RNG k-ε closure turbulence dissipation equations are evaluated employing the CFD code KIVA-3V Release 2. The numerical evaluations start by considering simple jet flows, including incompressible air jets and compressible helium jets. The results show that the RNG closure turbulence model predicts lower jet tip penetration than the "standard" k-ε model, as well as being lower than experimental data. The reason is found to be that the turbulence kinetic energy is dissipated too slowly in the downstream region near the jet nozzle exit. In this case, the over-predicted R term in RNG model becomes a sink of dissipation in the ε-equation. As a second step, the RNG turbulence closure dissipation models are further tested in complex engine flows to compare against the measured evolution of turbulence kinetic energy, and an estimate of its dissipation rate, during both the compression and expansion processes. In this case the turbulence energy is also over-predicted, because the turbulence model is not sufficiently dissipative. To improve predictions of the current RNG turbulence model, possible optimization approaches are explored. In particular, a generalized RNG closure model based on the "dimensionality" of the flow strain rate is proposed. When the generalized RNG model is tested in jet flows, the predicted tip penetrations match the experimental data well. The predicted turbulence energy of the engine flow is also improved significantly. Additionally, available direct numerical simulation (DNS) results are employed to support the continued use current RANS turbulence models, and to suggest strategies for improving the model coefficients. Two special compression cases are investigated, one is 3-D spherical compression, and the other is 1-D unidirectional compression.
Assessment of RNG Turbulence Modeling and the Development of a Generalized RNG Closure Model
Sae Technical Papers
SAE 2011 World Congress & Exhibition ; 2011
2011-04-12
Aufsatz (Konferenz)
Englisch
Assessment of RNG turbulence modeling and the development of a generalized RNG closure model
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