LARGE EDDY SIMULATION (LES)
Lars Davidson, www.tfd.chalmers.se/˜lada Chalmers University of Technology Gothenburg, Sweden
L ARGE E DDY S IMULATION (LES) Lars Davidson, - - PowerPoint PPT Presentation
L ARGE E DDY S IMULATION (LES) Lars Davidson, www.tfd.chalmers.se/lada Chalmers University of Technology Gothenburg, Sweden T HREE -D AY CFD C OURSE AT C HALMERS This lecture is a condensed version of the course Unsteady Simulations for
Lars Davidson, www.tfd.chalmers.se/˜lada Chalmers University of Technology Gothenburg, Sweden
◮This lecture is a condensed version of the course Unsteady Simulations for Industrial Flows: LES, DES, hybrid LES-RANS and URANS 5-7 November 2012 at Chalmers, Gothenburg, Sweden Max 16 participants 50% lectures and 50% workshops in front of a PC For info, see http://www.tfd.chalmers.se/˜lada/cfdkurs/cfdkurs.html
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The slides are partly based on the course material at (click here) http://www.tfd.chalmers.se/˜lada/ comp turb model/lecture notes.html This course is part of the MSc programme Applied Mechanics at
http://www.tfd.chalmers.se/˜lada/ msc/msc-programme.html The MSc programme is presented here http://www.chalmers.se/en/education/programmes/mast
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GS SGS SGS In LES, large (Grid) Scales (GS) are resolved and the small (Sub-Grid) Scales (SGS) are modelled. LES is suitable for bluff body flows where the flow is governed by large turbulent scales
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Snapshots of large turbulent scales illustrated by Q = −∂¯ ui ∂xj ∂¯ uj ∂xi
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TIME-AVERAGED flow and INSTANTANEOUS flow
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TIME-AVERAGED flow and INSTANTANEOUS flow In average there is backflow (negative velocities). Instantaneous, the negative velocities are often positive.
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TIME-AVERAGED flow and INSTANTANEOUS flow In average there is backflow (negative velocities). Instantaneous, the negative velocities are often positive. How easy is it to model fluctuations that are as large as the mean flow?
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TIME-AVERAGED flow and INSTANTANEOUS flow In average there is backflow (negative velocities). Instantaneous, the negative velocities are often positive. How easy is it to model fluctuations that are as large as the mean flow? Is it reasonable to require a turbulence model to fix this?
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TIME-AVERAGED flow and INSTANTANEOUS flow In average there is backflow (negative velocities). Instantaneous, the negative velocities are often positive. How easy is it to model fluctuations that are as large as the mean flow? Is it reasonable to require a turbulence model to fix this? Isn’t it better to RESOLVE the large fluctuations?
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RANS: time average. This is called Reynolds time averaging: Φ = 1 2T T
−T
Φ(t)dt, Φ = Φ + Φ′ In LES we filter (volume average) the equations. In 1D we get: ¯ Φ(x, t) = 1 ∆x x+0.5∆x
x−0.5∆x
Φ(ξ, t)dξ Φ = ¯ Φ + Φ′′ (1)
no filter
2 2.5 3 3.5 4 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
u , ¯ u x
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The filtering is defined by the discretization (nothing is done) The filtered Navier-Stokes (N-S) eqns, i.e. the LES eqns, read ∂¯ ui ∂t + ∂ ∂xj ¯ ui ¯ uj
ρ ∂¯ p ∂xi + ν ∂2¯ ui ∂xj∂xj − ∂τij ∂xj , ∂ ¯ ui ∂xi = 0 (2) where the subgrid stresses are given by τij = uiuj − ¯ ui¯ uj Contrary to Reynolds time averaging where u′
i = 0, we have here
u′′
i = 0
¯ ui = ¯ ui
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The N-S eqns are filtered (=discretized) using Eq. 1 The pressure gradient term, for example, reads ∂p ∂xi = 1 V
∂p ∂xi dV Now we want to move the derivative out of the integral. It is allowed if V is constant. The filtering volume, V=grid cell which is not constant Fortunately, the error is proportional to V 2, i.e. it is 2nd-order error ∂p ∂xi = ∂ ∂xi 1 V
pdV
= ∂ ∂xi (¯ p) + O
All linear terms are treated in the same way.
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First we filter the term and move the derivative out of the integral ∂uiuj ∂xj = ∂ ∂xj 1 V
uiujdV
= ∂ ∂xj (uiuj) + O
We have ∂ ∂xj uiuj; we want ∂ ∂xj ¯ ui ¯ uj Let’s add want we want (on both LHS ans RHS) and subtract want we don’t want This is how we end up with the convective term and the SGS term in Eq. 2, i.e. −∂τij ∂xj = − ∂ ∂xj (uiuj − ¯ ui¯ uj)
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GS SGS SGS Large scales (GS) are resolved; small scales (SGS) are modelled.
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The limit (cut-off) between GS and SGS is supposed to take place in the inertial subrange (II) I II III κ E(κ) cut-off I: large scales II: inertial subrange, −5/3-range III: dissipation subrange GS SGS
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We need a subgrid model for the SGS turbulent scales The simplest model is the Smagorinsky model [23]: τij − 1 3δijτkk = −2νsgs¯ sij νsgs = (CS∆)2 2¯ sij¯ sij ≡ (CS∆)2 |¯ s| ¯ sij = 1 2 ∂¯ ui ∂xj + ∂¯ uj ∂xi
∆ = (∆VIJK )1/3 (3) A damping function fµ is added to ensure that νsgs ⇒ 0 as y ⇒ 0 fµ = 1 − exp(−y+/26) A more convenient way to dampen the SGS viscosity near the wall is ∆ = min
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boundary-layer flow [13, 22] νt = ℓ2
∂y
νt = ℓ2 ∂Ui ∂xj + ∂Uj ∂xi ∂Ui ∂xj 1/2 = ℓ2 2SijSij 1/2 ≡ ℓ2|S|.
νsgs = (CS∆)2|¯ s| which is the same as Eq. 3
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E(κ) E(κ) ∝ κ−5/3 κ inertial range εsgs ≃
∂u′
i
∂xj ∂u′
i
∂xj
dissipating range
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LES can handle many flows which RANS (Reynolds Averaged Navier Stokes) cannot; the reason is that in LES large, turbulent scales are
large, unsteady, turbulent structures
LES is very much more expensive than RANS.
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RANS LES Domain 2D or 3D always 3D Time domain steady or unsteady always unsteady Space discretization 2nd order upwind central differencing Time discretization 1st order 2nd order (e.g. C-N) Turbulence model ≥ two-equations zero- or one-eq
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t1: Start time averaging t2: Stop time averaging t t1: start t2: end ¯ v1
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Biggest problem with LES: near walls, it requires very fine mesh in all directions, not only in the near-wall direction.
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Biggest problem with LES: near walls, it requires very fine mesh in all directions, not only in the near-wall direction. The reason: violent violent low-speed outward ejections and high-speed in-rushes must be resolved (often called streaks).
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Biggest problem with LES: near walls, it requires very fine mesh in all directions, not only in the near-wall direction. The reason: violent violent low-speed outward ejections and high-speed in-rushes must be resolved (often called streaks). A resolved these structures in LES requires ∆x+ ≃ 100, ∆y+
min ≃ 1 and ∆z+ ≃ 30
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Biggest problem with LES: near walls, it requires very fine mesh in all directions, not only in the near-wall direction. The reason: violent violent low-speed outward ejections and high-speed in-rushes must be resolved (often called streaks). A resolved these structures in LES requires ∆x+ ≃ 100, ∆y+
min ≃ 1 and ∆z+ ≃ 30
The object is to develop a near-wall treatment which models the streaks (URANS) ⇒ much larger ∆x and ∆z
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Biggest problem with LES: near walls, it requires very fine mesh in all directions, not only in the near-wall direction. The reason: violent violent low-speed outward ejections and high-speed in-rushes must be resolved (often called streaks). A resolved these structures in LES requires ∆x+ ≃ 100, ∆y+
min ≃ 1 and ∆z+ ≃ 30
The object is to develop a near-wall treatment which models the streaks (URANS) ⇒ much larger ∆x and ∆z In the presentation we use Hybrid LES-RANS for which the grid requirements are much smaller than for LES
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In RANS when using wall-functions, 30 < y+ < 100 for the wall-adjacent cells x y wall y+
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In RANS when using wall-functions, 30 < y+ < 100 for the wall-adjacent cells In LES, ∆z+ ≃ 30 x y wall y+ x z ∆z+
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In RANS when using wall-functions, 30 < y+ < 100 for the wall-adjacent cells In LES, ∆z+ ≃ 30 EVERYWHERE x y wall y+ x z ∆z+
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In RANS when using wall-functions, 30 < y+ < 100 for the wall-adjacent cells In LES, ∆z+ ≃ 30 EVERYWHERE AND ∆x+ ≃ 100, ∆y+
min ≃ 1
x y wall y+ x z ∆z+
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1 2 3 4 5 6 0.5 1 1.5
x z Fluctuating streamwise velocity at y+ = 5. DNS of channel flow. We find that the structures in the spanwise direction are very small which requires a very fine mesh in z direction.
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A New Approach of Zonal Hybrid RANS-LES Based on a Two-equation k − ε Model [7] ETMM9, Thessaloniki, 7-9 June 2012 Financed by the EU project ATAAC (Advanced Turbulence Simulation for Aerodynamic Application Challenges) DLR, Airbus UK, Alenia, ANSYS, Beijing Tsinghua University, CFS Engineering, Chalmers, Dassault Aviation, EADS, Eurocopter Deutschland, FOI, Imperial College, IMFT, LFK, NLR, NTS, Numeca, ONERA, Rolls-Royce Deutschland, TU Berlin, TU Darmstadt, UniMAN
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∂k ∂t + ∂(kUj) ∂xj = ∂ ∂xj
σku ∂k ∂xj
∂ε ∂t + ∂(εUj) ∂xj = ∂ ∂xj
σεu ∂ε ∂xj
ε k − C∗
ε2
ε2 k νt = Cµfµ k2 ε , C∗
ε2 = Cε1 + fk
fε (Cε2f2 − Cε1), σku ≡ σk f 2
k
fε , σεu ≡ σε f 2
k
fε Cε1, Cε2, σk, σε and Cµ same values as [1]. fε = 1. f2 and fµ read f2 =
3.1 2 1 − 0.3exp
Rt 6.5 2 fµ =
14 2 1 + 5 R3/4
t
exp
Rt 200 2 Baseline model: fk = 0.4. Range of 0.2 < fk < 0.6 is evaluated
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∂k ∂t + ∂(kUj) ∂xj = ∂ ∂xj
σku ∂k ∂xj
∂ε ∂t + ∂(εUj) ∂xj = ∂ ∂xj
σεu ∂ε ∂xj
ε k − C∗
ε2
ε2 k νt = Cµfµ k2 ε , C∗
ε2 = Cε1 + fk
fε (Cε2f2 − Cε1), σku ≡ σk f 2
k
fε , σεu ≡ σε f 2
k
fε Cε1, Cε2, σk, σε and Cµ same values as [1]. fε = 1. f2 and fµ read f2 =
3.1 2 1 − 0.3exp
Rt 6.5 2 fµ =
14 2 1 + 5 R3/4
t
exp
Rt 200 2 Baseline model: fk = 0.4. Range of 0.2 < fk < 0.6 is evaluated
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∂k ∂t + ∂(kUj) ∂xj = ∂ ∂xj
σku ∂k ∂xj
∂ε ∂t + ∂(εUj) ∂xj = ∂ ∂xj
σεu ∂ε ∂xj
ε k − C∗
ε2
ε2 k νt = Cµfµ k2 ε , C∗
ε2 = 1.5 + fk
fε (1.9 − 1.5), σku ≡ σk f 2
k
fε , σεu ≡ σε f 2
k
fε Cε1, Cε2, σk, σε and Cµ same values as [1]. fε = 1. f2 and fµ read f2 =
3.1 2 1 − 0.3exp
Rt 6.5 2 fµ =
14 2 1 + 5 R3/4
t
exp
Rt 200 2 Baseline model: fk = 0.4. Range of 0.2 < fk < 0.6 is evaluated
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x y ku,int, εu,int wall yint LES, fk < 1 RANS, fk = 1.0 Interface: how to treat k and ε over the interface? They should be reduced from their RANS values to suitable LES values The usual convection and diffusion across the interface is cut off, and new “interface boundary” conditions are prescribed ku,int = fkkRANS Nothing is done for ε xmax = 3.2 (64 cells), zmax = 1.6 (64 cells), y dir: 80 − 128 cells CDS in entire region
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int = 500
1 100 1000 30000 5 10 15 20 25 30
y+ U+
0.05 0.1 0.15 0.2
y u′v′+ Reτ = 4 000 Reτ = 8 000 Reτ = 16 000; Reτ = 32 000.
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1 100 1000 8000 5 10 15 20 25 30
y+ U+
500 1000 1500 2000
y+ u′v′+ y+ = 130 y+ = 500 y+ = 980
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int = 500
1 100 1000 10000 5 10 15 20 25 30
y+ U+
0.05 0.1 0.15 0.2
y u′v′+ fk = 0.2 fk = 0.3 fk = 0.5 fk = 0.6
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1 100 1000 30000 5 10 15 20 25 30
(Nx × Nz) = (32 × 32) y+ U+
1 100 1000 30000 5 10 15 20 25 30
(Nx × Nz) = (128 × 128) y+ U+ Reτ = 4 000 Reτ = 8 000 Reτ = 16 000; Reτ = 32 000.
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0.05 0.1 0.15 0.2
(Nx × Nz) = (32 × 32) y u′v′+
0.05 0.1 0.15 0.2
(Nx × Nz) = (128 × 128) y u′v′+ Reτ = 4 000 Reτ = 8 000 Reτ = 16 000; Reτ = 32 000.
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0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 4000 νt/(uτδ)
0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 8000
0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 16 000 y νt/(uτδ)
0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 32 000 y (Nx × Nz) = 64 × 64 32 × 32 128 × 128
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0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 4000 νt/(uτδ)
0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 8000
0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 16 000 y νt/(uτδ)
0.05 0.1 0.15 0.2 0.25 0.3 0.005 0.01 0.015
Reτ = 32 000 y (Nx × Nz) = 64 × 64 32 × 32 128 × 128
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When the grid is refined, νt gets smaller
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When the grid is refined, νt gets smaller E εsgs κ Pk,res κc
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When the grid is refined, νt gets smaller E εsgs,∆ εsgs,0.5∆ κ Pk,res κc 2κc
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When the grid is refined, νt gets smaller E εsgs,∆ εsgs,0.5∆ κ Pk,res κc 2κc εsgs,∆ = εsgs,0.5∆ εsgs = 2νt¯ sij¯ sij − τ12,t∂¯ u ∂y Grid refinement ⇒ must be accompanied with larger ¯ sij¯ sij ⇒ ¯ sij¯ sij must take place at higher wavenumbers if not ⇒ grid dependent
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t
20 40 60 80 100 0.005 0.01 0.015 0.02
E
w′/∂z)2 κz One-eq ksgs model
20 40 60 80 100 0.005 0.01 0.015 0.02 0.025 0.03
κz Zonal PANS (Nx × Nz) = 64 × 64 32 × 32 128 × 128
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Energy spectra of the SGS dissipation show that the peak takes place at surprisingly low wavenumber (length scale corresponding to 10 cells or more). κc κ E(κ) εsgs
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Energy spectra of the SGS dissipation show that the peak takes place at surprisingly low wavenumber (length scale corresponding to 10 cells or more). κc κ E(κ) εsgs εsgs,κ
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SGS dissipation in the ¯ u′
i ¯
u′
i/2 eq, εsgs = 2νt¯
sij¯ sij − τ12,t∂¯ u ∂y
0.1 0.15 0.2 0.25 0.3 5 10 15
y εsgs One-eq ksgs model
0.1 0.15 0.2 0.25 0.3 5 10 15
y εsgs Zonal PANS (Nx × Nz) = 64 × 64 32 × 32 128 × 128
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0.1 0.2 0.3 0.4 0.5 0.6 0.1 0.2 0.3 0.4 0.1 0.2 0.3 0.4 0.5 0.6 2E-3 4E-3 0.1 0.2 0.3 0.4 0.5 0.6 2E-3 4E-3
y k equation
0.1 0.2 0.3 0.4 0.5 0.6 0.01 0.02 0.03 0.1 0.2 0.3 0.4 0.5 0.6 2E-4 4E-4 0.1 0.2 0.3 0.4 0.5 0.6 2E-4 4E-4
ε equation y Pk+ ε+ Cε1Pk/ε+ Cε2ε2/k+ Left vertical axes: URANS region; right vertical axes: LES region.
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How can both the k eq. and ε be in local equilibrium??
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How can both the k eq. and ε be in local equilibrium?? If Pk = ε
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How can both the k eq. and ε be in local equilibrium?? If Pk = ε then C1 ε kPk=C∗
2
ε2 k , because C1 = C∗
2
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How can both the k eq. and ε be in local equilibrium?? If Pk = ε then C1 ε kPk=C∗
2
ε2 k , because C1 = C∗
2
However, the previous slide shows C1 ε k Pk
2
ε2 k
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How can both the k eq. and ε be in local equilibrium?? If Pk = ε then C1 ε kPk=C∗
2
ε2 k , because C1 = C∗
2
However, the previous slide shows C1 ε k Pk
2
ε2 k
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The answer is because of time averaging (ab < ab, (see below)
0.1 0.2 0.3 0.4 0.5 0.6 1 1.05 1.1 1.15 1.2
y εPk/k εPk/k ε2/k ε2/k
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0.1 0.2 0.3 0.4 1 2 3 4 5 6 7 8
Resolved y kres
0.1 0.2 0.3 0.4 1 2 3 4 5 6 7 8
Modelled: bottom; total: top y k, kres + k Reτ = 4 000 Reτ = 8 000 Reτ = 16 000; Reτ = 32 000.
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LRN PANS works well as zonal LES-RANS model for very high Reτ (> 32 000) The model gives grid independent results The location of the interface is not important (it should not be too close to the wall) Values of 0.2 < fk < 0.5 have little impact on the results
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Near walls: a RANS one-eq. k or a k − ω model. In core region: a LES one-eq. kSGS model. y x Interface wall wall URANS URANS LES y+
ml
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filtered in the core region, reads ∂¯ ui ∂t + ∂ ∂xj ¯ ui ¯ uj
ρ ∂¯ p ∂xi + ∂ ∂xj
ui ∂xj
νT = νsgs, y ≥ yml
same solution!
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∂kT ∂t + ∂ ∂xj (¯ ujkT) = ∂ ∂xj
∂xj
k3/2
T
ℓ PkT = 2νT ¯ Sij ¯ Sij, νT = Ckℓk1/2
T
LES-region: kT = ksgs, νT = νsgs, ℓ = ∆ = (δV)1/3 URANS-region: kT = k, νT = νt, ℓ ≡ ℓRANS = 2.5n[1 − exp(−Ak1/2y/ν)], Chen-Patel model (AIAA
Location of interface can be defined by min(0.65∆, y), ∆ = max(∆x, ∆y, ∆z)
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Instantaneous inlet data from channel DNS used. Domain: −8 ≤ x ≤ 48, 0 ≤ yinlet ≤ 1, 0 ≤ z ≤ 4. xmax = 40 gave return flow at the outlet Grid: 258 × 66 × 32. Re = UinH/ν = 18 000, angle 10o The grid is much too coarse for LES (in the inlet region ∆z+ ≃ 170) Matching plane fixed at yml at the inlet. In the diffuser it is located along the 2D instantaneous streamline corresponding to yml.
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H = 2δ 7.9H 21H 29H 4H 4.7H periodic b.c. convective outlet b.c. no-slip b.c.
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x = 3H 6 14 17 20 24H x/H = 27 30 34 40 47H
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x = 3H 6 14 17 20 24H x/H = 27 30 34 40 47H forcing; no forcing
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x = 3H 6 13 19 23H x/H = 26 33 40 47H resolved; modelled
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x = 3H 6 14 17 20 24H x/H = 27 30 34 40 47H forcing; no forcing At x = 24H, νT,max/ν ≃ 450 At x = −7H νT,max/ν ≃ 11
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DES [24]: Detached Eddy Simulation SST [18, 19]: A combination of the k − ε and the k − ω model ∂k ∂t + ∂ ∂xj (¯ ujk) = ∂ ∂xj
σk ∂k ∂xj
∂ω ∂t + ∂ ∂xj (¯ ujω) = ∂ ∂xj
σω ∂ω ∂xj
νt − βω2+ . . . The dissipation term in the k equation is modified as [19, 25] β∗kω → β∗kωFDES, FDES = max
CDES∆, 1
Lt = k1/2 β∗ω ⇒ RANS near walls and LES away from walls
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For the near-wall region, we know how fine the mesh should be in terms of viscous units (see Slide 22) An appropriate resolution for the fully turbulent part of the boundary layer is δ/∆x ≃ 10 − 20 and δ/∆z ≃ 20 − 40 This may be relevant also for jets and shear layers
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Energy spectra (both in spanwise direction and time) Two-point correlations Ratio of SGS turbulent kinetic energy ksgs to resolved 0.5u′u′ + v′v′ + w′w′ Ratio of SGS shear stress τsgs,12 to resolved u′v′ Ratio of SGS viscosity, νsgs to molecular, ν Energy spectra of SGS dissipation Comparison of SGS dissipation due to ∂u′
i/∂xj and ∂¯
ui/∂xj
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 55 / 1
10 10
1
10
2
10
10
10
10
Eww(kz) κz = 2π(kz − 1)/zmax Energy spectra
0.1 0.2 0.3 0.4
0.2 0.4 0.6 0.8 1
Bww(ˆ z)/w2
rms
ˆ z = z − z0 Two-point correlations (∆x, ∆z) 0.5∆x 0.5∆z
+: 2∆z The (∆x, ∆z) mesh is (δ/∆x, δ/∆z) = (10, 20)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 56 / 1
10 10
1
10
2
10
10
10
10
Eww(kz) κz = 2π(kz − 1)/zmax Energy spectra
0.1 0.2 0.3 0.4
0.2 0.4 0.6 0.8 1
Bww(ˆ z)/w2
rms
ˆ z = z − z0 Two-point correlations (∆x, ∆z) 0.5∆x 0.5∆z
+: 2∆z The (∆x, ∆z) mesh is (δ/∆x, δ/∆z) = (10, 20) Two-point correlation is better Shows that 2∆z and 2∆x (two-point corr in x) are too coarse.
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 56 / 1
1000 2000 3000 4000 1 2 3 4 5 6
kres y+ kres = (u′2 + v′2 + w′2)/2
1000 2000 3000 4000 0.4 0.5 0.6 0.7 0.8 0.9 1
γ y+ γ =
kres <kT >+kres
Pope [20] suggests γ > 0.8 indicates well resolved flow (∆x, ∆z) 0.5∆x 0.5∆z
+: 2∆z
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 57 / 1
1000 2000 3000 4000 1 2 3 4 5 6
kres y+ kres = (u′2 + v′2 + w′2)/2
1000 2000 3000 4000 0.4 0.5 0.6 0.7 0.8 0.9 1
γ y+ γ =
kres <kT >+kres
Pope [20] suggests γ > 0.8 indicates well resolved flow (∆x, ∆z) 0.5∆x 0.5∆z
+: 2∆z Pope criterion does not work here
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 57 / 1
1 2 3 4 0.2 0.4 0.6 0.8 1
y/H νsgs/ν
2 4 6 8 10 12
1
y/H νsgs/ν Nz = 32; Nz = 64; Nz = 128.
x = −H x = 20H
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 58 / 1
0.05 0.1 0.15 0.2 0.25 0.3 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
y/H τsgs,12/u′v′
0.01 0.02 0.03 0.04 0.05
y/H τsgs,12/u′v′ Nz = 32; Nz = 64; Nz = 128.
x = −H x = 20H
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 59 / 1
The PANS model is a modified k − ε model It can operate both in RANS mode and LES mode In the present work a low-Reynolds turbulence version of the PANS is used A method how to implement embedded LES is proposed It is evaluated for channel flow and hump flow
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 60 / 1
Embedded LES Using PANS [10, 11] Lars Davidson1 and Shia-Hui Peng1,2 Davidson& Peng
1Department of Applied Mechanics
Chalmers University of Technology, SE-412 96 Gothenburg, SWEDEN
2FOI, Swedish Defence Research Agency, SE-164 90, Stockholm,
SWEDEN
∂ku ∂t + ∂(kuUj) ∂xj = ∂ ∂xj
σku ∂ku ∂xj
∂εu ∂t + ∂(εuUj) ∂xj = ∂ ∂xj
σεu ∂εu ∂xj
εu ku − C∗
ε2
ε2
u
ku νu = Cµfµ k2
u
εu , C∗
ε2 = Cε1 + fk
fε (Cε2f2 − Cε1), σku ≡ σk f 2
k
fε , σεu ≡ σε f 2
k
fε Cε1, Cε2, σk, σε and Cµ same values as [1]. fε = 1. f2 and fµ read f2 =
3.1 2 1 − 0.3exp
Rt 6.5 2 fµ =
14 2 1 + 5 R3/4
t
exp
Rt 200 2 Baseline model: fk = 0.4. Range of 0.2 < fk < 0.6 is evaluated
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 62 / 1
d
x y δ 2.2δ LES, fk < 1 RANS, fk = 1.0 Interface Interface: Synthetic turbulent fluctuations are introduced as additional convective fluxes in the momentum equations and the continuity equation fk = 0.4 is the baseline value for LES [17]
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 63 / 1
0.5 1 1.5 2 0.5 1 1.5 2
y u′v′, v2
rms, w2 rms, u2 rms/u2 τ
0.1 0.2 0.3 0.4 0.5 0.2 0.4 0.6 0.8 1
w′w′ two-point corr ˆ z Anisotropic synthetic fluctuations, u′, v′, w′, Integral length scale L ≃ 0.13 (see 2-p point correlation) Asymmetric time filter (U′)m = a(U′)m−1 + b(u′)m with a = 0.954, b = (1 − a2)1/2 gives a time integral scale T = 0.015 (∆t = 0.00063)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 64 / 1
For ku & εu we prescribe “inlet” boundary conditions at the interface. First, the usual convective and diffusive fluxes at the interface are set to zero Next, new convective fluxes are added. Which “inlet” values should be used at the interface?
◮ ku,int = fkkRANS(x = 0.5δ), εu,int = C3/4
µ
k3/2
u,int/ℓsgs, ℓsgs = Cs∆,
∆ = V 1/3
◮ www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 65 / 1
For ku & εu we prescribe “inlet” boundary conditions at the interface. First, the usual convective and diffusive fluxes at the interface are set to zero Next, new convective fluxes are added. Which “inlet” values should be used at the interface?
◮ ku,int = fkkRANS(x = 0.5δ), εu,int = C3/4
µ
k3/2
u,int/ℓsgs, ℓsgs = Cs∆,
∆ = V 1/3
◮ Baseline Cs = 0.07; different Cs values are tested www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 65 / 1
10 10
1
10
2
5 10 15 20 25 30
y+ U+
0.5 1 1.5 2
0.5 1
y+ u′v′+ x/δ = 0.19 x/δ = 1.25 x/δ = 3
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 66 / 1
200 400 600 800 0.5 1 1.5 2 2.5 3 3.5
y/δ resolved stresses x/δ = 3
0.5 1 1.5 2 2.5 3 3.5 2 4 0.5 1 1.5 2 2.5 3 3.5 50 100
x u′u′+
max
νu/νmax u′u′+ u′u′+
max (left)
v′v′+ νu+
max (right)
w′w′+
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 67 / 1
ku,int = fkkRANS εu,int = C3/4
µ
k3/2
u,int/ℓsgs, ℓsgs = Cs∆
10 10
1
10
2
5 10 15 20 25 30
y+ U+ x/δ = 3
0.5 1 1.5 2
0.5 1
y+ u′v′+ Cs = 0.07 Cs = 0.1 Cs = 0.2
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 68 / 1
0.2 0.4 0.6 0.8 1 1 2 3 4 5 6
y+ νu/ν x/δ = 3
1 2 3 4 0.85 0.9 0.95 1 1.05
x/δ uτ Cs = 0.07 Cs = 0.1 Cs = 0.2
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 69 / 1
10 10
1
10
2
5 10 15 20
y+ U+ x/δ = 3
0.5 1 1.5 2
0.5 1
y+ u′v′+ fk = 0.4 fk = 0.2 fk = 0.6
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 70 / 1
0.2 0.4 0.6 0.8 1 0.5 1 1.5 2 2.5 3 3.5 4
y+ νu/ν x/δ = 3
1 2 3 4 0.85 0.9 0.95 1 1.05
x/δ uτ fk = 0.4 fk = 0.2 fk = 0.6
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 71 / 1
xI/c = 0.6 R S NTS 2D RANS PANS Inlet, Separation xS/c = 0.65; reattachment xR/c = 1.1 Rec = 936 000 Uijc
ν
(Uin = c = ρ = 1, ν = 1/Rec H/c = 0.91, h/c = 0.128, x/c = [0.6, 4.2] Mesh: 312 × 120 × 64, Zmax = 0.2c (baseline)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 72 / 1
1 2 3 4 5 6 0.15 0.2 0.25 0.3 0.35 0.4
y/c u′v′, v2
rms, w2 rms, u2 rms/u2 τ
0.02 0.04 0.06 0.08 0.1 0.2 0.4 0.6 0.8 1
w′w′ two-point corr ˆ z Integral length scale L ≃ 0.04 (see 2-p point correlation) Asymmetric time filter (U′)m = a(U′)m−1 + b(u′)m with a = 0.954, b = (1 − a2)1/2 gives a time integral scale T = 0.038 ∆t = 0.002. 7500 + 7500 time steps (100 hours one core) Fluctuations multiplied by fbl = max {0.5 [1 − tanh(y − ybl − ywall)/b] , 0.02}, ybl = 0.2, b = 0.01.
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 73 / 1
0.5 1 1.5 2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
x/c −Cp baseline inlet fluct 1.5× (baseline inlet fluct) 0.5× (baseline inlet fluct)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 74 / 1
0.5 1 1.5
2 4 6 8 10x 10
x/c Cf
0.6 0.8 1 1.2 1.4 1.6
1 2 3x 10
x/c zoom baseline inlet fluct 1.5× (baseline inlet fluct) 0.5× (baseline inlet fluct)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 75 / 1
0.2 0.4 0.6 0.8 1 1.2 0.1 0.15 0.2 0.25
x/c = 65 y
0.5 1 0.05 0.1 0.15 0.2 0.25
x/c = 80
0.5 1 0.05 0.1 0.15 0.2 0.25
x/c = 100 U/Ub y
0.5 1 0.05 0.1 0.15 0.2 0.25
x/c = 110 U/Ub baseline 1.5× (baseline) 0.5× (baseline)
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0.2 0.4 0.6 0.8 1 1.2 0.05 0.1 0.15 0.2 0.25
x/c = 120 U/Ub y
0.2 0.4 0.6 0.8 1 1.2 0.05 0.1 0.15 0.2 0.25
x/c = 130 U/Ub baseline 1.5× (baseline) 0.5× (baseline)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 77 / 1
5 10 15 x 10
0.1 0.15 0.2 0.25
x/c = 0.65 y
0.01 0.02 0.03 0.05 0.1 0.15 0.2 0.25
x/c = 0.80
0.01 0.02 0.03 0.04 0.05 0.1 0.15 0.2
x/c = 1.00 τ12,u, −u′v′/U2
b
y
0.01 0.02 0.03 0.05 0.1 0.15 0.2
x/c = 1.10 τ12,u, −u′v′/U2
b
baseline 1.5× (baseline) 0.5× (baseline)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 78 / 1
Resolved and Modelled (< 0) Shear stresses
0.01 0.02 0.03 0.05 0.1 0.15 0.2
x/c = 1.20 τ12,u, −u′v′/U2
b
y
0.01 0.02 0.03 0.05 0.1 0.15 0.2
x/c = 1.30 τ12,u, −u′v′/U2
b
baseline inlet fluct 1.5× (baseline inlet fluct) 0.5× (baseline inlet fluct)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 79 / 1
5 10 15 20 0.1 0.12 0.14 0.16 0.18 0.2
x/c = 0.65 y
20 40 60 80 100 120 0.05 0.1 0.15 0.2
x/c = 0.80
20 40 60 80 100 120 0.05 0.1 0.15 0.2
x/c = 1.00 νt/ν y
20 40 60 80 100 120 0.05 0.1 0.15 0.2
x/c = 1.10 νt/ν baseline 1.5× (baseline) 0.5× (baseline)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 80 / 1
20 40 60 80 100 120 0.05 0.1 0.15 0.2
x/c = 1.20 νt/ν y
20 40 60 80 100 120 0.05 0.1 0.15 0.2
x/c = 1.30 νt/ν baseline 1.5× (baseline) 0.5× (baseline)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 81 / 1
0.5 1 1.5 2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
x/c −Cp Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 82 / 1
0.5 1 1.5
2 4 6 8 10x 10
x/c Cf
0.6 0.8 1 1.2 1.4 1.6
1 2 3x 10
x/c zoom Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 83 / 1
0.2 0.4 0.6 0.8 1 1.2 0.1 0.15 0.2 0.25
x/c = 65 y
0.5 1 0.05 0.1 0.15 0.2 0.25
x/c = 80
0.5 1 0.05 0.1 0.15 0.2 0.25
x/c = 100 U/Ub y
0.5 1 0.05 0.1 0.15 0.2 0.25
x/c = 110 U/Ub Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 84 / 1
0.2 0.4 0.6 0.8 1 1.2 0.05 0.1 0.15 0.2 0.25
x/c = 120 U/Ub y
0.2 0.4 0.6 0.8 1 1.2 0.05 0.1 0.15 0.2 0.25
x/c = 130 U/Ub Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 85 / 1
5 x 10
0.1 0.15 0.2 0.25
x/c = 0.65 y
0.01 0.02 0.03 0.04 0.05 0.1 0.15 0.2 0.25
x/c = 0.80
0.01 0.02 0.03 0.04 0.05 0.1 0.15 0.2
x/c = 1.00 τ12,u, −u′v′/U2
b
y
0.01 0.02 0.03 0.05 0.1 0.15 0.2
x/c = 1.10 τ12,u, −u′v′/U2
b
Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 86 / 1
Resolved and Modelled (< 0) Shear stresses
0.01 0.02 0.03 0.05 0.1 0.15 0.2
x/c = 1.20 τ12,u, −u′v′/U2
b
y
0.01 0.02 0.03 0.05 0.1 0.15 0.2
x/c = 1.30 τ12,u, −u′v′/U2
b
Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 87 / 1
5 10 15 20 0.1 0.12 0.14 0.16 0.18 0.2
x/c = 0.65 y
50 100 150 200 250 0.05 0.1 0.15 0.2
x/c = 0.80
50 100 150 200 250 0.05 0.1 0.15 0.2
x/c = 1.00 νt/ν y
50 100 150 200 250 0.05 0.1 0.15 0.2
x/c = 1.10 νt/ν Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 88 / 1
50 100 150 200 250 0.05 0.1 0.15 0.2
x/c = 1.20 νt/ν y
50 100 150 200 250 0.05 0.1 0.15 0.2
x/c = 1.30 νt/ν Nk = 128 no inlet fluct fk = 0.5
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 89 / 1
LRN PANS has been shown to work well as an embedded LES method Channel flow: At two δ downstream the interface, the resolved turbulence in good agreement with DNS data and the wall friction velocity has reached 99% of its fully developed value. Channel flow: The treatment of the modelled ku and εu across the interface is important. LRN PANS predicts the hump flow well but the recover rate sligtly too slow Hump flow: large (small) inlet fluctuations gives a smaller (larger) recirculation
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 90 / 1
Embedded LES with k − ε PANS and Synthetic b.c.
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Embedded LES with k − ε PANS and Synthetic b.c. Channel flow
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 91 / 1
Embedded LES with k − ε PANS and Synthetic b.c. Channel flow
◮ Isotropic fluctuations work well for channel flow www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 91 / 1
Embedded LES with k − ε PANS and Synthetic b.c. Channel flow
◮ Isotropic fluctuations work well for channel flow ◮ Strong dependence on interface ku & εu values www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 91 / 1
Embedded LES with k − ε PANS and Synthetic b.c. Channel flow
◮ Isotropic fluctuations work well for channel flow ◮ Strong dependence on interface ku & εu values ◮ No strong dependence on amplitude, L or T of fluctuations www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 91 / 1
Hump flow
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 92 / 1
Hump flow
◮ PANS & synthetic inlet b.c. with fk everywhere gives good results
except Cf (error > 50%)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 92 / 1
Hump flow
◮ PANS & synthetic inlet b.c. with fk everywhere gives good results
except Cf (error > 50%)
◮ With embedded isotropic fluctuations, interface must be located far
upstream
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 92 / 1
Hump flow
◮ PANS & synthetic inlet b.c. with fk everywhere gives good results
except Cf (error > 50%)
◮ With embedded isotropic fluctuations, interface must be located far
upstream
◮ With embedded anisotropic fluctuations, good results are obtained,
still poor Cf
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 92 / 1
Hump flow
◮ PANS & synthetic inlet b.c. with fk everywhere gives good results
except Cf (error > 50%)
◮ With embedded isotropic fluctuations, interface must be located far
upstream
◮ With embedded anisotropic fluctuations, good results are obtained,
still poor Cf
◮ On-going work . . . www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 92 / 1
Large Eddy Simulation of Heat Transfer in Boundary layer and Backstep Flow Using PANS [6] Lars Davidson THMT-12, Palermo, Sept 2012
∂ku ∂t + ∂(kuUj) ∂xj = ∂ ∂xj
σku ∂ku ∂xj
∂εu ∂t + ∂(εuUj) ∂xj = ∂ ∂xj
σεu ∂εu ∂xj
εu ku − C∗
ε2
ε2
u
ku νu = Cµfµ k2
u
εu , C∗
ε2 = Cε1 + fk
fε (Cε2f2 − Cε1), σku ≡ σk f 2
k
fε , σεu ≡ σε f 2
k
fε Cε1, Cε2, σk, σε and Cµ same values as [1]. fε = 1. f2 and fµ read f2 =
3.1 2 1 − 0.3exp
Rt 6.5 2 fµ =
14 2 1 + 5 R3/4
t
exp
Rt 200 2 Baseline model: fk = 0.4.
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 94 / 1
Incompressible finite volume method Pressure-velocity coupling treated with fractional step Differencing scheme for momentum eqns:
◮ 95% 2nd order central and 5% 2nd order upwind differencing
scheme (baseline) OR
◮ 100% 2nd order central differencing
Hybrid 1st order upwind/2nd order central scheme k & ε eqns. 2nd-order Crank-Nicholson for time discretization
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 95 / 1
x y L H δinlet Inlet: δinlet = 1 (covered by 45 cells), Reθ = 3 600, Uin = ρ = 1. Stretching 1.12 up to y/δ ≃ 1. Domain: L/δin = 3.2, H/δin = 15.6, Zmax = 1.5δin Resolution: ∆z+
in ≃ 27, ∆x+ in ≃ 54
Grid: 66 × 96 × 64 (x, y, z)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 96 / 1
Prescribe an homogeneous Reynolds tensor, uiuj (here from DNS)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 97 / 1
( u
′ 1
u
′ 1
)
λ
x1,λ ( u
′ 2
u
′ 2
)
λ
x2,λ u′
1,λu′ 2,λ = 0
Prescribe an homogeneous Reynolds tensor, uiuj (here from DNS) isotropic fluctuations in principal directions, (u′
1u′ 1)λ = (u′ 2u′ 2)λ,
u′
1,λu′ 2,λ = 0
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 97 / 1
( u
′ 1
u
′ 1
x1,λ ( u
′ 2
u
′ 2
x2,λ u′
1,λu′ 2,λ = 0
Prescribe an homogeneous Reynolds tensor, uiuj (here from DNS) isotropic fluctuations in principal directions, (u′
1u′ 1)λ = (u′ 2u′ 2)λ,
u′
1,λu′ 2,λ = 0
re-scale the normal components, (u′
1u′ 1)λ > (u′ 2u′ 2)λ,
u′
1,λu′ 2,λ = 0
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 97 / 1
u′
1u′ 1 > u′ 2u′ 2
x1 u′
2u′ 2
x2 u′
1u′ 2 = 0
Transform from (x1,λ, x2,λ) to (x1, x2) u′2
1
u′2
2
= 23, u′2
1
u′2
3
= 5 from (u′
1u′ 1)peak in DNS channel flow, Reτ = 500
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 98 / 1
A pre-cursor RANS simulation using the AKN model (i.e. PANS with fk = 1) is carried out. At Reθ = 3 600, URANS, VRANS, kRANS are taken. ¯ uin = URANS + u′
synt, ¯
vin = VRANS + v′
synt, ¯
win = w′
synt
Anisotropic synthetic fluctuations are used. The fluctuations are scaled with ku/ku,max. ku,in = fkkRANS, εu,in = C3/4
µ
k3/2
u,in/ℓsgs, ℓsgs = Cs∆, ∆ = V 1/3,
Cs = 0.05
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 99 / 1
2 4 6 200 400 600 800 1000
stresses y/H
0.1 0.2 0.3 0.4 0.5 0.2 0.4 0.6 0.8 1
ˆ z/δ, ˆ z/H Bww(ˆ z) Two-point correlation u+2
rms,
v+2
rms,
w+2
rms
u′v′+
x = 3δin
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 100 / 1
1 10 50 1000 5 10 15 20 25
y+ U+ 100%CDS
0.5 1 1.5 2 2.5 3 2.6 2.8 3 3.2 3.4 3.6 x 10
x Cf x = δin; x = 2δin; x = 3δin; : DNS [21] 100% CDS; 100% CDS, Uin from AKN; 25% larger inlet fluct.; 25% larger inlet fluct., Cs = 0.07; markers: 0.37 (log10Rex)−2.584 (+: AKN; ◦: DNS)
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 101 / 1
1 2 3 500 1000 1500
y+ u′v′ u′u′
1 2 3 500 1000 1500
y+ u′v′ v′v′, w′w′, u′u′ x = δin; x = 2δin; x = 3δin. x = 3δin; Markers: DNS [21]
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 102 / 1
4.05H 21H 4H H x y qw ReH = 28 000 Experiments by Vogel & Eaton [26] Mean inlet profiles from RANS (same as in boundary layer) Grid: 336 × 120 in x × y plane. Zmax = 1.6H, Nk = 64, ∆z+
in = 31.
Anisotropic synthetic fluctuations, u′, v′, w′ (same as for boundary layer flow); no fluctuations for t′ Constant heat flux, qw, on lower wall.
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 103 / 1
NUMBER
5 10 15 20
1 2 3 4 x 10
x/H Cf
5 10 15 1 1.5 2 2.5 3 3.5 4x 10
x/H St PANS; PANS, 50% smaller inlet fluctuations; WALE; •: PANS, no inlet fluctuations; : 2D RANS; ◦,•: experiments [26].
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 104 / 1
0.2 0.4 0.6 0.8 1 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
¯ u/Uin x = −1.13H
0.2 0.4 0.6 0.8 1 0.5 1 1.5 2 2.5
¯ u/Uin x = 3.2H
0.2 0.4 0.6 0.8 1 0.5 1 1.5 2 2.5
¯ u/Uin x = 14.86H PANS; PANS, 50% smaller inlet fluctuations; WALE;
: 2D RANS; ◦: experiments [26].
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0.05 0.1 0.15 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
urms/Uin y/H x = −1.13H
0.05 0.1 0.15 0.2 0.5 1 1.5 2 2.5
urms/Uin x = 3.2H
0.05 0.1 0.15 0.5 1 1.5 2 2.5
urms/Uin x = 14.86H PANS; PANS, 50% smaller inlet fluctuations; WALE;
: 2D RANS; ◦: experiments [26].
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2 4 6 8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
νu/ν y/H x = −1.13H
5 10 15 20 0.5 1 1.5 2 2.5
νu/ν x = 3.2H
5 10 15 0.5 1 1.5 2 2.5
νu/ν x = 14.86H PANS; PANS, 50% smaller inlet fluctuations; WALE;
: 2D RANS/10;
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Fraction of time, γ, when the flow along the bottom wall is in the downstream direction.
2 4 6 8 10 12 14 0.2 0.4 0.6 0.8 1
x/H γ PANS; PANS, 50% smaller inlet fluctuations; WALE;
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5 10 15 x 10
0.02 0.04 0.06 0.08 0.1
y/H PANS
5 10 15 x 10
0.02 0.04 0.06 0.08 0.1
RANS 2νt¯ s12; ν ∂¯ u ∂y ; −u′v′; ◦: 2νt¯ s12−u′v′.
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0.2 0.4 0.6 0.8 1 x 10
0.02 0.04 0.06 0.08 0.1
y/H PANS
0.2 0.4 0.6 0.8 1 x 10
0.02 0.04 0.06 0.08 0.1
RANS 2νt¯ s12; ν ∂¯ u ∂y ; −u′v′; ◦: 2νt¯ s12−u′v′.
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0.02 0.04 0.06 0.02 0.04 0.06 0.08 0.1
y/H PANS
0.02 0.04 0.06 0.02 0.04 0.06 0.08 0.1
RANS ∂ ∂y (2νt¯ s12); ν ∂2¯ u ∂y2 ; −∂¯ u¯ u ∂x ; +: −∂¯ u¯ v ∂y ; ⋆: −∂¯ p ∂x , △: −∂u′v′ ∂y .
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0.02 0.04 0.06 0.02 0.04 0.06 0.08 0.1
y/H PANS
0.02 0.04 0.06 0.02 0.04 0.06 0.08 0.1
RANS ∂ ∂y (2νt¯ s12); ν ∂2¯ u ∂y2 ; −∂¯ u¯ u ∂x ; +: −∂¯ u¯ v ∂y ; ⋆: −∂¯ p ∂x , △: −∂u′v′ ∂y .
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0.02 0.04 0.06 0.08 0.1
y/H PANS
0.02 0.04 0.06 0.08 0.1
RANS νt σt ∂ ¯ T ∂y
ν σℓ ∂¯ T ∂y ; −vθ. ◦: νt σt ∂ ¯ T ∂y
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 113 / 1
0.02 0.04 0.06 0.08 0.1
y/H PANS
0.02 0.04 0.06 0.08 0.1
RANS νt σt ∂ ¯ T ∂y
ν σℓ ∂¯ T ∂y ; −vθ. ◦: νt σt ∂ ¯ T ∂y
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50 100 0.02 0.04 0.06 0.08 0.1
y
PANS
50 100 0.02 0.04 0.06 0.08 0.1
RANS ∂ ∂y νt σt ∂¯ T ∂y
ν σℓ ∂2¯ T ∂y2 ; −∂¯ u¯ T ∂x ; +: −∂¯ v¯ T ∂y ; △: −∂vθ ∂y .
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50 0.02 0.04 0.06 0.08 0.1
y/H PANS
50 0.02 0.04 0.06 0.08 0.1
RANS ∂ ∂y νt σt ∂¯ T ∂y
ν σℓ ∂2¯ T ∂y2 ; −∂¯ u¯ T ∂x ; +: −∂¯ v¯ T ∂y ; △: −∂vθ ∂y .
www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 116 / 1
Developing boundary layer
◮ Synthetic fluctuations give fully developed conditions after a couple
◮ 5% upwinding dampens resolved fluctuations; can be compensated
by 25% larger inlet fluctuations
Backstep flow
◮ Very good agreement with experiments ◮ 2D RANS predicts turbulent diffusion surprisingly well ◮ Synthetic inlet fluctuations give an improved Stanton number;
◮ LRN PANS and WALE equally good ◮ 5% upwinding has a negligble effect in the recirculation region www.tfd.chalmers.se/˜lada Helsinki 4 October 2012 117 / 1
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