materials performance centre seminars, 12/09/2006
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Materials Performance Centre Seminars, 12/09/2006. Application of Large Eddy Simulation to thermal-hydraulics in the Nuclear Power Generation Industry. [email protected] School of Mech, Aero & Civil Eng. Fluids AIG / CFD group [email protected] EDF R&D Chatou. - PowerPoint PPT PresentationTRANSCRIPT
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Materials Performance Centre Seminars, 12/09/2006
Application of Large Eddy Simulation
to thermal-hydraulics
in the Nuclear Power Generation Industry.
[email protected] of Mech, Aero & Civil Eng.
Fluids AIG / CFD group
[email protected] R&D Chatou
Contributions: Y. Addad, I. Afgan, S. Benhamadouche, S. Berrouk, N Jarrin C. Moulinec, T. Pasutto,
The
Uni
vers
ity
of M
anch
este
r
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Osborne Reynolds 1868, became a professor of engineering at Owens College (now University of Manchester)
Reynolds tank, G. Begg building
Low speed jet
Higher speed jet
Turbulence, Reynold Number Re = UD/visc.
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Kolmogorov Energy Cascade
2 212
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Assume : ( ) sin( ) 2 /
. sin( )cos( ) sin(2 )
thus energy transfer from 2 / to 2 2 /( )
i i i i
i i i i i i i
i i i i
u x u k x k L
duu u k k x k x u k k xdx
k L k L
Андре́5й Никола́5е́вич Колмого5ров
Moscow State Uni. 1939
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Solve the standard (Navier Stokes) flow equation on a very fine mesh- All scales are resolved on that mesh (down to Kolmogorov scale)- No model needed- Results considered as valid as experimental data- Enormous computer resources, for even modest speed-domain size (Reynolds number)
Sergio Hoyas and Javier Jimenez, (2006) "Scaling of velocity fluctuations in turbulent channels up to Re_tau = 2000", Phys. of Fluids, vol 18,
Direct Numerical Simulation (DNS)
Nx=6144, Ny=633, Nz=4608 points = 17,921,212,416 cells
6 Million processor hours on 2048 processors,
Barcelona Supercomputing Center
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Luckily, only Large Scales matter (most of the time)
Large Scales & Human activity• Drag, mixing, heat transfer, • Large Scales dictate flow physics• Generated by/scale with obstacle• Impose dissipation rate
Exceptions: noise, combustion,
Weather forecast (we are the small scales !)
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Large Eddy Simulation = Filtering
1( , ) ( , ') '
2
t T
t Tu x t u x t t dt
T
drrxGtrutxuV
)(),(),(
CFD codes naturally induce filter = 2 dx
Space Filter
Time Filter
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DHIT: Decay of Hom. Iso. Turb.
MANDATORY test case for first time LES !
Reveals numerical dissipation, stability,
G rid
S ta tio n4 2
S ta tio n9 8
U = 1 0 m s0
-1
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• Direct Numerical Simulation (DNS) databases <=> Experiments
= “Costly” Fluid Dyn., exceptional, limited to zoom effect,
- 100% accurate, back to 1st principles, NO modelling hypothesis
• Large Eddy Simulation (LES)= “Colourful” Fluid Dyn., much detail,
fluctuations, spectra,- Applicable Eng. problems, at some cost- Almost as reliable as DNS, but know-how
required, not well established
• Reynolds Averaged (RANS)= “Conventional” Fluid Dyn., used daily in
Eng., only mean values (B&W)- Economical, full reactor or sub-component
design (parametric) possible- Problem: wide range of models to choose
from, - needs improvement & validation for new
range of applications (high temperature, buoyancy, conjugate heat
transfer, )
Future : Coupling of RANS and LES, using DNS for insight & validation
3 Levels of CFD approaches to turbulent flow
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Industrial LES applications to reactor thermal hydraulics
LES is mostly about numerical methods
Grid able to capture most turbulent scales Easier in Power Industry (confined, non-streamlined geometry) Local/embedded grid refinement, polyhedral scales
Boundary conditions Walls => quasi DNS (wall functions not ideal) Some real periodic geometry. pb. in Power Gen (tube bundles) Synthetic inlet turbulence
Target values Order of Mag. (within 10%), not 0.01 on Cd Thermal mixing & loading, spectra, vibrations….
Need Fast Unstructured FV Solver
EDF code Saturne & Star-CD very similar
High Accuracy Numerical Scheme No numerical dissipation
( Central differencing, Second order in time) Avoid any mesh distortion
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LES at EDF: Thermal stresses in T junction
Configuration studied : Thot = 168°C , Tcold = 41°CFlow rate = 1000 m3/h, ratio 20%
- Experimental mock-up (both thermalhydraulics and thermal fatigue mechanical aspects)
- models and numerical tools to gain a better understanding.
Length of the numerical simulation : 11 seconds
QhotQcold
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EDFElectricitéde FranceFluid results
Instantaneous fluid temperature field (Code_Saturne)
(Peniguel et al. ASME-PVP Cleveland 2003)Shortcut to aaPVP_anim.mpg.lnk
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Solid and fluid meshes
Solid mesh : 958 975 nodeswidth of the first solid element : 100 microns
Fluid mesh: 401 472 cells
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Instantaneous fluid temperature Instantaneous solid temperature
Time (s)
Instantaneous solid temperature field (Syrthes)(location C12)
C12
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Location CEX1
- On site meas
-L.E.S.
- Mock-up
CEX1
Frequency (Hz)
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T Junction, stratified case
Fluid temperature and flow structure
Recirculation zone
C12
Temperature In the symmetry plane (case 1)
Temperature near the wall (case 1)
VH = 3,37 m/s
TH = 204°C
VC = 0,77 m/s
TC = 41°C
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Analysis of the results
Attenuation of the fluctuations by the wall thermal inertia
( Ring C12 – 50 °)
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Mesh too coarse for Re=1 Milliontemperatures (ring C12) fluid: probe located at 10° probe located at 50°
Solid:
underestimation of the fluctations (especially at 10°) limitation of the wall function approach ?
LES
Exp
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Fluid meshes 1st mesh : ~ 500 000 hexaedric cells y+~ 300 2nd mesh : ~ 1 000 000 hexaedric cells y+~ 170 (first node at 0,38 mm from the wall)
(1 000 000 cell mesh)
FAATER exp. : Meshes
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Outflow region
Jet
x
y
Cold upflow
Trust & Quality-CFD project: hot wall jet (Magnox case)
Buoyancy: none medium highVelocity: high medium low
3-D view (STCL2). A: t1 = 2114 D j /V bjÝsÞ, B: t t2 = t1 + 18. 5D j /V bjÝsÞ.
Addad Y. , D. Laurence and S. Benhamadouche. The Negative Buoyant Wall Jet: LES Results, I.J. Heat and Fluid Flow, 25, 795-808, 2004
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Grid generation (buoyant case)
● Pre- k-eps simulation
● cell Volume near jet inlet (V)1/3=0.002● y+=1
● In mixing region (V)1/3=0.007
● NCELLS=770 000
●StarCD code
IntegralLength scalefrom k-epsilon
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Horizontal Velocity Comparison
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LES DB => Analytic Wall Function development
(from A. Gerasimov)
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Thermal hydraulics of reactors
Study the physics of the flow in the decay heat inlet pen
Examine the LES solution of the code Star-CD for the natural/mixed convection cases.
Validate further the analytical wall functions developed at UMIST by Gerasimov et al.
Mixed convection in co-axial pipes(Y. Addad PhD, M. Rabitt British Energy)
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K-eps pre-study
Streamlines coloured by temperature
Cold Inlet Pipe in vessel => stratification trap
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Coaxial heated cylinder study
• LES validation and parametric test cases: Case1-Natural convection in square cavity (Ra=1.58 109) Case2-Natural convection in annular cavity (Ra=1.8109) Case3- annular cavity single coaxial cylinder (Ra=2.381010) Case4- annular cavity with 3 coaxial cylinders (Ra=2.381010) Case5- Flow in actual penetration cavity (bulk Re=620,000).
Bishop 88, McLeod 89
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CASE-4: Ra=2.3810E+10
CASE-3: Ra=2.3810E+10
Natural Convection in coaxial cylinders
Case 2: Ra=1.810E+9SGS visc/Molecular visc.<1
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3 Cylinders
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Flow through in-line tube bundles
STAR CCM grid
Mean pressure
gradient
Direction is in-lin
e
Large heat exchanger=> Homogeneous conditions
=> Periodic subset considered
Re=45 000, P/D= 1.5
Objectives: Flow induced vibrations in heat exchangers (Lift & Drag coef.)
Staggered: studied 10 years agoCurrent: in line
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In-line tube bundle
[email protected], with STAR-CCM
- Fully symmetric conditions, but non-symmetric solutions
- Coanda effect ?
- Star-CCM LES launched to confirm EDF finding(Benhamadouche et al. NURETH 11, Avignon 2005)
Time averaged velocity field =>
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In-line tube bundle P/D=1.5
-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
0 45 90 135 180 225 270 315 360
Azimuthal Angle
No
rma
lize
d P
res
su
re
SATURNE
STAR CCM
STAR CCMMean velocity
(Afgan)
EDF Code-SaturneMean velocity
(Benhamadouche)
mean pressure
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-Vortex Method for Inlet
-Polyhedral Cells (670,000)
-Re=57,400
-Y+=2 (Prisms)
-L1=3D; L2=5D
- Smagorinsky Model, Van Driest Damping
LES in a 180° U-Bend Pipe
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U-Bend Pipe Cross Sections: Mean flow
45 degrees
177 degrees
135 degrees
90 degrees
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Research: Reconstructing fluctuations
Real Eddies in Channel flowCost = 5 days computing
Synthetic Eddies in Channel flowCost = 5 seconds computing
Matches all rms values andgiven spectrum
3-D view (STCL2). A: t1 = 2114 D j /V bjÝsÞ, B: t t2 = t1 + 18. 5D j /V bjÝsÞ.
KNOO project:Develop similar technique to
reconstruct temperature fluctuations at solid wall
Link with materials ageing research
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Research: Mesh strategy for LES
a)
Possible FV near-wall refinements: a) dichotomy, b) non-conforming, c) & d) polyhedral & zoom.
- Non dissipative Finite Volume Methods
- Optimal meshing strategy for LES
- Quality criteria for LES General but essential issue. Collaboration with:- CD – Adapaco (STAR-CD code)- EDF R&D (Saturne code)- Health & Safety Labs, CFD for Nuclear Reactor licensing ?
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DNS at Re* N Cells
395 10 Million
640 28 Million
720 84 Million
2000 17,921 Million
RANS LES
Under-resolved LESRANS – LES coupling
U
Wall distance
J. Uribe, Manchester
Research: RANS – LES coupling
LES: ~ 0.1 Million cells
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Conclusions – Industrial LES
• LES of Industrial flow• Much more information:
Thermal stresses, fatigue, Acoustics, FIV (vibrations)• Cost-wise accessible when limited to subdomain
(synthetic turbulence for inlet)• Complex geometry possibly easier than smooth channel flow (academic overkill ?)
• Flexible flexibility with professional/commercial software:
• Opens new range of applications for LES• Medium Re number : DNS near wall resolution possible• Greater breakthrough than elaborate SGS models?
• Further developments: • More meshing control (total cell size control from pre-simulation)• High Re : RANS –LES coupling, embedded LES
• Cross-discipline research: Fluids / Structure-Mech/ Materials ?Cracks, Thermal stripping, ageing,
corrosion …