review of thermofluid / mhd activities for dcll sergey smolentsev & us tbm thermofluid/mhd group...
DESCRIPTION
DCLL is current US blanket choice for DEMO and testing in ITER DCLL DEMO B-field ITER TBM Blanket performance is strongly affected by MHD phenomena Studying MHD in DCLL conditions is one of the most important goals FCI He PbLi SiC/SiC FCI is the key element of DCLLTRANSCRIPT
Review of Thermofluid / MHD activities for DCLL
Sergey Smolentsev & US TBM Thermofluid/MHD Group
2006 US-Japan Workshop on FUSION HIGH POWER DENSITY COMPONENTS and SYSTEMS
Santa Fe, New Mexico, USANov. 15-17, 2006
Outline
• Introduction. MHD phenomena in DCLL blankets
• Scaling analysis for DCLL DEMO and ITER TBM
• Particular MHD phenomena• MHD software development: HIMAG • Experiment
DCLL is current US blanket choice for DEMO and testing in ITER
DCLL DEMO
B-field
ITER TBM
•Blanket performance is strongly affected by MHD phenomena
•Studying MHD in DCLL conditions is one of the most important goals
FCI
He
PbLi
SiC/SiC FCI is the keyelement of DCLL
Thermofluid / MHD activities cover two major areas: (I) Design, (II) R&D Thermofluid / MHD issues of the DCLL blanket:
•Effectiveness of FCI as electric/thermal insulator•MHD pressure drop•Flow distribution and balancing•Heat transfer
• physical/mathematical model development• code development• numerical simulations• experiments
These issues are being addressed via:
Heat Transfer in DCLL blankets is strongly affected by fluid flow phenomena, where MHD
plays a major roleA. Formation of high-
velocity near-wall jets
B. 2-D MHD turbulence in flows with M-type velocity profile
C. Reduction of turbulence via Joule dissipation
D. Natural/mixed convection
E. Strong effects of MHD flows and FCI properties on heat transfer
-0.15 -0.1 -0.05 0 0.05 0.1 0.15Radia l d istance, m
400
800
1200
1600
Tem
pera
ture
, C
lam inar flow m odeltu rbulen t flow m odel
DEMO
E
g
DB
=500
=100
=5
A C
Key DCLL parameters (outboard)
Parameter DEMO ITER H-H ITER D-TSurface heat flux, Mw/m2 0.55 0.3 0.3
Neutron wall load, Mw/m2 3.08 - 0.78
PbLi In/Out T, C 500/700 470/~450 360/470
2a x 2b x L, m 0.22x0.22x2 0.066x0.12x1.6 0.066x0.12x1.6
PbLi velocity, m/s 0.06 0.1 0.1
Magnetic field, T 4 4 4
MHD / Heat Transfer phenomena in ITER can bequantitatively/qualitatively different from those in DEMO
Engineering scaling (poloidal flow)
PARAMETER ITER D-T DEMORe 30,500 61,000
Ha 6350 11,640
Ha/Re 0.208 0.190
N 1320 2217
Gr 7.22x109 3.52x1012
r 11.1 70.3
Gr/Re 2.36x105 5.76x107
Ha/Gr 8.80x10-7 3.31x10-9
a/b 0.55 1.0
L/a 50 18
Major differences between ITER and DEMO are expected for buoyancy-driven flows, which are much more intensive in DEMO conditions
Formation of near-wall jets and MHD pressure drop reduction by FCI
a b
a b
No pressure equalization openings
With a pressure equalization slot
DCLL unit-cell with FCI
0.01 0.1 1 10 100 1000SiC /S iC E lectric C onductivity, S /m
0
200
400
600
800
Pre
ssur
e dr
op re
duct
ion
fact
or No pressure equaliza tionopen ingsS lo t in the H artm ann wa llS lo t in the paralle l w all
MHD pressure drop reduction by FCI
DEMO (old)B=4 THa=16,000
Study of MHD buoyancy-driven flowsA. Numerical simulation of unsteady buoyancy-driven flows
B. Analytical solution for steady mixed convection
(a)
(b)
B
Poloidal distance
2
Re
baHa
Grr
Present computationsare limited to Gr~107.The near goal is toachieve Gr~109-1012.
Modeling of 2-D MHD turbulence•Two eddy-viscosity models (zero- and one-equation) have been developed and tested against experimental data (MATUR)•2-D DNS was performed for flows with internal shear layers to address the effect of bulk eddies on the boundary layer•One-equation model was used in heat transfer calculations for DCLL
2-D DNS
Transitions in MHD flows in a gradient magnetic field
BC: Flow will be unstable if the Hartmann number built through the magnetic field gradient > ~ 5
A. Linear stability analysis
l0 x
0
y
xU(y) (y)
Sketch of the problem. Formation of the double row of staggered vortices from the internal shear layers.
B. Nonlinear analysis
Heat transfer for 3 DCLL scenarios:DEMO, ITER H-H, ITER D-T
FSG
AP
FCI
Pb-L
i
100 S/m
20 S/m
FCI = 5 S/m
Temperature Profile for Model DEMO Case
kFCI = 2 W/m-K
Parametric analysis at: 0.01<<500, 2<k<20
• Preliminary identification of required SiC FCI properties:
~100 S/m, k~2 W/m-K• The most critical requirement is
that on T across the FCI. Near-wall jet allows for lower T
• Reduction of the jet effect via instabilities, turbulence, buoyancy-driven flows ?
• Narrow design window• Further MHD analysis is
necessary
MHD software development: HIMAG• The HyPerComp Incompressible
MHD Solver for Arbitrary Geometry (HIMAG) has been developed over the past several years by a US software company HyPerComp with some support from UCLA.
• At the beginning of the code design, the emphasis was on the accurate capture of a free surface in low to moderate Hartmann number flows.
• At present, efforts are directed to the code modification and benchmarking for higher Hartmann number flows in typical closed channel configurations relevant to the DCLL blanket.
y / a
U / U0
Rectangular duct, Ha=10,000
Circular pipe, Ha=1000
MTOR Laboratory at UCLA
JUPITER 2 MHD Heat Transfer Exp. in UCLA FLIHY Electrolyte Loop
BOB magnet
QTOR magnet and LM flow
loop
The manifold experiment
• (Exp. A) Non-conducting test-article • (Exp. B) Conducting test-article• (Exp. C) Manifold optimization • Parameters: L=1 m, B~2 T• Measurements: Pressure, electric potential, flow rate, velocity• Status: Vacuum testing
Goal: Manifold design that provides uniform flow distribution and minimizes the MHD pressure drop
Modeling the manifold experiment
X
0
50
100
150
200
Y
-40-20
020
40
Z 01020
(Exp. A): Ha = 1000; Re = 1000; N = 1000
Modeling the manifold experiment
Y
-40
-20
0
20
40
Z
0
10
20
Flow imbalance:center channel = +11.8%side channels = -5.9%
Dependence on Ha, Reand geometry must be studied – Likely to be more imbalanced at higher Ha
CONCLUSIONS
• Basic MHD phenomena that affect blanket performance have been identified
• Preliminary MHD/Heat Transfer analysis have been performed for 3 blanket scenarios using reduced 2-D/3-D models
• More analysis is required to address 3-D issues based on full models and via experiments
• HIMAG is potentially a very effective numerical tool for LM blanket applications