Turbulent Combustion Properties Behind a Confined Conical Stabilizer

1992 ◽  
Vol 114 (1) ◽  
pp. 33-38 ◽  
Author(s):  
J. C. Pan ◽  
W. J. Schmoll ◽  
D. R. Ballal
Keyword(s):  
Recirculation Zone ◽  
Mixing Layer ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Reynolds Stresses ◽  
Streamline Curvature ◽  
Gradient Diffusion ◽  
Combustion Properties ◽  
Strain Interaction ◽  
Mean Strain

Turbulence properties were investigated in and around the recirculation zone produced by a 45 deg conical flame stabilizer of 25 percent blockage ratio confined in a pipe supplied with a turbulent premixed methane-air mixture at a Reynolds number of 5.7×104. A three-component LDA system was used for measuring mean velocities, turbulence intensities, Reynolds stresses, skewness, kurtosis, and turbulent kinetic energy. It was found that wall confinement elongates the recirculation zone by accelerating the flow and narrows it by preventing mean streamline curvature. For confined flames, turbulence production is mainly due to shear stress-mean strain interaction. In the region of maximum recirculation zone width and around the stagnation point, the outer stretched flame resembles a normal mixing layer and gradient-diffusion closure for velocity holds. However, and in the absence of turbulent heat flux data, countergradient diffusion cannot be ruled out. Finally, and because of the suppression of mean streamline curvature by confinement, in combusting flow, the production of turbulence is only up to 33 percent of its damping due to dilatation and dissipation.

scholarly journals Turbulent Combustion Properties Behind a Confined Conical Stabilizer

1990 ◽  
Author(s):  
J. C. Pan ◽  
W. J. Schmoll ◽  
D. R. Ballal
Keyword(s):  
Recirculation Zone ◽  
Mixing Layer ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Reynolds Stresses ◽  
Streamline Curvature ◽  
Gradient Diffusion ◽  
Combustion Properties ◽  
Strain Interaction ◽  
Mean Strain

Turbulence properties were investigated in and around the recirculation zone produced by a 45° conical flame stabilizer of 25% blockage ratio confined in a pipe supplied with a turbulent premixed methane-air mixture at a Reynolds number of 5.7 × 104. A three-component LDA system was used for measuring mean velocities, turbulence intensities, Reynolds stresses, skewness, kurtosis, and turbulent kinetic energy. It was found that wall confinement elongates the recirculation zone by accelerating the flow and narrows it by preventing mean streamline curvature. For confined flames, turbulence production is mainly due to shear stress-mean strain interaction. In the region of maximum recirculation zone width and around the stagnation point, the outer stretched flame resembles a normal mixing layer and gradient-diffusion closure for velocity holds. However, and in the absence of turbulent heat flux data, counter-gradient diffusion cannot be ruled out. Finally, and because of the suppression of mean streamline curvature by confinement, in combusting flow, the production of turbulence is only up to 33% of its damping due to dilatation and dissipation.

Turbulent Computations of Internal Flows Using an Anisotropic Diffusivity Model

1997 ◽  
Author(s):  
E. Laroche
Keyword(s):  
Wide Spectrum ◽  
Mixing Layer ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Reynolds Stresses ◽  
Generalized Gradient ◽  
Internal Flows ◽  
Reynolds Analogy ◽  
Gradient Diffusion ◽  
The Difference

The Reynolds analogy is known to give inadequate results for strongly anisotropic turbulence. The use of an anisotropic diffusivity, through the Generalized Gradient Diffusion Hypothesis (GGDH), can partially overcome these deficiencies. The quality of the heat transfer prediction then relies on the accuracy of the Reynolds stresses estimation, which requires a second order closure. The article presents applications of an Algebraic Stress Model (ASM) coupled with a GGDH to the computations of various internal flows. The computations were done using the NS3D MATHILDA code, developed at ONERA, and largely used in the French aerospace industry. The study compares the ASM+GGDH results with experimental measurements for heated configurations such as a pipe, a rotor/stator cavity and a channel with ribs, ie covering a wide spectrum of turbine gas internal flows. The ASM+GGDH model leads to major improvements for the latter case, where the wall fluxes were largely underestimated in a standard k-ε calculation. The difference is due to an overestimation of the turbulent Prandtl number, closer to 0.5 for a mixing layer. The ASM+GGDH model also gives a correct hierarchy of the turbulent heat fluxes for pipe flows, contrary to a standard isotropic model. Concerning the rotor/stator cavity the results are in good agreement with the measurements provided by Owen et al [1].

Experimental and computational investigations of flow dynamics in LPP combustor

2017 ◽  
Vol 121 (1240) ◽  
pp. 790-802 ◽  
Author(s):  
Y. W. YAN ◽  
Y. P. Liu ◽  
Y. C. Liu ◽  
J. H. Li
Keyword(s):  
Recirculation Zone ◽  
Swirling Flow ◽  
Mixing Layer ◽  
Experimental Tests ◽  
Flow Dynamics ◽  
Reynolds Stresses ◽  
Lean Combustion ◽  
Low Emission ◽  
Image Velocimetry ◽  
Combustion Technology

ABSTRACTA Lean Premixed Prevaporised (LPP) low-emission combustor with a staged lean combustion technology was developed. In order to study cold-flow dynamics in the LPP combustor, both experimental tests using the particle image velocimetry (PIV) to quantify the flow dynamics and numerical simulation using the commercial software (FLUENT) were conducted, respectively. Numerical results were in good agreement with the experimental data. It is shown from the observation of the results that: there is a Primary Recirculation Zone (PRZ), a Corner Recirculation Zone (CRZ) and a Lip Recirculation Zone (LRZ) in the LPP combustor, and the exchanges of mass, momentum and energy between pilot swirling flow and primary swirling flow are contributed by the velocity gradients, and the shear flow is transformed into a mixing layer exhibiting the higher Reynolds stresses, which suggests the mixing process is strictly affected by the Reynolds stresses.

LES Informed Data-Driven Modelling of a Spatially Varying Turbulent Diffusivity Coefficient in Film Cooling Flows

2020 ◽  
Author(s):  
Christopher D. Ellis ◽  
Hao Xia ◽  
Gary J. Page
Keyword(s):  
Random Forests ◽  
Film Cooling ◽  
Turbulent Heat Flux ◽  
Data Driven ◽  
Turbulent Heat ◽  
Diffusivity Coefficient ◽  
Turbulent Diffusivity ◽  
Cooling Flows ◽  
Gradient Diffusion ◽  
Spatially Varying

Abstract A novel data-driven approach is used to describe a spatially varying turbulent diffusivity coefficient for the Higher Order Generalised Gradient Diffusion Hypothesis (HOGGDH) closure of the turbulent heat flux to improve upon RANS cooling predictions in film cooling flows. Machine learning algorithms are trained on two film cooling flows and tested on a case of a different density and blowing ratio. The Random Forests and Neural Network algorithms successfully reproduced the LES described coefficient and the magnitude of the turbulent heat flux vector. The Random Forests model was implemented in a steady RANS solver with a k-ω SST turbulence model and applied to four cases. All cases saw improvements in the predicted Adiabatic Cooling Effectiveness (ACE) over the cooled surface compared to the standard Gradient Diffusion Hypothesis (GDH) approach, but only minor improvements in the centreline and lateral spread are seen compared to a HOGGDH model with a constant cθ of 0.6. Further improvements to cooling predictions are highlighted by extending these data-driven approaches into turbulence modelling to improve flow field predictions.

Budgets of Reynolds Stresses and Turbulent Heat Flux for Hypersonic Turbulent Boundary Layers Subject to Pressure Gradients

2022 ◽  
Author(s):  
Gary L. Nicholson ◽  
Junji Huang ◽  
Lian Duan ◽  
Meelan M. Choudhari ◽  
Bryan Morreale ◽  
...  
Keyword(s):  
Heat Flux ◽  
Boundary Layers ◽  
Turbulent Boundary Layers ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Pressure Gradients ◽  
Reynolds Stresses

The Effect of Buoyancy on the Turbulence Structure of Vertical Round Jets

1978 ◽  
Vol 100 (4) ◽  
pp. 659-664 ◽  
Author(s):  
F. Tamanini
Keyword(s):  
Strain Tensor ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Turbulence Structure ◽  
Reynolds Stresses ◽  
Buoyant Jet ◽  
Buoyant Flows ◽  
Round Jets ◽  
The Mean ◽  
Rate Of Dissipation

The paper presents an application of the algebraic stress modeling (ASM) technique to the prediction of the flow in a turbulent round buoyant jet. In the ASM approach, algebraic formulas are obtained for the Reynolds stresses, uiuj, and for the components of the turbulent heat flux, tui. In the model used here, transport equations are solved for the turbulence kinetic energy, k, its dissipation, ε, and the mean square temperature fluctuations, g. The study shows that buoyancy increases the rate of dissipation of g above the values indicated by previous recommendations for the modeling of that quantity. As a possible explanation for this result it is suggested that buoyancy introduces anisotropy in the fluctuations at the dissipation scale. The study shows that the contribution from the secondary components of the strain tensor to the production of k is non-negligible. In addition, between 12 and 17 percent of the longitudinal enthalpy flux is contributed by the turbulent fluctuations. Finally, it is observed that the modeling of buoyant flows still presents uncertainties and that additional work is necessary to properly account for the effect of buoyancy on the production of ε and the dissipation of g.

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

2018 ◽  
Author(s):  
Pedro M. Milani ◽  
Julia Ling ◽  
John K. Eaton
Keyword(s):  
Machine Learning ◽  
Film Cooling ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Cooling Flows ◽  
Gradient Diffusion ◽  
Machine Learning Approach ◽  
Accurate Temperature ◽  
Injection Region ◽  
Model Features

Current turbulent heat flux models fail to predict accurate temperature distributions in film cooling flows. The present paper focuses on a machine learning approach to this problem, in which the Gradient Diffusion Hypothesis (GDH) is used in conjunction with a data-driven prediction for the turbulent diffusivity field αt. An overview of the model is presented, followed by validation against two film cooling datasets. Despite insufficiencies, the model shows some improvement in the near-injection region. The present work also attempts to interpret the complex machine learning decision process, by analyzing the model features and determining their importance. These results show that the model is heavily reliant of distance to the wall d and eddy viscosity vt, while other features display localized prominence.

The Structure of a Boundary Layer on a Rough Wall with Blowing and Heat Transfer

1979 ◽  
Vol 101 (2) ◽  
pp. 193-198 ◽  
Author(s):  
M. M. Pimenta ◽  
R. J. Moffat ◽  
W. M. Kays
Keyword(s):  
Boundary Layer ◽  
Correlation Coefficients ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Turbulence Structure ◽  
Reynolds Stresses ◽  
Hot Wire ◽  
Mean Velocity ◽  
Length Constant ◽  
The Mean

A regular, deterministic, rough surface was tested at four velocities from 11 to 40 m/s, with and without blowing, to evaluate the Stanton number and friction factor characteristics. Hot-wire data were taken to document the turbulence components, the Reynolds stresses, and the turbulent heat flux. Data are presented concerning the streamwise development of the mean and fluctuating components, and the effect of blowing. Correlation coefficients and mixing lengths were deduced from the hot-wire data and are also presented. While the mean velocity data showed only two allowable states for the boundary layer (laminar and “fully rough”), the turbulence structure indicated a third: “transitionally rough”. Distributions of u′v′/uτ2 and v′t′/uτtτ are similar, except for high blowing (F = 0.004). The turbulent Prandtl number lies between 0.85 and 1.0 for the entire layer, and a mixing length constant of κ = 0.41 describes the data with good accuracy for all velocities and all values of blowing tested.

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Pedro M. Milani ◽  
Julia Ling ◽  
John K. Eaton
Keyword(s):  
Machine Learning ◽  
Film Cooling ◽  
Eddy Viscosity ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Cooling Flows ◽  
Gradient Diffusion ◽  
Accurate Temperature ◽  
Injection Region ◽  
Model Features

Current turbulent heat flux models fail to predict accurate temperature distributions in film cooling flows. The present paper focuses on a machine learning (ML) approach to this problem, in which the gradient diffusion hypothesis (GDH) is used in conjunction with a data-driven prediction for the turbulent diffusivity field αt. An overview of the model is presented, followed by validation against two film cooling datasets. Despite insufficiencies, the model shows some improvement in the near-injection region. The present work also attempts to interpret the complex ML decision process, by analyzing the model features and determining their importance. These results show that the model is heavily reliant of distance to the wall d and eddy viscosity νt, while other features display localized prominence.

scholarly journals GRADIENT-DIFFUSION CLOSURE AND THE EJECTION-SWEEP CYCLE IN CONVECTIVE BOUNDARY LAYERS

2016 ◽  
Vol 38 ◽  
pp. 552
Author(s):  
Khaled Ghannam ◽  
Tomer Duman ◽  
Gabriel Katul ◽  
Marcelo Chamecki
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Vertical Velocity ◽  
Potential Temperature ◽  
Sensible Heat Flux ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Third Order ◽  
Convective Boundary ◽  
Gradient Diffusion

The inadequacy of conventional gradient-diffusion closure in modeling turbulent heat flux within the convective atmospheric boundary-layer is often alleviated by accounting for nonlocal transport. Such nonlocal effects are a manifestation of the inherent asymmetry in vertical transport in the convective boundary layer, which is in turn associated with third-order moments (skewness and fluxes of fluxes). In this work, the role of these third-order moments in second-order turbulence closure of the sensible heat flux is examined with the goal of reconciling the models to various closure assumptions. Surface layer similarity theory and mixed-layer parametrizations are used here, complemented by LES results when needed. The turbulent heat flux with various closure assumptions of the flux transport term is solved, including both local and nonlocal approaches. We connect to ejection-sweep cycles in the flow field using the GramCharlier cumulant expansion of the joint probability distribution of vertical velocity and potential temperature. In this nonlocal closure, the transport asymmetry models that include the vertical velocity skewness as a correction term to H originate from ejection-sweep events. Vertical inhomogeneity results in a modified-skewness correction to the nonlocal contribution to the heat flux associated with the relative intensity of ejections and sweeps.

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