Assessment of turbulence model predictions for a centrifugal compressor simulation

Lee Gibson, Lee Galloway, Sung in Kim, Stephen Spence
2017 Journal of the Global Power and Propulsion Society  
Steady-state computational fluid dynamics (CFD) simulations are an essential tool in the design process of centrifugal compressors. Whilst global parameters, such as pressure ratio and efficiency, can be predicted with reasonable accuracy, the accurate prediction of detailed compressor flow fields is a much more significant challenge. Much of the inaccuracy is associated with the incorrect selection of turbulence model. The need for a quick turnaround in simulations during the design
more » ... n process also demands that the turbulence model selected be robust and numerically stable with short simulation times. In order to assess the accuracy of a number of turbulence model predictions, the current study used an exemplar open test case, the centrifugal compressor "Radiver", to compare the results of three eddy-viscosity models and two Reynolds stress type models. The turbulence models investigated in this study were: (i) Spalart-Allmaras (SA), (ii) Shear Stress Transport (SST), (iii) a modification to the SST model denoted the SST-curvature correction (SST-CC), (iv) Reynolds stress model of Speziale, Sarkar and Gatski (RSM-SSG), and (v) the turbulence frequency formulated Reynolds stress model (RSM-ω). Each was found to be in good agreement with the experiments (below 2% discrepancy), with respect to total-to-total parameters at three different operating conditions. However, for the near surge operating point P1, local flow field differences were observed between the models, with the SA model showing particularly poor prediction of local flow structures. The SST-CC showed better prediction of curved rotating flows in the impeller. The RSM-ω was better for the wake and separated flow in the diffuser. The SST model showed reasonably stable, robust and time efficient capability to predict global performance and local flow features.
doi:10.22261/2ii890 fatcat:zpgroq5bpvhgzoca4ycxnn6mz4