Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRevisiting the role of intermittent heat transport towards Reynolds stress anisotropy in convective turbulence
86
0
0.0
(
0
)
Added by
Subharthi Chowdhuri
Publication date
2019
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Thermal plumes are the energy containing eddy motions that carry heat and momentum in a convective boundary layer. The detailed understanding of their structure is of fundamental interest for a range of applications, from wall-bounded engineering flows to quantifying surface-atmosphere flux exchanges. We address the aspect of Reynolds stress anisotropy associated with the intermittent nature of heat transport in thermal plumes by performing an invariant analysis of the Reynolds stress tensor in an unstable atmospheric surface layer flow, using a field-experimental dataset. Given the intermittent and asymmetric nature of the turbulent heat flux, we formulate this problem in an event-based framework. In this approach, we provide structural descriptions of warm-updraft and cold-downdraft events and investigate the degree of isotropy of the Reynolds stress tensor within these events of different sizes. We discover that only a subset of these events are associated with the least anisotropic turbulence in highly-convective conditions. Additionally, intermittent large heat flux events are found to contribute substantially to turbulence anisotropy under unstable stratification. Moreover, we find that the sizes related to the maximum value of the degree of isotropy do not correspond to the peak positions of the heat flux distributions. This is because, the vertical velocity fluctuations pertaining to the sizes associated with the maximum heat flux, transport significant amount of streamwise momentum. A preliminary investigation shows that the sizes of the least anisotropic events probably scale with a mixed-length scale ($z^{0.5}lambda^{0.5}$, where $z$ is the measurement height and $lambda$ is the large-eddy length scale).
rate research
Read More
Despite a cost-effective option in practical engineering, Reynolds-averaged Navier-Stokes simulations are facing the ever-growing demand for more accurate turbulence models. Recently, emerging machine learning techniques are making promising impact in turbulence modeling, but in their infancy for widespread industrial adoption. Towards this end, this work proposes a universal, inherently interpretable machine learning framework of turbulence modeling, which mainly consists of two parallel machine-learning-based modules to respectively infer the integrity basis and closure coefficients. At every phase of the model development, both data representing the evolution dynamics of turbulence and domain-knowledge representing prior physical considerations are properly fed and reasonably converted into modeling knowledge. Thus, the developed model is both data- and knowledge-driven. Specifically, a version with pre-constrained integrity basis is provided to demonstrate detailedly how to integrate domain-knowledge, how to design a fair and robust training strategy, and how to evaluate the data-driven model. Plain neural network and residual neural network as the building blocks in each module are compared. Emphases are made on three-fold: (i) a compact input feature parameterizing the newly-proposed turbulent timescale is introduced to release nonunique mappings between conventional input arguments and output Reynolds stress; (ii) the realizability limiter is developed to overcome under-constraint of modeled stress; and (iii) constraints of fairness and noisy-sensitivity are first included in the training procedure. In such endeavors, an invariant, realizable, unbiased and robust data-driven turbulence model is achieved, and does gain good generalization across channel flows at different Reynolds numbers and duct flows with various aspect ratios.
This numerical study presents a simple but extremely effective way to considerably enhance heat transport in turbulent multiphase flows, namely by using oleophilic walls. As a model system, we pick the Rayleigh-Benard setup, filled with an oil-water mixture. For oleophilic walls, e.g. using only $10%$ volume fraction of oil in water, we observe a remarkable heat transport enhancement of more than $100%$ as compared to the pure water case. In contrast, for oleophobic walls, the enhancement is then only about $20%$ as compared to pure water. The physical explanation of the highly-efficient heat transport for oleophilic walls is that thermal plumes detach from the oil-rich boundary layer and are transported together with the oil phase. In the bulk, the oil-water interface prevents the plumes to mix with the turbulent water bulk. To confirm this physical picture, we show that the minimum amount of oil to achieve the maximum heat transport is set by the volume fraction of the thermal plumes. Our findings provide guidelines of how to optimize heat transport in thermal turbulence. Moreover, the physical insight of how coherent structures are coupled with one phase of a two-phase system has very general applicability for controlling transport properties in other turbulent multiphase flows.
A new scaling is derived that yields a Reynolds number independent profile for all components of the Reynolds stress in the near-wall region of wall bounded flows. The scaling demonstrates the important role played by the wall shear stress fluctuations and how the large eddies determine the Reynolds number dependence of the near-wall turbulence behavior.
Intense fluctuations of energy dissipation rate in turbulent flows result from the self-amplification of strain rate via a quadratic nonlinearity, with contributions from vorticity (via the vortex stretching mechanism) and the pressure Hessian tensor, which we analyze here using direct numerical simulations of isotropic turbulence in periodic domains of up to $12288^3$ grid points, and Taylor-scale Reynolds numbers in the range $140-1300$. We extract the statistics of various terms involved in amplification of strain and additionally condition them on the magnitude of strain. We find that strain is overall self-amplified by the quadratic nonlinearity, and depleted via vortex stretching; whereas pressure Hessian acts to redistribute strain fluctuations towards the mean-field and thus depleting intense strain. Analyzing the intense fluctuations of strain in terms of its eigenvalues reveals that the net amplification is solely produced by the third eigenvalue, resulting in strong compressive action. In contrast, the self-amplification terms acts to deplete the other two eigenvalues, whereas vortex stretching acts to amplify them, both effects canceling each other almost perfectly. The effect of the pressure Hessian for each eigenvalue is qualitatively similar to that of vortex stretching, but significantly weaker in magnitude. Our results conform with the familiar notion that intense strain is organized in sheet-like structures, which are in the vicinity of, but never overlap with regions of intense vorticity due to fundamental differences in their amplifying mechanisms.
We present high-precision experimental and numerical studies of the Nusselt number $Nu$ as functions of the Rayleigh number $Ra$ in geostrophic rotating convection with domain aspect ratio ${Gamma}$ varying from 0.4 to 3.8 and the Ekman number Ek from $2.0{times}10^{-7}$ to $2.7{times}10^{-5}$. The heat-transport data $Nu(Ra)$ reveal a gradual transition from buoyancy-dominated to geostrophic convection at large $Ek$, whereas the transition becomes sharp with decreasing $Ek$. We determine the power-law scaling of $Nu{sim}Ra^{gamma}$, and show that the boundary flows give rise to pronounced enhancement of $Nu$ in a broad range of the geostrophic regime, leading to reduction of the scaling exponent ${gamma}$ in small ${Gamma}$ cells. The present work provides new insight into the heat-transport scaling in geostrophic convection and may explain the discrepancies observed in previous studies.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
