Zonal Detached Eddy Simulation extension to k-ω models

Uribe, Cédric; Marty, Julien; Gerolymos, G. A.

doi:10.2514/6.2017-4279

Cited by 2 publications

(5 citation statements)

References 71 publications

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“…Two others, named ZDES-SST and ZDES-BSL hereafter, correspond to a formulation of ZDES based on the k − ω turbulence model of Menter [25], respectively with (ZDES-SST) and without (ZDES-BSL) the Shear Stress Transport correction proposed by Menter [25] (BSL for BaSeLine version of the model). The formulation of ZDES-SST relies on the proposition of Strelets [34] for DES approach, and is detailed by Uribe et al [23]. It must be pointed out that in this previous work the ZDES-SA and ZDES-SST approaches were assessed on three generic flow test cases (a mixing layer, a backward facing step and a low Reynolds circular cylinder flow) without revealing any significant discrepancy between their predictions.…”

Section: Zonal Detached Eddy Simulationmentioning

confidence: 99%

“…Hence comes the ZDES-BSL approach. Let's note that, as other limiters, the SST correction is only applied when the ZDES-SST works in a RANS behavior (despite an equation misprint in [23] indicating the opposite). Neither vorticity based production terms nor vorticity based SST correction are used.…”

Section: Zonal Detached Eddy Simulationmentioning

confidence: 99%

“…The RANS length scale (L RANS ) of the turbulence model is expressed (if not obvious as the wall distance d w for ZDES-SA) thanks to turbulent variables (as the √ k β ω ratio for ZDES-SST and ZDES-BSL) and its appearances are identified in the equations. Some of them are replaced by an hybrid length scale (L DES ), classically in destruction term of the first turbulent variable, as here, but several choices have been proposed [23]. In Table 2 f w andS are SA model terms relative to wall dumping functions.…”

Section: Zonal Detached Eddy Simulationmentioning

confidence: 99%

“…This remains to be carried out with ZDES method. Although this approach is historically based on Spalart-Allmaras RANS model, recent developments allow the use of other RANS turbulence models based on the Boussinesq hypothesis [23]. Moreover, in studies dealing with (D)DES methods applied to turbomachinery flows and mentioned earlier, the authors focused more on validation and comprehension of flow physics than on issues a to c .…”

Section: Introductionmentioning

confidence: 99%

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Impact of Underlying RANS Turbulence Models in Zonal Detached Eddy Simulation: Application to a Compressor Rotor

Marty

Uribe

2020

IJTPP

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The present study focuses on the impact of the underlying RANS turbulence model in the Zonal Detached Eddy Simulation (ZDES) method when used for secondary flow prediction. This is carried out in light of three issues commonly investigated for hybrid RANS/LES methods (detection and protection of attached boundary layer, emergence, and growth of resolved turbulent fluctuations and accurate prediction of separation front due to progressive adverse pressure gradient). The studied configuration is the first rotor of a high pressure compressor. Three different turbulence modelings (Spalart and Allmaras model (SA), Menter model with (SST) and without (BSL) shear stress correction) are assessed as ZDES underlying turbulence model and also as turbulence model of unsteady RANS simulations. Whatever the underlying turbulence model, the ZDES behaves well with respect to the first two issues as the boundary layers appear effectively shielded and the RANS-to-LES switch is close downstream of trailing edges and separation fronts leading to a quick LES treatment of wakes and shear layers. Both tip leakage and corner flows are strongly influenced by the Navier–Stokes resolution approach (unsteady RANS vs. ZDES) but the underlying turbulence modelling (SA vs. SST vs. BSL) impacts mainly the junction flow near the hub for both approaches. ZDES underlying turbulence model choice appear essential since it leads to quite different corner flow separation topologies and so to inversion of the downstream stagnation pressure radial gradient.

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Section: Zonal Detached Eddy Simulationmentioning

confidence: 99%

Section: Zonal Detached Eddy Simulationmentioning

confidence: 99%

Section: Zonal Detached Eddy Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Underlying RANS Turbulence Models in Zonal Detached Eddy Simulation: Application to a Compressor Rotor

Marty

Uribe

2020

IJTPP

Self Cite

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“…The comparison of the two meshes shows that with the first mesh, the SAS-DRSM technique is able to capture the same PSD as the second mesh up to the ninth harmonics (on the same mesh, the discrepancy between ZDES and SAS-DRSM arises at the 20 th harmonics) while the first mesh is ten times smaller (4.3 million points vs 44 million points per blade passage). This means also that the PSD prediction could be im-proved by using a more elaborated RANS model (DRSM) in the ZDES framework [37,38].…”

Section: Spectral Analysismentioning

confidence: 99%

Assessment of Scale Adaptive Simulation of a Rotor of High Pressure Compressor

Marty

Gaible

Bézard

2018

Volume 2C: Turbomachinery

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The main design objectives of a high pressure compressor are the aerodynamic efficiency and the operating range (e.g. the surge margin). Those quantities are impacted by secondary and leakage flows occurring in the blade passage such as corner separation or stall and tip leakage flows. The turbulence modeling influences strongly the prediction of the overall performances. The aims of the present study were (i) the validation of the combination of the SAS approach with the DRSM turbulence model by comparison to experimental data, especially to laser measurements in the tip of a rotor of a high pressure compressor and (ii) the discussion of the flow prediction improvements with respect to turbulence approaches classically used in CFD and industry: URANS simulations and standard SAS simulation i.e. combined with SST turbulence model. The SAS results are compared to experimental data and to URANS results (SST and DRSM). Only the simulations with IGV wakes predict the velocity fluctuations near tip gap, from the leading edge. Concerning the time-averaged performances, the stagnation pressure losses are slightly overestimated by SAS, especially with DRSM model. This is due to an amplification of the hub corner separation. Moreover, the isentropic efficiency is very sensitive to the SAS approach and to the turbulence model. The spectral analysis shows that the prediction of the amplitude and frequencies of the power spectral density of static pressure is improved using the SAS approach instead of URANS one. The SAS approach leads to PSD similar to ZDES, especially with the DRSM model. Thus, the SAS-DRSM is able to well predict the tip leakage flow with the fine mesh. Nevertheless, this approach amplifies the hub corner separation leading to a strong underestimation of overall performances.

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Zonal Detached Eddy Simulation extension to k-ω models

Cited by 2 publications

References 71 publications

Impact of Underlying RANS Turbulence Models in Zonal Detached Eddy Simulation: Application to a Compressor Rotor

Impact of Underlying RANS Turbulence Models in Zonal Detached Eddy Simulation: Application to a Compressor Rotor

Assessment of Scale Adaptive Simulation of a Rotor of High Pressure Compressor

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