Application of a Pressured-Based OpenFOAM Solver for Rotating Detonation Engines

TL;DR

This paper develops a high-accuracy, cost-efficient OpenFOAM simulation framework for rotating detonation engines, validated against experimental data, and demonstrates significant computational cost reductions through adaptive mesh refinement and dynamic load balancing.

Contribution

The study introduces a validated multicomponentFluid solver for RDEs with novel cost-saving techniques like AMR and DLB, achieving up to 11.2 times reduction in computational costs.

Findings

Simulation results agree well with experimental data.

AMR and DLB reduce computational costs significantly.

DLB has a notable impact on efficiency.

Abstract

This study aims to develop a simulation framework for rotating detonation engines (RDEs) using multicomponentFluid solver in OpenFOAM v12 and to demonstrate reducing the computational costs by adaptive mesh refinement (AMR) and dynamic load balancing (DLB). RDEs have been extensively studied for improvements in efficiency for power generation and aircraft propulsion systems. A well-established framework, showing both high accuracy and cost efficiency, is required to facilitate further research and development in RDEs. The multicomponentFluid solver is validated against two problems: one-dimensional planar detonation simulation and two-dimensional RDE simulation, in which the present study's results are compared to reference results of experiments and simulations, respectively. In the problems, the present simulation results agree well with the validation data both qualitatively (e.g., pressure distribution and temperature field) and quantitatively (e.g., detonation velocity, mass flux, and specific impulse and thrust). In the two-dimensional RDE simulation, we propose a detonation velocity correction method for fair comparison with Chapman-Jouguet (CJ) detonation velocity. Moreover, the two-dimensional RDE simulation is optimized using AMR and DLB. By adopting both, computational costs decrease by up to 11.2 times. The effect of each of them is examined as well, which highlights the importance of DLB.