Large Eddy Simulation of an Asymmetric Jet in Crossflow

Folkersma, Mikko; Bodart, Julien

doi:10.1007/978-3-319-63212-4_10

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…The third dataset is the Skewed case, produced by Folkersma et al [25]. It is the same as the baseline geometry, except that the cooling hole has a compound angle of injection: inclined 30 • about the streamwise and spanwise directions (see Fig.…”

Section: Datasetsmentioning

confidence: 99%

Generalization of machine-learned turbulent heat flux models applied to film cooling flows

Milani¹,

Ling²,

Eaton³

2019

Preprint

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The design of film cooling systems relies heavily on Reynolds-Averaged Navier-Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales. Most turbulent heat flux models, which are based on isotropic diffusion with a fixed turbulent Prandtl number (Pr t ), fail to accurately predict heat transfer in film cooling flows. In the present work, machine learning models are trained to predict a non-uniform Pr t field, using various datasets as training sets. The ability of these models to generalize beyond the flows on which they were trained is explored. Furthermore, visualization techniques are employed to compare distinct datasets and to help explain the cross-validation results. NOMENCLATUREθ Dimensionless temperature u i Velocity component in the i-th direction D Hole diameter in film cooling flows BR Blowing ratio in a jet in crossflow configuration d Distance to the nearest wall k Turbulent kinetic energy ε Turbulent dissipation rate ν t Eddy viscosity calculated by realizable k − ε model [m 2 /s] α t Turbulent diffusivity [m 2 /s] Pr t Turbulent Prandtl number ν t /α t V (i) Volume of the i-th computational cell

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Section: Datasetsmentioning

confidence: 99%

Generalization of machine-learned turbulent heat flux models applied to film cooling flows

Milani¹,

Ling²,

Eaton³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The channel is also wider in the spanwise direction (8.62D Â 17.3D). The skew creates a qualitatively distinct mean velocity field: while the baseline case contains a counter-rotating vortex pair downstream of injection, the secondary flow in the skewed case is dominated by a single vortex [4,20]. The Reynolds number based on the main flow bulk velocity and hole diameter is Re D ¼ 5800 and the blowing ratio is BR ¼ 1.…”

Section: Skewed Inclined Jet In Crossflowmentioning

confidence: 99%

“…The LES of this geometry was performed by Folkersma [20]. It was a nominally compressible simulation, but since the Mach number was low (approximately 0.2), the density variations were negligible.…”

Section: Skewed Inclined Jet In Crossflowmentioning

confidence: 99%

“…At the channel inlet, a turbulent velocity profile was picked to best match the LES velocity from Ref. [20]. A slip condition was applied at the top wall, and no-slip was applied at all the other walls.…”

Section: Skewed Inclined Jet In Crossflowmentioning

confidence: 99%

“…Skewed geometry: (a) is from Folkersma[20] and contains a wall-normal plane showing the skewed hole and a spanwise plane showing dimensions and the plenum (the latter image is also valid for the baseline case); (b) shows the mesh used in the RANS simulation at the wall around the injection hole…”

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A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

Milani

Ling

Sáez-Mischlich

et al. 2017

Journal of Turbomachinery

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In film cooling flows, it is important to know the temperature distribution resulting from the interaction between a hot main flow and a cooler jet. However, current Reynoldsaveraged Navier-Stokes (RANS) models yield poor temperature predictions. A novel approach for RANS modeling of the turbulent heat flux is proposed, in which the simple gradient diffusion hypothesis (GDH) is assumed and a machine learning (ML) algorithm is used to infer an improved turbulent diffusivity field. This approach is implemented using three distinct data sets: two are used to train the model and the third is used for validation. The results show that the proposed method produces significant improvement compared to the common RANS closure, especially in the prediction of film cooling effectiveness. [DOI: 10.1115/1.4038275] Recently, the rapid increase in computational capability has led to 1 Corresponding author.

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Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows

Milani

Ling

Eaton

2019

Journal of Turbomachinery

View full text Add to dashboard Cite

The design of film cooling systems relies heavily on Reynolds-averaged Navier–Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales. Most turbulent heat flux models, which are based on isotropic diffusion with a fixed turbulent Prandtl number (Prt), fail to accurately predict heat transfer in film cooling flows. In the present work, machine learning models are trained to predict a non-uniform Prt field using various datasets as training sets. The ability of these models to generalize beyond the flows on which they were trained is explored. Furthermore, visualization techniques are employed to compare distinct datasets and to help explain the cross-validation results.

show abstract

Large Eddy Simulation of an Asymmetric Jet in Crossflow

Cited by 4 publications

References 35 publications

Generalization of machine-learned turbulent heat flux models applied to film cooling flows

Generalization of machine-learned turbulent heat flux models applied to film cooling flows

A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows

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