Adaptive scale resolving for turbulent jets using data assimilation augmented Reynolds-averaged simulations

This study proposes a turbulence model called the PaSAS–Re model which has a low computational cost. The proposed model can predict the time-averaged flow accurately and can predict the fluctuation field for turbulent jets. A data assimilation model that can accurately predict the mean flow distribution in free jets and the wall jets is used as a parent model. The scale-adaptive simulation (SAS) source term is added to the proposed model to equip it with the ability to achieve a behavior like large-eddy simulation. However, the SAS approach cannot switch to scale-resolving mode if the flow, such as a turbulent jet, does not exhibit a sufficiently strong instability. Therefore, the partially averaged Navier–Stokes (PANS) model is used in this study to generate necessary instabilities. The PANS model converts the partially modeled turbulent kinetic energy k into resolved fluctuation, which is beneficial for activating the SAS model. The proposed PaSAS–Re model is tested on free jets and impinging jets using coarse meshes to highlight its low cost. The results of time-averaged velocity and velocity fluctuation show that the best performance is achieved for fk = 0.8. The effects of the PANS approach, vortex stretching term, and SAS source term on the prediction results are analyzed and found to be important for accurately predicting and generating dynamic behavior. Finally, the simulations are conducted using impinging jets for further verification and application of the proposed model. The results show that the PaSAS–Re model is suitable to predict the time-averaged flow and flow fluctuations. Therefore, the proposed model can be used in engineering to obtain time-averaged velocity and velocity fluctuation in a low computational cost.