scholarly journals RANS predictions of trailing-edge slot flows using heat-flux closures developed with CFD-driven machine learning

2021 ◽  
pp. 1-13
Author(s):  
Chitrarth Lav ◽  
Ali Haghiri ◽  
Richard Sandberg
Keyword(s):  
Machine Learning ◽  
Heat Flux ◽  
Temperature Field ◽  
Wall Temperature ◽  
Thermal Field ◽  
Turbine Blades ◽  
Turbulent Heat Flux ◽  
High Fidelity ◽  
Turbulent Heat ◽  
Trailing Edge

Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbine blades that can withstand the harsh aerothermal environment in a modern gas turbine. Because of their computational efficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite their known underprediction of wall temperature. In this paper, a novel framework using a branch of machine learning, geneexpression programming (GEP) [Zhao et al. 2020, J. Comp. Physics, 411:109413] is used to develop closures for the turbulent heat-flux to improve upon this underprediction. In the original use of GEP (“frozen” approach), the turbulent heat-flux from a high-fidelity database was used to evaluate the fitness of the candidate closures during the symbolic regression, however, the resulting closure had no information of the temperature field during the optimisation process. In this work, the regression process of the GEP instead incorporates RANS calculations to evaluate the fitness of the candidate closures. This allows the inclusion of the temperature field from RANS to advance the iterative regression, leading to a more integrated heat-flux closure development, and consequently more accurate and robust models. The GEP-based CFD-driven framework is demonstrated on a trailing edge slot configuration with three blowing ratios. Full a posteriori predictions from the new closures are compared to high-fidelity reference data and both conventional RANS closures and closures obtained from the “frozen” approach.

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.

An explicit algebraic Reynolds-stress and scalar-flux model for stably stratified flows

2013 ◽  
Vol 723 ◽  
pp. 91-125 ◽  
Author(s):  
W. M. J. Lazeroms ◽  
G. Brethouwer ◽  
S. Wallin ◽  
A. V. Johansson
Keyword(s):  
Heat Flux ◽  
Kinetic Energy ◽  
Temperature Gradient ◽  
Reynolds Stress ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Homogeneous Shear ◽  
Self Consistent ◽  
The Mean ◽  
Homogeneous Shear Flow

AbstractThis work describes the derivation of an algebraic model for the Reynolds stresses and turbulent heat flux in stably stratified turbulent flows, which are mutually coupled for this type of flow. For general two-dimensional mean flows, we present a correct way of expressing the Reynolds-stress anisotropy and the (normalized) turbulent heat flux as tensorial combinations of the mean strain rate, the mean rotation rate, the mean temperature gradient and gravity. A system of linear equations is derived for the coefficients in these expansions, which can easily be solved with computer algebra software for a specific choice of the model constants. The general model is simplified in the case of parallel mean shear flows where the temperature gradient is aligned with gravity. For this case, fully explicit and coupled expressions for the Reynolds-stress tensor and heat-flux vector are given. A self-consistent derivation of this model would, however, require finding a root of a polynomial equation of sixth-order, for which no simple analytical expression exists. Therefore, the nonlinear part of the algebraic equations is modelled through an approximation that is close to the consistent formulation. By using the framework of a$K\text{{\ndash}} \omega $model (where$K$is turbulent kinetic energy and$\omega $an inverse time scale) and, where needed, near-wall corrections, the model is applied to homogeneous shear flow and turbulent channel flow, both with stable stratification. For the case of homogeneous shear flow, the model predicts a critical Richardson number of 0.25 above which the turbulent kinetic energy decays to zero. The channel-flow results agree well with DNS data. Furthermore, the model is shown to be robust and approximately self-consistent. It also fulfils the requirements of realizability.

scholarly journals Air–Sea Turbulent Heat Fluxes in Climate Models and Observational Analyses: What Drives Their Variability?

2019 ◽  
Vol 32 (8) ◽  
pp. 2397-2421 ◽  
Author(s):  
R. Justin Small ◽  
Frank O. Bryan ◽  
Stuart P. Bishop ◽  
Robert A. Tomas
Keyword(s):  
Heat Flux ◽  
Climate Models ◽  
Climate Model ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Small Scale ◽  
Turbulent Heat ◽  
Realistic Representation ◽  
Frontal Zones ◽  
The Tropics

Abstract A traditional view is that the ocean outside of the tropics responds passively to atmosphere forcing, which implies that air–sea heat fluxes are mainly driven by atmosphere variability. This paper tests this viewpoint using state-of-the-art air–sea turbulent heat flux observational analyses and a climate model run at different resolutions. It is found that in midlatitude ocean frontal zones the variability of air–sea heat fluxes is not predominantly driven by the atmosphere variations but instead is forced by sea surface temperature (SST) variations arising from intrinsic oceanic variability. Meanwhile in most of the tropics and subtropics wind is the dominant driver of heat flux variability, and atmosphere humidity is mainly important in higher latitudes. The predominance of ocean forcing of heat fluxes found in frontal regions occurs on scales of around 700 km or less. Spatially smoothing the data to larger scales results in the traditional atmosphere-driving case, while filtering to retain only small scales of 5° or less leads to ocean forcing of heat fluxes over most of the globe. All observational analyses examined (1° OAFlux; 0.25° J-OFURO3; 0.25° SeaFlux) show this general behavior. A standard resolution (1°) climate model fails to reproduce the midlatitude, small-scale ocean forcing of heat flux: refining the ocean grid to resolve eddies (0.1°) gives a more realistic representation of ocean forcing but the variability of both SST and of heat flux is too high compared to observational analyses.

Investigation of thermal dispersion and intra-pore turbulent heat flux in porous media

2020 ◽  
Vol 81 ◽  
pp. 108523
Author(s):  
Nima Fallah Jouybari ◽  
T. Staffan Lundström ◽  
J.Gunnar I. Hellström
Keyword(s):  
Heat Flux ◽  
Porous Media ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Thermal Dispersion
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