Turbulence kinetic energy

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Turbulence kinetic energy
Common symbols
TKE, k
In SI base units J/kg = m 2s −2
Derivations from
other quantities

In fluid dynamics, turbulence kinetic energy (TKE) is the mean kinetic energy per unit mass associated with eddies in turbulent flow. Physically, the turbulence kinetic energy is characterized by measured root-mean-square (RMS) velocity fluctuations. In the Reynolds-averaged Navier Stokes equations, the turbulence kinetic energy can be calculated based on the closure method, i.e. a turbulence model.

Contents

The TKE can be defined to be half the sum of the variances σ² (square of standard deviations σ) of the fluctuating velocity components: where each turbulent velocity component is the difference between the instantaneous and the average velocity: (Reynolds decomposition). The mean and variance are respectively.

TKE can be produced by fluid shear, friction or buoyancy, or through external forcing at low-frequency eddy scales (integral scale). Turbulence kinetic energy is then transferred down the turbulence energy cascade, and is dissipated by viscous forces at the Kolmogorov scale. This process of production, transport and dissipation can be expressed as: where: [1]

Assuming that molecular viscosity is constant, and making the Boussinesq approximation, the TKE equation is:

By examining these phenomena, the turbulence kinetic energy budget for a particular flow can be found. [2]

Computational fluid dynamics

In computational fluid dynamics (CFD), it is impossible to numerically simulate turbulence without discretizing the flow-field as far as the Kolmogorov microscales, which is called direct numerical simulation (DNS). Because DNS simulations are exorbitantly expensive due to memory, computational and storage overheads, turbulence models are used to simulate the effects of turbulence. A variety of models are used, but generally TKE is a fundamental flow property which must be calculated in order for fluid turbulence to be modelled.

Reynolds-averaged Navier–Stokes equations

Reynolds-averaged Navier–Stokes (RANS) simulations use the Boussinesq eddy viscosity hypothesis [3] to calculate the Reynolds stress that results from the averaging procedure: where

The exact method of resolving TKE depends upon the turbulence model used; kε (k–epsilon) models assume isotropy of turbulence whereby the normal stresses are equal:

This assumption makes modelling of turbulence quantities (k and ε) simpler, but will not be accurate in scenarios where anisotropic behaviour of turbulence stresses dominates, and the implications of this in the production of turbulence also leads to over-prediction since the production depends on the mean rate of strain, and not the difference between the normal stresses (as they are, by assumption, equal). [4]

Reynolds-stress models (RSM) use a different method to close the Reynolds stresses, whereby the normal stresses are not assumed isotropic, so the issue with TKE production is avoided.

Initial conditions

Accurate prescription of TKE as initial conditions in CFD simulations are important to accurately predict flows, especially in high Reynolds-number simulations. A smooth duct example is given below. where I is the initial turbulence intensity [%] given below, and U is the initial velocity magnitude. As an example for pipe flows, with the Reynolds number based on the pipe diameter:

Here l is the turbulence or eddy length scale, given below, and cμ is a kε model parameter whose value is typically given as 0.09;

The turbulent length scale can be estimated as with L a characteristic length. For internal flows this may take the value of the inlet duct (or pipe) width (or diameter) or the hydraulic diameter. [5]

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<span class="mw-page-title-main">Boundary layer</span> Layer of fluid in the immediate vicinity of a bounding surface

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<span class="mw-page-title-main">Surface layer</span> Layer of a turbulent fluid affected by interaction with a surface

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<span class="mw-page-title-main">Turbulence modeling</span> Use of mathematical models to simulate turbulent flow

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In fluid dynamics, the Taylor microscale, which is sometimes called the turbulence length scale, is a length scale used to characterize a turbulent fluid flow. This microscale is named after Geoffrey Ingram Taylor. The Taylor microscale is the intermediate length scale at which fluid viscosity significantly affects the dynamics of turbulent eddies in the flow. This length scale is traditionally applied to turbulent flow which can be characterized by a Kolmogorov spectrum of velocity fluctuations. In such a flow, length scales which are larger than the Taylor microscale are not strongly affected by viscosity. These larger length scales in the flow are generally referred to as the inertial range. Below the Taylor microscale the turbulent motions are subject to strong viscous forces and kinetic energy is dissipated into heat. These shorter length scale motions are generally termed the dissipation range.

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<span class="mw-page-title-main">Gamma-Re Transition Model</span>

Gamma-Re (γ-Re) transition model is a two equation model used in Computational Fluid Dynamics (CFD) to modify turbulent transport equations to simulate laminar, laminar-to-turbulent and turbulence states in a fluid flow. The Gamma-Re model does not intend to model the physics of the problem but attempts to fit a wide range of experiments and transition methods into its formulation. The transition model calculated an intermittency factor that creates turbulence by slowly introducing turbulent production at the laminar-to-turbulent transition location.

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In continuum mechanics, an energy cascade involves the transfer of energy from large scales of motion to the small scales or a transfer of energy from the small scales to the large scales. This transfer of energy between different scales requires that the dynamics of the system is nonlinear. Strictly speaking, a cascade requires the energy transfer to be local in scale, evoking a cascading waterfall from pool to pool without long-range transfers across the scale domain.

References

  1. Pope, S. B. (2000). Turbulent Flows . Cambridge: Cambridge University Press. pp.  122–134. ISBN   978-0521598866.
  2. Baldocchi, D. (2005), Lecture 16, Wind and Turbulence, Part 1, Surface Boundary Layer: Theory and Principles , Ecosystem Science Division, Department of Environmental Science, Policy and Management, University of California, Berkeley, CA: USA.
  3. Boussinesq, J. V. (1877). "Théorie de l'Écoulement Tourbillant". Mem. Présentés Par Divers Savants Acad. Sci. Inst. Fr. 23: 46–50.
  4. Laurence, D. (2002). "Applications of Reynolds Averaged Navier Stokes Equations to Industrial Flows". In van Beeck, J. P. A. J.; Benocci, C. (eds.). Introduction to Turbulence Modelling, Held March 18–22, 2002 at Von Karman Institute for Fluid Dynamics. Sint-Genesius-Rode: Von Karman Institute for Fluid Dynamics.
  5. Flórez Orrego; et al. (2012). "Experimental and CFD study of a single phase cone-shaped helical coiled heat exchanger: an empirical correlation". Proceedings of ECOS 2012 – The 25th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, June 26–29, 2012, Perugia, Italy. ISBN   978-88-6655-322-9.

Further reading