization. Guillaume Blanquart: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.

Data Availability

Code for table generation will be made available on request.

Supplemental Material

Supplementary data: Additional tabulation details, simulation parameters, and statistics for all hydrogen-oxygen mixtures used. (PDF)

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Additional details

Created:
January 6, 2025
Modified:
January 6, 2025