classical turbulence modeling - university of notre damegtryggva/turbulence-intro.pdf · reynoldʼs...

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Computational Fluid Dynamics ClassicalTurbulence Modeling Grétar Tryggvason Fall 2011 Computational Fluid Dynamics A jet in a cross flow cross section of a jet Most engineering problems involve turbulent flows. Such flows involve are highly unsteady and contain a large range of scales. However, in most cases the mean or average motion is well defined. Computational Fluid Dynamics Flow over a sphere The drag depends on the separation point Computational Fluid Dynamics A modest Reynolds number the separated boundary layer remains initially laminar (left), before becoming turbulent. If the boundary layer is tripped (right) it becomes turbulent, so that it separates farther rearward. The overall drag is thereby dramatically reduced, in a way that occurs naturally on a smooth sphere only at a Reynolds numbers ten times as great. ONERA photograph, Werle 1980. From "An Album of Fluid Motion," by Van Dyke, Parabolic Press. Instantaneous flow past a sphere at R = 15,000. Instantaneous flow past a sphere at R = 30,000 with a trip wire Computational Fluid Dynamics Examples of Reynolds numbers: Flow around a 3 m long car at 100 km/hr: Flow around a 100 m long submarine at 10 km/hr: Re = LU v = 3 ! 27.78 1.5 ! 10 "5 = 5.5 ! 10 6 Kinematic viscosity (~20 °C) Water ν = 10 -6 m 2 /s Air ν = 1.5 10 -5 m 2 /s 1km/hr = 0.27778 m/s Re = LU v = 100 ! 2.78 10 "6 = 2.78 ! 10 8 Water flowing though a 0.01 m diameter pipe with a velocity of 1 m/s Re = LU v = 0.01 ! 1 10 "6 = 10 4 Computational Fluid Dynamics Reynolds Averaged Navier-Stokes (RANS): Only the averaged motion is computed. The effect of fluctuations is modeled Large Eddy Simulations (LES): Large scale motion is fully resolved but small scale motion is modeled Direct Numerical Simulations (DNS): Every length and time scale is fully resolved

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Page 1: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

“Classical”!Turbulence

Modeling!

Grétar Tryggvason!Fall 2011!

Computational Fluid Dynamics!

A jet in a cross flow!

cross section of a jet!

Most engineering problems involve turbulent flows. Such flows involve are highly unsteady and contain a large range of scales. However, in most cases the mean or average motion is well defined. !

Computational Fluid Dynamics!

Flow over a sphere!

The drag depends on the separation point!

Computational Fluid Dynamics!

A modest Reynolds number the separated boundary layer remains initially laminar (left), before becoming turbulent. If the boundary layer is tripped (right) it becomes turbulent, so that it separates farther rearward. The overall drag is thereby dramatically reduced, in a way that occurs naturally on a smooth sphere only at a Reynolds numbers ten times as great. ONERA photograph, Werle 1980.! From "An Album of Fluid Motion," by Van Dyke, Parabolic Press. !

Instantaneous flow past a sphere at R = 15,000.!

Instantaneous flow past a sphere at R = 30,000 with a trip wire

Computational Fluid Dynamics!

Examples of Reynolds numbers:!Flow around a 3 m long car at 100 km/hr:!

Flow around a 100 m long submarine at 10 km/hr:!

Re = LUv

= 3! 27.781.5 !10"5

= 5.5 !106

Kinematic viscosity !(~20 °C)!!Water ν = 10-6 m2/s!Air ν = 1.5 ✕10-5 m2/s!!1km/hr = 0.27778 m/s!

Re = LUv

= 100 ! 2.7810"6

= 2.78 !108

Water flowing though a 0.01 m diameter pipe with a velocity of 1 m/s!

Re = LUv

= 0.01!110"6

=104

Computational Fluid Dynamics!

Reynolds Averaged Navier-Stokes (RANS): Only the averaged motion is computed. The effect of fluctuations is modeled!

Large Eddy Simulations (LES): Large scale motion is fully resolved but small scale motion is modeled!

Direct Numerical Simulations (DNS): Every length and time scale is fully resolved!

Page 2: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

Reynolds Averaged

Navier-Stokes Equations!

Computational Fluid Dynamics!

To solve for the mean motion, we derive equations for the mean motion by averaging the Navier-Stokes equations. The velocities and other quantities are decomposed into the average and the fluctuation part !

a = A + a'

< a > = A< a' > = 0< a + b > = A+ B< ca > = cA< !a > =!A

Defining an averaging procedure that satisfies the following rules:!

This will hold for spatial averaging, temporal averaging, and ensamble averaging!

Computational Fluid Dynamics!

There are several ways to define the proper averages !

For homogeneous turbulence we can use the space average !

For steady turbulence flow we can use the time average!

For the general case we use the ensemble average!

< a > = 1Ladx

0

L

!

< a > = 1T

adt0

T

!

< a > = ar (x,t)ensambles! a = A + a'

Computational Fluid Dynamics!

!!tu + " #uu = $ 1

%"p+ &"2u

u =U + u'p = P+ p'

< a >=A< a' >= 0< ca >=cA< !a >=!A

Start with the Navier-Stokes equations!

Decompose the pressure and velocity into mean and fluctuations:!

a = A + a'

Or, in general, for any dependant variable:!

Computational Fluid Dynamics!

!!tU +" #UU = $

1%"P + &"2U $ "# < u'u' >

Applying the averaging to the Navier-Stokes equations results in:!

< u'u'>=< u'u'> < u'v'> < u'w'>< u'v'> < v 'v'> < v 'w'>

< u'w'> < v 'w'> < w'w'>

!

"

# # #

$

%

& & &

Reynold’s stress tensor!

Computational Fluid Dynamics!

Physical interpretation!

< uv >

Fast moving fluid particle!

Slow moving fluid particle!

Net momentum transfer due to velocity fluctuations!

Page 3: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

Closure:!

Since we only have an equation for the mean flow, the Reynolds stresses must be related to the mean flow. !

No rigorous process exists for doing this!!

THE TURBULENCE PROBLEM!

Computational Fluid Dynamics!

Zero and One equation models!

Computational Fluid Dynamics!

Introduce the “turbulent eddy viscosity”!

!T = l02

t0

< u'u' >ij= !"T

#Ui

#x j

+#U j

#xi

$

%&

'

()

where!

Computational Fluid Dynamics!

Zero equation models!

!T = l02 dUdy

Prandtl’ mixing length!

l0 = !y

Smagorinsky model!

Baldvin-Lomaz model!

!T = l02 2S ijS ij( )1/ 2

!T = l02 " i" i( )1/ 2

S ij = 12

!Ui

!x j

+!U j

!xi

"

# $ $

%

& ' '

! i = "Ui

"x j

#"U j

"xi

$

% & &

'

( ) )

Computational Fluid Dynamics!

One equation models!

!T = k1/ 2t0

Where k is obtained by an equation describing its temporal-spatial evolution!

However, the problem with zero and one equation models is that t0 and l0 are not universal. Generally, it is found that a two equation model is the minimum needed for a proper description !

Computational Fluid Dynamics!

Two equation models!

Page 4: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

To characterize the turbulence it seems reasonable to start with a measure of the magnitude of the velocity fluctuations. If the turbulence is isotropic, the turbulent kinetic energy can be used:!

k = 12

< u'u'> + < v 'v'> + < w'w'>( )The turbulent kinetic energy does, however, not distinguish between large and small eddies.!

Computational Fluid Dynamics!

To distinguish between large and small eddies we need to introduce a new quantity that describe!

! " # $u'i$u'i$x j$x j

Usually, the turbulent dissipation rate is used!

Smaller eddies dissipate faster!

Computational Fluid Dynamics!

!T = Cµk 2

"

!!tU + " #UU = $ 1

%"P + & + &T( )"2U

Solve for the average velocity!

Where the turbulent kinematic eddy viscosity is given by!

Computational Fluid Dynamics!

!k!t

+U j!k!x j

= " ij!Ui

!x j

#$ + !!x j

% !k!x j

# 12ui'ui'u j' # 1

&p'u j

''

( )

*

+ ,

The exact k-equation is:!

where!

! ij = " ui'u j'

The exact epsilon-equation is considerably more complex and we will not write it down here.!!Both equations contain transport, dissipation and production terms that must be modeled!

Computational Fluid Dynamics!

!k!t

+U "#k = # "Dk#k + production $ dissipation

!"!t

+U #$" = $ #D"$" + production % dissipation

The general for for the equations for k and epsilon is:!

These terms must be modeled !Closure involves proposing a form for the missing terms and optimizing free coefficients to fit experimental data!

Computational Fluid Dynamics!

Here!

The k-epsilon model!

!T = C k 2

"

! ij =< ui'u j' >= 2

3k"ij #$T

%Ui

%x j

+%U j

%xi

&

' ( (

)

* + + and!

C1 = 0.09; C2 =1.0; C3 = 0.769; C4 =1.44; C5 =1.92

DkDt= +! " (# + C2#T )!k - $ ij

%Ui

%x j

&'

D!Dt= " # ($ + C3$T )"! + C4

!k% ij

&Ui

&x j

'C5!2

kProduction! Dissipation!

Turbulent!transport!

Page 5: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

Other two equation turbulence models:!!RNG k-epsilon!!Nonlinear k-epsilon!!k-enstrophy!!k-lo!!k-reciprocal time!!etc!

Computational Fluid Dynamics!

Turbulent transport of energy and species concentrations is modeled in similar ways.!!For temperature we have:!

!T!t

+ " #uT = $"2T

u = U + u'T =< T > +T'

! < T >!t

+ " #U < T >= $"2 < T > %"# <UT >

Gradient Transport Hypothesis:!

<UT >!"T# < T >

Computational Fluid Dynamics!

Model Predictions!

Computational Fluid Dynamics!

Spreading rates:!

! ! exp ! k-e ! !Cmott!Plane jet !0.10 - 0.11 ! 0.108! !0.102!Round jet !0.085-0.095 ! 0.116! !0.095!Mixing layer !0.13 - 0.17 ! 0.152! !0.154!

Computational Fluid Dynamics!

From: C.G. Speziale: Analytical Methods for the Development of Reynolds-stress closure in Turbulence. Ann Rev. Fluid Mech. 1991. 23: 107-157!

Computational Fluid Dynamics!

From: C.G. Speziale: Analytical Methods for the Development of Reynolds-stress closure in Turbulence. Ann Rev. Fluid Mech. 1991. 23: 107-157!

Page 6: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

Results!

From: C.G. Speziale: Analytical Methods for the Development of Reynolds-stress closure in Turbulence. Ann Rev. Fluid Mech. 1991. 23: 107-157!

Computational Fluid Dynamics!

Wall bounded turbulence!

Computational Fluid Dynamics!

Wall bounded turbulence!

Fundamental assumption: determined by local variables only!

Mean flow!

Only the mean shear rate and the properties of the fluid are important!

!w = dUdy, ", #

Computational Fluid Dynamics!

Define a “shear velocity:”!

v* = !w"

!w = µ du dy, ", #

kg /ms2[ ], kg /m3[ ], m2 /s[ ]

Normalize the length and velocity near the wall!

u+ = uv*

y + = y v*

v

Called “wall variables”!

Computational Fluid Dynamics!

Thus, the velocity near the wall is!

Velocity versus distance from wall!

u+

y +

u+ = y +

u+ = 1!ln y + + C

10!

! = 0.4C = 5.5

v* = !w

"

!w = µ du dy

u+ = uv*

y + = y v*

v

Buffer layer!

Outer layer!

Viscous sub-layer!

Computational Fluid Dynamics!

For a “practical” engineering problem!L = 1m; U = 1m/s; ν = 10-6 (water)!

The Reynolds number is therefore:!

For a flat plate, the average drag coefficient is!

Re = LUv

=106

CD = FD12 !U

2LW

CD = 0.592Re!1/ 5 where!

CD = 0.0037

!w = FDLW

= CD12 "U

2 = 3.74Thus!

and!

And we find!

v* = 0.06

Page 7: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

y = 10!! *

= 10 "10#6

0.06=1.667 "10#4m = 0.1667 mm

Thickness of the viscous sub-layer!

Find the thickness of the boundary layer!

!L

= 0.37Re"1/ 5

!L

= 0.0233m = 23.3mm

To resolve the viscous sublayer at the same time as the turbulent boundary layer would require a large number of grid points!

The average thickness of the viscous sub-layer is 10 in units of y+:!

Computational Fluid Dynamics!

To deal with this problem it is common to use “wall functions” where the mean velocity is matched with an analytical approximation to the viscosus sublayer.!!For a reference, see: Patel, Rodi, and Scheuerer, “Turbulence Models for Near-Wall and Low Reynolds Number Flows: A Review. AIAA Journal, 23 (1985), 1308-1319!

Computational Fluid Dynamics!

Second order closure!

Computational Fluid Dynamics!

The k-epsilon and other two equation models have several serious limitations, including the inability to predict anisotropic Reynolds stress tensors, relaxation effects, and nonlocal effects due to turbulent diffusion.!

For these problems it is necessary to model the evolution of the full Reynolds stress tensor!

Computational Fluid Dynamics!

Derive equations for the Reynolds stresses:!

!ui

!t+"uiu j = #

1$"p + %"2ui

The Navier-Stokes equations in component form:!

ui!ui!t

+ "uiu j = - 1# "p+ $"2ui% & '

( ) *

Multiply the equation by the velocity!

and averaging leds to equations for !

!!t

uiu j

Computational Fluid Dynamics!

The new equations contain terms like!

which are not known. These terms are therefore modeled!

uiuiu j

The Reynolds stress model introduces 6 new equations (instead of 2 for the k-e model. Although the models have considerably more physics build in and allow, for example, anisotrophy in the Reynolds stress tensor, these model have yet to be optimized to the point that they consistently give superior results.!

For practical problems, the k-e model or more recent improvements such as RNG are therefore most commonly used!!

Page 8: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

Direct Numerical Simulations!

Computational Fluid Dynamics!

In direct numerical simulations the full unsteady Navier-Stokes equations are solved on a sufficiently fine grid so that all length and time scales are fully resolved. The “size” of the problem is therefore very limited. The goal of such simulations is to provide both insight and quantitative data for turbulence modeling!

Computational Fluid Dynamics!

Channel Flow!

Streamwise velocity!

Flow direction!

Periodic streamwise and spanwise boundaries!

Wall!

Computational Fluid Dynamics!

Streamwise vorticity!

Computational Fluid Dynamics!

Streamwise vorticity!

Turbulent shear stress!

Turbulent eddies generate a nearly uniform velocity profile!

Channel Flow!

Computational Fluid Dynamics!

Turbulence are intrinsically linked to vorticity, yet laminar flows can also be vortical so looking at the vorticity is not sufficient to understand what is going on in a turbulent flows. Several attempts have been made to define properties of the turbulent flows that identifies vortices (as opposed to simply vortical flows.!

One of the most successful method is the lambda-2 method of Hussain.!

Page 9: Classical Turbulence Modeling - University of Notre Damegtryggva/Turbulence-Intro.pdf · Reynoldʼs stress tensor! ... The exact epsilon-equation is considerably more complex

Computational Fluid Dynamics!

Visualizing turbulence!

!u =

"u"x

"u"y

"u"z

"v"x

"v"y

"v"z

"w"x

"w"y

"w"z

#

$

% % % % % %

&

'

( ( ( ( ( (

S = 12!u +!Tu( ) = 12

2"u"x

"u"y

+ "v"x

"u"z

+ "w"x

"v"x

+ "u"y

2"v"y

"v"z

+ "w"y

"w"x

+ "u"z

"w"y

+ "v"z

2"w"z

#

$

% % % % % %

&

'

( ( ( ( ( (

! = 12"u -"Tu( ) = 12

0 #u#y

$ #v#x

#u#z

$ #w#x

#v#x

$ #u#y

0 #v#z

$ #w#y

#w#x

$ #u#z

#w#y

$ #v#z

0

%

&

' ' ' ' ' '

(

)

* * * * * *

Computational Fluid Dynamics!

It can be shown that the second eigenvalue of !

S2 + !2

define vortex structures!Referece: J. Jeong and F. Hussain, "On the identification of a vortex," Journal of Fluid Mechanics, Vol. 285, 69-94, 1995.!

Other quantities have also been used, such as the second invariant of the velocity gradient:!

Q = !ui!x j

!u j

!xi

!2

Computational Fluid Dynamics!

!2 = "0.3

!2 = "0.2

Computational Fluid Dynamics!

Turbulence models are used to allow us to simulate only the averaged motion, not the unsteady small scale motion.!

Turbulence modeling rest on the assumption that the small scale motion is “universal” and can be described in terms of the large scale motion.!

Although considerable progress has been made, much is still not known and results from calculations using such models have to be interpreted by care!!

Computational Fluid Dynamics!

For more information:!D. C. Wilcox, Turbulence Modeling for CFD (2nd ed. 1998; 3rd ed. 2006). !The author is one of the inventors of the k-ω model and the book promotes it use. The discussion is, however, general and very accessible, as well as focused on the use of turbulence modeling for practical applications in CFD!