Chemical-diffusive models for flame acceleration and transition-to-detonation: genetic algorithm and optimisation procedure

Abstract

This paper presents a general approach for developing an automated, fast and flexible procedure to determine the reaction parameters for a simplified chemical-diffusive model to simulate flame acceleration and deflagration-to-detonation transition (DDT) in a stoichiometric methane–air mixture. The procedure uses a combination of a genetic algorithm and Nelder-Mead optimisation scheme to find the optimal reaction parameters for a reaction rate based on an Arrhenius form for conversion of reactants to products. The model finds six optimal reaction parameters that reproduce six flame and detonation properties. Results show that the reaction parameters closely reproduce their intended flame and detonation properties. The laminar flame profile computed using the reaction parameters in a 1D Navier-Stokes code matches the profile obtained when using a detailed chemical reaction mechanism. The optimal reaction parameters are then used in a 2D simulation of flame acceleration and DDT in an obstacle-laden channel containing stoichiometric methane–air, and the results show that the computation closely follows the transition-to-detonation observed in experiments. This automated procedure for finding parameters for a proposed reaction model makes it possible to simulate the behaviour of flames and detonations in large, complex scenarios, which would otherwise be an incalculable problem.

Keywords:

• chemical-diffusive model
• deflagration-to-detonation transition
• genetic algorithm
• optimisation procedure

Acknowledgments

The authors are grateful to Drs Gabriel Goodwin, Ryan Houim, Weilin Zheng and Huahua Xiao for their help with flame acceleration and DDT (FAST) simulations, and to Drs David Kessler and Vadim Gamezo for their background work in development of chemical-diffusive models.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the University of Maryland through Minta Martin Endowment Funds in the Department of Aerospace Engineering, and through the Glenn L. Martin Institute Chaired Professorship at the A. James Clark School of Engineering. In addition, this work was supported by the Alpha Foundation for the Improvement of Mine Safety and Health, Inc. [grant number: AFC215–20], the Office of Naval Research [Contract No. N00014–14–1–0177], and the Army Research Office [Contract No. W911NF-17-1-0524]. The authors acknowledge the University of Maryland Supercomputing Resources (http://www.it.umd.edu/hpcc) made available in conducting the research reported in this paper.