LES Investigation of Boussinesq Constitutive Relation Validity in a Corner Separation Flow

Monier, Jean-François; Poujol, Nicolas; Laurent, Mathieu; Gao, Feng; Boudet, Jérôme; Aubert, Stéphane; Shao, Lei

doi:10.1115/gt2018-75792

Cited by 5 publications

(3 citation statements)

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“…Weatheritt et al [7] found the validity of the Boussinesq approximation to be poor in the wake behind a low pressure turbine cascade and proposed a more complex relationship between τ i j and S i j to replace the Boussinesq approximation with, improving alignment of the two tensors. Monier et al [8] applied a similar methodology to a compressor corner separation using LES data.…”

Section: Accepted Manuscript N O T C O P Y E D I T E Dmentioning

confidence: 99%

Importance of Nonequilibrium Modeling for Compressors

Spencer

Przytarski

Adami

et al. 2023

Journal of Turbomachinery

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This paper investigates the importance of non-equilibrium boundary layer modelling for three compressor blade geometries, using RANS and high fidelity simulations. We find that capturing non-equilibrium effects in RANS is crucial to capturing the correct boundary-layer loss. This is because the production of turbulence within the non-equilibrium region affects both the loss generation in the non-equilibrium region, but also the final equilibrium state. We show that capturing the correct non-equilibrium behaviour is possible by adapting industry standard models (in this case the k-omega SST model). We show that for the range of cases studied here, non-equilibrium effects can modify the trailing-edge momentum thickness by up to 40 percent, and can change the trailing-edge shape factor from 1.8 to 2.1.

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Section: Accepted Manuscript N O T C O P Y E D I T E Dmentioning

confidence: 99%

Importance of Nonequilibrium Modeling for Compressors

Spencer

Przytarski

Adami

et al. 2023

Journal of Turbomachinery

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“…Dandois et al 11 applied QCR to their in-house solver elsA and calculated the separation at the wing-body junction and in the rectangular duct with shock-induced separation, respectively, showing that QCR can significantly improve the fidelity of the flow simulation at the geometric junction without deteriorating the accuracy of the simulation elsewhere. Monier et al 17 assessed the applicability of the Boussinesq constitutive relation for corner separation flow in a compressor cascade using LES-calculated data as a reference. The investigation demonstrated that the incorporation of the QCR correction improved the relationship between the mean strain rate tensor and the Reynolds stress tensor for corner separation at the inlet and upstream.…”

Section: Introductionmentioning

confidence: 99%

Validation and analyses of QCR correction turbulence model in sub-/super-sonic inner flows

Wang,

Gu,

Zhang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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The inlet, isolation section, and other internal flow components are important parts of the aircraft propulsion system. Their performances affect the stability of the entire propulsion system. According to those components, complicated shock wave/boundary layer interaction (SBLI), flow separation, and secondary flow phenomena would occur. The commonly used turbulence models, SA and SST, cannot predict the anisotropy of turbulence. This deficiency makes the calculated results differ significantly from the experimental results and cannot accurately predict their aerodynamic performance. This paper validates the feasibility and effectiveness of the turbulence models based on quadratic constitutive relation (QCR) correction applied to the flow of square duct, compression corners, diffusing 3D S-Duct, and axisymmetric cylindrical isolator. This can support future calculations of complex flow fields with flow separation and secondary flow phenomena in the subsonic or supersonic inlet. The results show that the turbulence model with QCR correction is better than the original turbulence model. Among them, the SA-QCR2020 turbulence model is the best, which is able to predict the presence of secondary flows and large boundary layer separated flows well.

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“…Yet, Franceschini et al [24] recently showed, in the context of mean-flow data-assimilation, that the Boussinesq approximation could induce rigid models, that were able to only approximately reconstruct, through DA, simple mean-flows, such as the one obtained in backward-facing steps which exhibit separation and pressure gradients. In simple shear-dominated flows, the approximation is in fact known to be "effective" (turbulence essentially enhances mixing in an isotropic way, which is compatible with a linear constitutive relation), but actually not even strictly valid (Monier et al [25] showed that the Reynolds stresses were only roughly aligned with the local velocity gradient in turbulent boundary-layers). In view of drastically improving the fidelity of RANS computations, it therefore seems reasonable to lift this constraint in the modeling of the Reynolds stresses.…”

mentioning

confidence: 99%

Machine learning-augmented turbulence modeling for RANS simulations of massively separated flows

et al. 2021

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Most widely used Reynolds-Averaged Navier-Stokes (RANS) models employ the Boussinesq approximation, which assumes a linear relationship between the turbulent Reynolds stresses and the mean-velocity gradient tensor. This assumption, which can be very stringent, is more suited for simple shear-flows and is regarded as an important shortcoming for the improvement of the representation of turbulence in complex geometries. Correction of the local turbulence length scales, as achieved for example by the introduction of a correction term in the eddy-viscosity equation of a Spalart-Allmaras model, then indeed only allows relatively small corrections to the base-line model and may not be sufficiently flexible to adapt to common situations where the Reynolds stresses are not aligned with the velocity gradient tensor. For the variational data-assimilation step, we consider a vectorial source correction term in the momentum equations (output quantity) together with the Spalart-Allmaras model, which allows full flexibility to adapt to any mean-flow topology. We show how the machine-learning framework should be adapted, in particular with respect to the vectorial nature of the correction term and given invariance properties. As for the input quantities, we discuss the impact of considering either local quantities for the non-dimensionalization or global ones that characterize the configuration. We showcase the procedure on the periodic hill configuration, for which a rich DNS database is available in the literature: in particular, availability of the full mean-flow solution for a range of Reynolds numbers and geometries, ensures identification of "physical" correction terms and allows learning of turbulence models that accurately extrapolate to new Reynolds numbers and geometries.

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LES Investigation of Boussinesq Constitutive Relation Validity in a Corner Separation Flow

Cited by 5 publications

References 0 publications

Importance of Nonequilibrium Modeling for Compressors

Importance of Nonequilibrium Modeling for Compressors

Validation and analyses of QCR correction turbulence model in sub-/super-sonic inner flows

Machine learning-augmented turbulence modeling for RANS simulations of massively separated flows

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