Molecular level simulations of combustion processes using the DSMC method

Abstract

A technique called the Direct Simulation Monte Carlo (DSMC) method is used for simulating laminar one-dimensional hydrogen-air flames with an aim to establish the feasibility of molecular-level simulations for combustion using the Quantum-Kinetic (QK) reaction model. In DSMC, simulation particles are employed which represent many molecules with similar properties. The DSMC method effectively solves the Boltzmann equation by decoupling the motion of molecules into two phases: a Move phase and a Collision phase. Chemistry is treated using the Quantum-Kinetic model in which the total energy exchange resulting from molecular collision determines the outcome of the chemical reactions. A major advantage of DSMC is that it avoids using continuum Arrhenius reaction rates or simplified gradient laws for the diffusivities of mass, momentum and heat. Instead, any such laws and their parameters emerge naturally from the DSMC results. Simulations of one-dimensional hydrogen-air flames using the DSMC method with a detailed chemical scheme show promising results for molecular-level simulations of combustion. An adjustment to the activation energy of a key reaction is made in order to achieve the correct flame speed. The species and temperature profiles from DSMC show reasonable agreement with those obtained from Direct Numerical Simulation (DNS) using a conventional continuum approach. Likewise, the molecular diffusivity of hydrogen and oxygen also show reasonable agreement with the DNS results, although differences in the results still exist, especially for oxygen diffusivity. These are expected to improve with further development in the DSMC method for combustion.

Keywords:

• DSMC
• molecular level simulations
• particle simulation
• Boltzmann equation
• laminar flame

Acknowledgments

This work has been performed using resources provided by the “Cambridge Service for Data Driven Discovery” (CSD3, http://csd3.cam.ac.uk) system operated by the University of Cambridge Research Computing Service (http://www.hpc.cam.ac.uk) funded by EPSRC Tier-2 capital grant EP/P020259/1. Special thanks are due to Dr. Michail Gallis for providing help with SPARTA.

Disclosure statement

No potential conflict of interest was reported by the author(s).