β-distribution for Reynolds stress and turbulent heat flux in relaxation turbulent boundary layer of compression ramp

2017 ◽  
Vol 60 (12) ◽  
Author(s):  
YanChao Hu ◽  
WeiTao Bi ◽  
ShiYao Li ◽  
ZhenSu She
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Turbulent Boundary Layer ◽  
Reynolds Stress ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Compression Ramp

scholarly journals Explicit Algebraic Reynolds-stress Modelling of a Convective Atmospheric Boundary Layer Including Counter-Gradient Fluxes

2020 ◽  
Author(s):  
Velibor Želi ◽  
Geert Brethouwer ◽  
Stefan Wallin ◽  
Arne V. Johansson
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Atmospheric Boundary Layer ◽  
Reynolds Stress ◽  
Turbulent Heat Flux ◽  
Gradient Term ◽  
Turbulent Heat ◽  
Bound Layer Meteorol ◽  
Wide Range ◽  
Stable Atmospheric Boundary Layer

AbstractIn a recent study (Želi et al. in Bound Layer Meteorol 176:229–249, 2020), we have shown that the explicit algebraic Reynolds-stress (EARS) model, implemented in a single-column context, is able to capture the main features of a stable atmospheric boundary layer (ABL) for a range of stratification levels. We here extend the previous study and show that the same formulation and calibration of the EARS model also can be applied to a dry convective ABL. Five different simulations with moderate convective intensities are studied by prescribing surface heat flux and geostrophic forcing. The results of the EARS model are compared to large-eddy simulations of Salesky and Anderson (J Fluid Mech 856:135–168, 2018). It is shown that the EARS model performs well and is able to capture the counter-gradient heat flux in the upper part of the ABL due to the presence of the non-gradient term in the relation for vertical turbulent heat flux. The model predicts the full Reynolds-stress tensor and heat-flux vector and allows us to compare other important aspects of a convective ABL such as the profiles of vertical momentum variance. Together with the previous studies, we show that the EARS model is able to predict the essential features of the ABL. It also shows that the EARS model with the same model formulation and coefficients is applicable over a wide range of stable and moderately unstable stratifications.

scholarly journals Assessment of Turbulence Models in a Hypersonic Cold-Wall Turbulent Boundary Layer

Fluids ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 37 ◽  
Author(s):  
Junji Huang ◽  
Jorge-Valentino Bretzke ◽  
Lian Duan
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Shear Stress ◽  
Turbulent Boundary Layer ◽  
Prandtl Number ◽  
Eddy Viscosity ◽  
Turbulence Models ◽  
Turbulent Heat Flux ◽  
Turbulent Heat ◽  
Cold Wall

In this study, the ability of standard one- or two-equation turbulence models to predict mean and turbulence profiles, the Reynolds stress, and the turbulent heat flux in hypersonic cold-wall boundary-layer applications is investigated. The turbulence models under investigation include the one-equation model of Spalart–Allmaras, the baseline k - ω model by Menter, as well as the shear-stress transport k - ω model by Menter. Reynolds-Averaged Navier-Stokes (RANS) simulations with the different turbulence models are conducted for a flat-plate, zero-pressure-gradient turbulent boundary layer with a nominal free-stream Mach number of 8 and wall-to-recovery temperature ratio of 0.48 , and the RANS results are compared with those of direct numerical simulations (DNS) under similar conditions. The study shows that the selected eddy-viscosity turbulence models, in combination with a constant Prandtl number model for turbulent heat flux, give good predictions of the skin friction, wall heat flux, and boundary-layer mean profiles. The Boussinesq assumption leads to essentially correct predictions of the Reynolds shear stress, but gives wrong predictions of the Reynolds normal stresses. The constant Prandtl number model gives an adequate prediction of the normal turbulent heat flux, while it fails to predict transverse turbulent heat fluxes. The discrepancy in model predictions among the three eddy-viscosity models under investigation is small.

Heat Transfer in the Accelerated Fully Rough Turbulent Boundary Layer

1981 ◽  
Vol 103 (1) ◽  
pp. 153-158 ◽  
Author(s):  
H. W. Coleman ◽  
R. J. Moffat ◽  
W. M. Kays
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Heat Flux ◽  
Turbulent Boundary Layer ◽  
Prandtl Number ◽  
Pressure Gradient ◽  
Turbulent Heat Flux ◽  
Test Surface ◽  
Turbulent Heat ◽  
Turbulent Prandtl Number

Heat transfer behavior of a fully rough turbulent boundary layer subjected to favorable pressure gradients was investigated experimentally using a porous test surface composed of densely packed spheres of uniform size. Stanton numbers and profiles of mean temperature, turbulent Prandtl number, and turbulent heat flux are reported. Three equilibrium acceleration cases (one with blowing) and one non-equilibrium acceleration case were studied. For each acceleration case of this study, Stanton number increased over zero pressure gradient values at the same position or enthalpy thickness. Turbulent Prandtl number was found to be approximately constant at 0.7–0.8 across the layer, and profiles of the non-dimensional turbulent heat flux showed close agreement with those previously reported for both smooth and rough wall zero pressure gradient layers.

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.

Effects of spatial resolution and temporal offset of air-sea boundary-layer variables on turbulent heat flux estimates

2021 ◽  
Author(s):  
Tong Lee ◽  
Chelle Centemann ◽  
Carol Anne Clayson ◽  
Mark Bourassa ◽  
Shannon Brown ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
High Resolution ◽  
Surface Wind ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Turbulent Heat ◽  
Input Variables ◽  
Temporal Offset

<p>Air-sea turbulent heat fluxes and their spatial gradients are important to the ocean, climate, weather, and their interactions. Satellite-based estimation of air-sea latent and sensible fluxes, providing broad coverage, require measurements of sea surface temperature, ocean-surface wind speed, and air temperature and humidity above sea surface. Because no single satellite has been able to provide simultaneous measurements of these input variables, they typically come from various satellites with different spatial resolutions and sampling times that can be offset by hours. These factors introduce errors in the estimated heat fluxes and their gradients that are not well documented. As a model-based assessment of these errors, we performed a simulation using a Weather Research and Forecasting (WRF) model forced by high-resolution blended satellite SST for the Gulf Stream extension region with a 3-km resolution and with 30-minute output. Latent and sensible heat fluxes were first computed from input variables with the original model resolutions and at coincident times. We then computed the heat fluxes by (1) decimating the input variables to various resolutions from 12.5 to 50 km, and (2) offsetting the “sampling” times of some input variables from others by 3 hours. The resultant estimations of heat fluxes and their gradients from (1) and (2) were compared with the counterparts without reducing resolution and without temporal offset of the input variables. The results show that reducing input-variable resolutions from 12.5 to 50 km weakened the magnitudes of the time-mean and instantaneous heat fluxes and their gradients substantially, for example, by a factor of two for the time-mean gradients. The temporal offset of input variables substantially impacted the instantaneous fluxes and their gradients, although not their time-mean values. The implications of these effects on scientific and operational applications of heat flux products will be discussed. Finally, we highlight a mission concept for providing simultaneous, high-resolution measurements of boundary-layer variables from a single satellite to improve air-sea turbulent heat flux estimation.</p>

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