Investigation of URANS CFD Methods for Supersonic Hydrogen Jets 2024-01-2687
The urgent need to combat global warming has spurred legislative efforts within the transport sector to transition away from fossil fuels. Hydrogen is increasingly being utilised as a green energy vector, which can aid the decarbonisation of transport, including internal combustion engines. Computational fluid dynamics (CFD) is widely used as a tool to study and optimise combustion systems especially in combination with new fuels like hydrogen. Since the behaviour of the injection event significantly impacts combustion and emissions formation especially in direct injection applications, the accurate modelling of H2 injection is imperative for effective design of hydrogen combustion systems. This work aims to evaluate unsteady Reynolds-Averaged Navier Stokes (URANS) modelling of the advective transport process and related numerical methods. Measurements of H2 injection forming supersonic jets inside of constant volume chamber carried out at wide range of relevant conditions are utilised for validation. Investigations focused on aspects of simulated jet definition and its compatibility with Schlieren methods, cubic equations of state as well as probability of the relevant conditions inside the jet, use of adaptive mesh refinement (AMR), mesh dependency, convective flux and colocation methods, nozzle pressure ratio (NPR) effects and contribution of individual terms. The paper recommends applying molar fraction of H2 to define the jet as well as utilising Soave-Redlich-Kwong as equation of state. Mesh dependency is found to be strongly influenced by nozzle kinetic power (NKP), but not by selection of the turbulence model. Advanced convective flux schemes with flux/slope limiters and colocation generalisation can effectively reduce numerical diffusion and dispersion errors. Finally, comparison of the simulations against physical experiments has shown a good agreement across a wide range of NPRs, proving the reliability of modelling the advection.
