A low-dimensional network model with post-detonation expansion for flow field analysis of rotating detonation combustor

Rotating detonation engines (RDEs) represent a promising technology for pressure-gain combustion, but their predictive modelling remains challenging due to the complex interplay of high-speed flow and chemical reaction. High-fidelity computational fluid dynamics (CFD) simulations coupled with detailed chemistry are prohibitively expensive for design. This work develops a novel, efficient Low-dimensional Network model with Post-detonation Expansion (LNPE) approach for RDEs formulated in the wave-fixed reference frame. The model represents key flow structures in the combustor (i.e., detonation wave, oblique shock, and slip lines) as geometric discontinuities, with the intervening regions undergoing chemical reaction and isentropic expansion. Within this framework, a network of plug flow reactors (PFRs) tracks the thermochemical evolution of fluid parcels from the detonation front to the combustor exit, while the distribution of shock structures is implicitly determined by matching the thermochemical state evolution. Using high-fidelity two-dimensional simulation data as a benchmark, the present LNPE model successfully captures the flow evolution and demonstrates good accuracy in predicting distributions of temperature, pressure, velocity, and species concentrations at the RDE exit. Furthermore, the model reveals that the post-detonation expansion rapidly quenches reactions, leading to low NOx emissions compared to constant-volume or constant-pressure combustion processes. Parametric studies on hydrogen-air and methane-air mixtures demonstrate the model's capability to analyse the effects of fuel type and equivalence ratio on wave structure, energy conversion (kinetic-to-static enthalpy ratio), and pollutant formation. The proposed wave-fixed LNPE approach provides a computationally efficient tool for rapid analysis of performance and emission of RDEs, facilitating preliminary combustor design and optimization.