Implicit algebraic model for predicting turbulent heat flux in film cooling flow

2009 ◽  
Vol 64 (5) ◽  
pp. 517-531 ◽  
Author(s):  
Mehran Rajabi-Zargarabadi ◽  
Farzad Bazdidi-Tehrani
Keyword(s):  
Heat Flux ◽  
Film Cooling ◽  
Algebraic Model ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Cooling Flow

Experimental and Numerical Investigation of Turbulent Mixing in Film Cooling Applications

2017 ◽  
Author(s):  
Michael Straußwald ◽  
Karin Schmid ◽  
Hagen Müller ◽  
Michael Pfitzner
Keyword(s):  
Heat Flux ◽  
Film Cooling ◽  
Density Ratio ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Flow Dynamics ◽  
Frame Rate ◽  
Experimental Investigations ◽  
Turbulent Heat ◽  
Future Design

Fundamental knowledge on the flow dynamics and in particular the turbulent heat flux in film cooling flows is essential for the future design process of efficient cooling geometries. Thermographic PIV has been used to measure temperature and velocity fields in flows emanating from cylindrical effusion holes simultaneously. The measurements were carried out in a closed-loop, heated wind tunnel facility at a repetition rate of 6 kHz. Due to the high frame rate of the measurements, the unsteady flow dynamics could be resolved. For a density ratio of DR = 1.6 and a momentum ratio of I = 8, the jet ejected from the cylindrical effusion hole lifts off the surface. From the instantaneous measurements it could be observed that pockets of hot air are entrained into the coolant forcing the relatively fast cooling air to dodge the slow main flow air. These shear layer fluctuations result in turbulent heat fluxes that do not follow the gradient diffusion hypothesis which is often used in RANS models. In addition to these experimental investigations, numerical results from RANS simulations with the k-ω-SST turbulence model are presented that were carried out as basis for future investigations on turbulent heat flux modeling.

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

2019 ◽  
Author(s):  
Pedro M. Milani ◽  
Julia Ling ◽  
John K. Eaton
Keyword(s):  
Heat Flux ◽  
Film Cooling ◽  
Cross Validation ◽  
Turbulent Heat Flux ◽  
Navier Stokes ◽  
Turbulent Heat ◽  
Turbulent Prandtl Number ◽  
Cooling Systems ◽  
Cooling Flows ◽  
Visualization Techniques

Abstract 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.

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

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Pedro M. Milani ◽  
Julia Ling ◽  
John K. Eaton
Keyword(s):  
Heat Flux ◽  
Film Cooling ◽  
Cross Validation ◽  
Turbulent Heat Flux ◽  
Navier Stokes ◽  
Turbulent Heat ◽  
Turbulent Prandtl Number ◽  
Cooling Systems ◽  
Cooling Flows ◽  
Visualization Techniques

Abstract 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.

Extension of equilibrium turbulent heat flux models to high-speed shear flows

2009 ◽  
Vol 633 ◽  
pp. 61-70 ◽  
Author(s):  
RODNEY D. W. BOWERSOX
Keyword(s):  
Heat Flux ◽  
Prandtl Number ◽  
High Speed ◽  
Shear Flows ◽  
Present Model ◽  
Algebraic Model ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Governing Parameter ◽  
Simple Modification

An algebraic heat flux truncation model was derived for high-speed gaseous shear flows. The model was developed for high-temperature gases with caloric imperfections. Fluctuating dilatation moments were modelled via conservation of mass truncations. The present model provided significant improvements, up to 20%, in the temperature predictions over the gradient diffusion model for a Mach number ranging from 0.02 to 11.8. Analyses also showed that the near-wall dependence of the algebraic model agreed with expected scaling, where the constant Prandtl number model did not. This led to a simple modification of the turbulent Prandtl number model. Compressibility led to an explicit pressure gradient dependency with the present model. Analyses of a governing parameter indicated that these terms are negligibly small for low speeds. However, they may be important for high-speed flow.

Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow

2020 ◽  
Author(s):  
Fabíola Paula Costa ◽  
Rubén Bruno Díaz ◽  
Pedro M. Milani ◽  
Jesuíno Takachi Tomita ◽  
Cleverson Bringhenti
Keyword(s):  
Machine Learning ◽  
Heat Flux ◽  
Prandtl Number ◽  
Film Cooling ◽  
Computational Cost ◽  
Learning Model ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Turbulent Prandtl Number ◽  
Machine Learning Model

Abstract Film cooling is an important technique to ensure safe operation and performance fulfillment of turbines. Its ultimate goal is to protect the axial turbine blades from high gas temperatures. An appropriate study is necessary in order to obtain a reliable representation of the flow characteristics involved in such phenomena. Because of the high computational cost of high-fidelity simulations, the low-fidelity simulation method Reynolds Averaged Navier Stokes (RANS) is commonly used in practical configurations. However, the majority of the current turbulent heat flux models fail to accurately predict heat transfer in film cooling flows. Recent work suggests the use of machine learning models to improve turbulent closure in these flows. In the present work, a machine learning model for spatially varying turbulent Prandtl number previously described in the literature is applied to a transverse film cooling flow consisting of a jet square channel. The results obtained in the present work were compared to adiabatic effectiveness experimental data available in the literature to assess the performance of the machine learning model. The results shown that for low blowing ratios (BR = 0.2 and BR = 0.4) the proposed machine learning model has poor performance. However, for the case with the highest blowing ratio (BR = 0.8), the proposed model presented better results. These results are then explained in terms of the resulting turbulent Prandtl number field and suggest that the training set is not appropriate for capturing the turbulent heat flux in fully attached jets in crossflow.

Fluid Mechanics and Heat Transfer Measurements in Transitional Boundary Layers Conditionally Sampled on Intermittency

1994 ◽  
Vol 116 (3) ◽  
pp. 405-416 ◽  
Author(s):  
J. Kim ◽  
T. W. Simon ◽  
M. Kestoras
Keyword(s):  
Heat Flux ◽  
Plate Boundary ◽  
Turbulent Heat Flux ◽  
Transitional Flow ◽  
Turbulent Heat ◽  
Mean Velocity ◽  
Power Spectral ◽  
Turbulent Characteristics ◽  
Transition Models ◽  
Prandtl Numbers

An experimental investigation of transition on a flat-plate boundary layer was performed. Mean and turbulence quantities, including turbulent heat flux, were sampled according to the intermittency function. Such sampling allows segregation of the signal into two types of behavior—laminarlike and turbulentlike. Results show that during transition these two types of behavior cannot be thought of as separate Blasius and fully turbulent profiles, respectively. Thus, simple transition models in which the desired quantity is assumed to be an average, weighted on intermittency, of the laminar and fully turbulent values may not be entirely successful. Deviation of the flow identified as laminarlike from theoretical laminar behavior is due to a slow recovery after the passage of a turbulent spot, while deviation of the flow identified as turbulentlike from fully turbulent characteristics is possibly due to an incomplete establishment of the fully turbulent power spectral distribution. Measurements were taken for two levels of free-stream disturbance—0.32 and 1.79 percent. Turbulent Prandtl numbers for the transitional flow, computed from measured shear stress, turbulent heat flux, and mean velocity and temperature profiles, were less than unity.

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