Why Prandlt Mixing Length Theory works at all?

In summary, the Prandlt Mixing Theory is based on some unreasonable assumptions, but it can still accurately predict simple 1-D flows. Davidson argues that this is because it is an extension of vortex dynamics, where the vorticity of large eddies is similar in magnitude to that of the mean flow. This is due to the three-dimensional structures formed by turbulent fluctuations, which tend to organize on the scale of the large eddies. The vorticity is then defined on this scale, which may be small for both velocity fluctuations and large eddies, but still accurately represents the turbulent vorticity.
  • #1
FluidStu
26
3
There are several unreasonable assumptions in the formulation of the Prandlt Mixing Theory. However, it works reasonably well for simple 1-D flow. An attempt to explain why is given by Davidson in his book 'Turbulence', on page 122 - 124, section 4.1.4.

It's stated that it still works because the mixing length theory is really just an extension of vortex dynamics. How? Actually there are two specific questions:

1) In the case of a planar x-y flow, the mean vorticity (that of the mean flow) points in the z-direction, while that of the turbulent vorticity (that of the fluctuating flow) is random. How does this mean that the vorticity of the large eddies is the same order of magnitude as that of the mean flow? Doesn't that assume that the fluctuating velocity is relatively insignificant?

2) We might define the vorticity as:

ωz ~ ∂ūx/∂y,

where ūx is the mean velocity. How then, can we extend this to say that:

ωz ~ ∂ūx/∂y ~ u/l,

where u is a typical measure of u' (fluctuating velocity) and l is typical size of the large eddies? I don't understand the physical meaning of this last statement, particularly since in (1) it seems like we said that the fluctuating component of the velocity is relatively insignificant? Why should the vorticity then be defined on the scale of these fluctuating velocities?

Thanks
 
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  • #3
FluidStu said:
1) In the case of a planar x-y flow, the mean vorticity (that of the mean flow) points in the z-direction, while that of the turbulent vorticity (that of the fluctuating flow) is random. How does this mean that the vorticity of the large eddies is the same order of magnitude as that of the mean flow? Doesn't that assume that the fluctuating velocity is relatively insignificant?

Provided that the large eddy length scale, ##\ell##, is significantly smaller than the boundary layer thickness (or other such inertial length scale), then yes, this implies that the fluctuating velocities are small. However, this isn't how Davidson is trying to argue that the orders of magnitude are the same. Instead, he uses more of heuristic explanation about how the turbulent fluctuations tend to tease out the initially-straight mean vortex lines into three dimensional structures (e.g. the hairpin vortices typical of boundary layers)., and that these structures tend to organize on the scale of the large eddies but retain roughly the same vorticity as they did originally due to conservation of vorticity.

FluidStu said:
2) We might define the vorticity as:

ωz ~ ∂ūx/∂y,

where ūx is the mean velocity. How then, can we extend this to say that:

ωz ~ ∂ūx/∂y ~ u/l,

where u is a typical measure of u' (fluctuating velocity) and l is typical size of the large eddies? I don't understand the physical meaning of this last statement, particularly since in (1) it seems like we said that the fluctuating component of the velocity is relatively insignificant? Why should the vorticity then be defined on the scale of these fluctuating velocities?

It doesn't really matter if the velocity fluctuations themselves are small, because if the large eddy length scale, ##\ell##, is also small, then the gradient can still be large and of the same order of magnitude as the mean flow vorticity (which has a larger numerator and denominator). In fact, assuming that both ##u## and ##\ell## are small means that ##u/\ell## more accurately approximates a derivative, which is how they justify using that to represent the turbulent vorticity.

Of course, the main thing to take away here is that turbulence models are inherently hand-wavy, but as long as the scaling arguments hold for a given physical situation, the model will perform reasonably well.
 

Related to Why Prandlt Mixing Length Theory works at all?

1. What is Prandtl Mixing Length Theory?

Prandtl Mixing Length Theory is a widely used theory in fluid mechanics that explains the turbulent mixing of fluids in a boundary layer. It was developed by Ludwig Prandtl in the early 20th century.

2. How does Prandtl Mixing Length Theory work?

This theory is based on the concept of eddy viscosity, which describes the turbulent transfer of momentum in a fluid. It assumes that the turbulent eddies in a fluid can be modeled as a series of cylinders, and the length of these cylinders is known as the mixing length. The theory then uses this mixing length to calculate the eddy viscosity and predict the turbulent flow in a fluid.

3. Why is Prandtl Mixing Length Theory considered valid?

Prandtl Mixing Length Theory is considered valid because it has been extensively verified through experiments and has been shown to accurately predict the behavior of turbulent flow in various situations. It also has a solid theoretical foundation and has been widely used in practical applications.

4. What are the limitations of Prandtl Mixing Length Theory?

While Prandtl Mixing Length Theory is a useful tool in fluid mechanics, it does have its limitations. It assumes that the mixing length remains constant throughout the flow, which may not always be the case. It also does not take into account the effects of compressibility and heat transfer, which may be important in certain situations.

5. Are there any alternative theories to Prandtl Mixing Length Theory?

Yes, there are alternative theories to Prandtl Mixing Length Theory, such as the Reynolds stress model and the Large Eddy Simulation model. These theories may provide more accurate predictions in certain situations, but they are also more complex and computationally intensive.

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