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Introduction of SOLPS and Modeling of Hydrogen Isotope Inventory in mixed materials
Chaofeng Sang, Dezhen Wang, Xavier Bonnin and PSI&AD group
Dalian University of Technology
School of Physics and Optoelectronic Technology
2011.11.26, HefeiPSI&AD: http:// sites.google.com/site/dlutplasma
Introduction of SOLPS code package ;
Hydrogen isotope inventory ;
Recent work
Outline
Introduction of SOLPS code package ;
Hydrogen isotope inventory ;
Recent work
Outline
Introduction of SOLPS
B2, Braams' multi-species, 2D, fluid plasma code
Eirene , Reiter's Monte-Carlo neutrals code
Carre , grid generator
DG, Kukushkin's pre-processor
b2plot, Coster's post-processor
SOLPS (Scrape-off Layer Plasma simulator) is code package which can simulate the 2D SOL plasma. The package mainly includes :
The main version of SOLPS code is SOLPS4.X , SOLPS5.X; the difference of these two series of version is that, 4.x use b2 and 5.x use b2.5; X is depended by the different version of EIRENE (EIRENE96, EIRENE99, and latest version). SOLPS6.0 is in developing.
System and compiler requirements for SOLPS:
IBM AIX well supported
SUN : SUN’s compiler suite, Fujitsu;
SGI: has been used in the past;
Linux: (main)
Fujitsu’s PC compiler main one in use at Garching;
Linux.pfg90 compiler; (ITM-gateway, JET)
Intel compiler; gfortran compiler, g77
Introduction of SOLPS
Simulation domain of SOLPS :
Region for single-null geometries
Introduction of SOLPS
Region for double-null geometries
Introduction of SOLPS
The function of each code of SOLPS :
B2, a multi-fluid plasma code (2D), which can simulate different particles
(H/D/T/He/C/W/Be) in the SOL. The type of the particle can be defined.
Eirene , a 3D Monte Carlo kinetic code, which can trace the movement of
neutral particles.
DG, a graphical tool used for developing and modifying plasma devices
and plasma grids (define magnetic field, wall materials etc.), as well as
producing input date for some of the other codes (Carre, Triangle, Unip) ;
Carre , to creat grid, ( use the output of DG as input data );
b2plot, used for plotting results from simulation runs.
Introduction of SOLPS
Introduction of SOLPS
Long-term SOLPS programming projects Revamp of the meshing workflow (not started, nobody identified) Re-evaluation of sparse-matrix solvers, see Sparse Solvers (in progress, Klingshirn) Examination and possible implementation of also solving density equations summed over homonuclear sequences (not started, nobody identified) Implementation of H/D/T inventories in walls/targets (CS, in progress) Stabilization of 2d wall heat transport model by source linearization and self-consistent treatments of heat fluxes to the walls (including SEE, backscattering, etc...) (not started, XPB) More accurate calculation of electron cooling rates from atomic data (in progress, see issues 1-3 below, DPC + XPB + LDH) Improve the treatment of drifts (in progress, St. Petersburg) Development of SOLPS6 (in progress, Klingshirn) Improvement of b2fstati to create a 'reasonable' non-flat start state (not started, nobody identified)
An example of EAST case :
SOLPS 5.0 SOLPS 5.1
Introduction of SOLPS
Introduction of SOLPS code package ;
Hydrogen isotope inventory ;
Recent work
Background
Hydrogen Isotopes(HIs) inventory is a key issue for the next fusion device because of safety reasons (T limited to 700 g, ITER).
In the future fusion device, simultaneous use of Be, W and C as the wall material for different parts of plasma facing components (PFCs) will bring in material mixing issues, which compound that of hydrogen isotopes retention.
For large simulation code such SOLPS, a standalone module which can simulate fuel retention is required.
Simulation flow chart
PIC-MCC SOLPSWall model
Plasma backgroundPlasma flux to the real wall
Impurity recycling
HIIPs
Components of the wall
Temperature distribution
WALLDYN
Heating
including
WALLDYN: Wall dynamic code, which is being developed by surface science group, IPP ;Compounds : W, Be, C, WC, W2C, Be2C Be2W , Be12W, Be22W
HIIPC: Hydrogen Isotope Inventory Processes Code
PIC-MCC SOLPSWall model
Plasma backgroundPlasma flux to the real wall
WALLDYN
Heating
Impurity recycling
HIIPs
Components of the wall
Temperature distribution
Heating
Hydrogen Isotope Inventory Processes code.
AMNS databaseInput data, database
Simulation flow chart
Outline
HIIPsHeating
Temperature distribution Metal model Porosity model
HIs retention in the metal
wall.HIs retention in
the porous media( include carbon materials and co-deposition laye
r )Based on rate equation
1. Heating model
Equations:
Tk
q
z
tzT
z
tzTTk
zt
tzTC
front
p
|,
,,
baT
Tk
1
The heating conductivity
ba, Constants determined by experiments
z The direction normal to the target surface
The density of the materials
q Incident energy load
pC The specific heat of the materials
The heating model is applied to calculate the temporal evolution of temperature distribution in the bulk target, which can be applied to the HIIPs
Metal/Porous media
Cooling side
Flux (energy, particle)
z direction
Schematic of the simulation domain
Simulation results
For different materials (a)The steady-state temperature distribution inside the wall (z=0 is the heating surface of the wall) at long time,(b) the time-dependent surface temperature.
Due to the difference of the thermal conductivity, different material has different temperature distribution.
For wall thickness (distance of surface to the cool side) L = 1 cm, variation of the steady-state surface temperature with the heating flux.
Larger energy load leads to higher surface temperature
Fixed heating flux 3.0 MW/m2, (a) variation of the steady-state surface temperature with L, (b) minimum time to achieve steady-state temperature for different L values
The thicker the wall is, the higher the surface temperature can achieve.
Higher temperature need more time to get steady state.
Simulation results
2. Metal model : HIIPs in metal materials
kT
E
aCCC
Da
t
tzC
t
tzCzR
z
tzCD
zt
tzC
Hdtraptts
t
tss
exp12
3
2,
,1
,,
30
0
The solute HIs concentration
The trapped HIs concentration
The HIs implantation profile
The incident particle flux
tzCs ,
tzCt ,
z
0
Recombination process only occurs on the surface of the metal wall:
LsrH CKJ ,02 |2
2
The diffusivity
The backscattering coefficient
The lattice constant
The detrapping energy
RD
a
HdtrapE
Simulation results
HIIs as functions of wall temperature after exposition to a HIs flux for 50 s, (a) the total, solute, and trapped HIs retention; (b) the percentage of solute and trapped HIs.
The total and solute retention HIs decrease as the temperature is increasing ;trapped; the trapped HIs areal density first increases with temperature, and then starts to decrease
Temperature range 450-900K, most of the HIs retention inside the wall in the form of trapped.
Simulation results
After exposition to the HIs flux for 50 s, the depth profiles of HIs retention for different wall temperatures.
HIs can diffuse deeper inside the wall when the wall temperature is higher
Comparing the total His retained, varying with time, using either a fixed wall temperature or the temperature from our heating model. The insert graph is the temperature evolution of the two cases.
When the temperature is calculated by the heating model, the retention amount is very different in the first 10 s (discharge time).
Depth profiles of HIs retained in the wall after 50 s as a function of impinging HIs flux
The larger flux can make HIs diffuse deeper inside the wall and increase the total retention amount. (Because of saturated region near the surface of the wall).
After pre-exposure to HIs for 50 s, and turning off the particle flux, (a) HIs retained as a function of time; (b) HIs retention depth profiles at different times.
Total retention amount decrease with time; the fuel diffuse deeper with time.
Simulation results
3. Porosity model : HII in porous media
Carbon-based materials and co-deposited materials are porous media , therefore, we can use this four-region model to simulate HIIP in these materials. The definition of of each region for the porosity model is shown .
Porous media is made up of granules and voids, the granules are consist of surface and bulk.
Some experimental data about mixed materials is demanded ( IPP-Garching)
Basic equations
surfbulkbulksurfLHHAvoid
bulksurfsurfbulkHdtrapHTrap
LHHERHHDAvoid
rrV
Srrf
V
S
z
ND
t
N
rrrr
rrrrfz
Dt
2
22
2
21
2
2
2
2
Where
The surface concentration of solute HIs in regions I and III tz,The volume concentration of solute HIs in regions II and IV tzN ,
Inter-regional transport from bulk to surfacesurfbulkr
The surface void fractionvoidf zrrA 01 The area of the surfaceS
The volume of the bulkV
thermal desorption rateHDr
Eley-Rideal processesERHr 2
Langmuir-Hinshelwood processesLHHr 2
Real flux inside the wall
Detail equations
Simulation results
Hydrogen isotope inventory (a) two-region for carbon-based target, (b) four region for carbon and co-deposited layer with the interface at z=0.
HIs diffuse more deeper inside the co-deposited layer than inside the carbon-based wall. There is a suddenly drop of the HIs density at the interface. (The diffusivity is very different)
Simulation results
Higher temperature can increase the diffusivity and make the fuel diffuse deeper.
Four-region case, at different temperatures (700-1200 K), t = 1 s, the depth profiles of HIs retained in (a) surface (region I, III), (b) bulk (region II, IV) .
Hydrogen isotope inventory (a) two-region for carbon-based target, (b) four region for carbon and co-deposited layer with the interface at z=0.
Given a fixed co-deposited growth rate (0.1 μm.s-1), the HIs retained density distribution evolution (a) σI + σI
Htrap and σIII + σIIIHtrap, (b) nII and nIV
After the implanting flux (Γ0) is turned off, the HIs release rate evolution for different wall temperatures (800 ~1300 K).
HIs release rate drops very quickly just after the flux turning off. Higher temperature have a higher release rate.
Simulation results
Conclusions
Heating model : Calculate the wall temperature which is the input of metal and porosity
modules. We find that the material properties has big influence on the temperature; thicker
wall would increase temperature of the wall surface; and larger energy load can also increase
wall temperature. ;
Metal model: The model is based on the rate equations which can simulate HIs retention
inside the metal materials (W/Be). We investigate the wall temperature effect to the HIIPs, and
the HIIPs during and after the injected flux.
Pososity model : This model can handle fuel retention inside the porous media (carbon,
co-deposited layer). The wall temperature effect, inject flux, release rate, and retention during
co-deposition are studied.
The HIIPC code is applied to simulate Hydrogen isotope inventory in the mixed materials. The code include three modules: heating , metal , porosity module
4. Bubble growth during HIIPs in Tungsten
For metal materials (tungsten) wall, bubble growth is a key issue. It can change material properties, increase HIs retention, and even make blistering occur, which can create metal impurity, and thus reduce the lifetime of the wall. Therefore, it is important to study bubble growth during fuel retention. We improve the metal model to have the capability to handle bubble growth.
Assumptions of the model :To make the model simple and flexible, we make the following assumptions.
Bubble nucleation has already took place ( small bubbles already exist ); The pressure in the bubble satisfies the Greenwood mechanical equilibrium condition ;
The bubble shape is spherical with a radius rb
Hydride formation is neglected ;
The effects of helium is not considered ;
There are only hydrogen molecules (no hydrogen atoms) in the bubbles.
Equations
Density of Bubbles
Number of HIs molecules inside Bubble
Absorption rate
Recombination rate
Fugacity in the bubble
Pressure inside bubble : Greenwood’s equilibrium condition
Shear modulus of tungsten
The temperature should be much lower than the melting temperature of tungsten.
Equations
To get the relationship between pressure and HIs number inside the bubble, the state equation for HIs is required:
In the very high pressure case, we use the fugacity to replace the pressure:
Physical validation :To make the model physically valid, we should make sure that the bubbles should not be too big and avoid the case when they are touching each other :
Simulation results
The bubble radius and internal pressure as function of particle number inside bubble.
Maximum Equilibrium Solution (MES) and Maximum Solute density (MSD) as function of temperature
It is easier for bubbles to grow at lower temperature
MESKey temperature (520 K)
Simulation results
T = 500 K, bubble can grow,Cs, rb, Nb distribution at different time.
T = 600 K, bubble can not grow,Cs, rb, Nb distribution at different time.
Simulation result and conclusions
T = 500 K, the total HIs retained evolution for different bubble densities.
Bubble can grow under 500 k. When the bubble density (Cb) is large enough, the total HIs inventory amount can be increased during bubble growing.
Conclusions
This section of work includes:
Develop a new model which can
handle bubble growth;
We find that the wall temperature
is important during bubble growth. It
is easier for bubbles to grow at
lower temperature
Bubble growth could increase the
total HIs retention amount.
0 10 20 30 40 50 60 70
4.0x1021
6.0x1021
8.0x1021
0 = 1024 atoms.m-2.s-1
Temperature = 500 K
Time (s)
HIs
ret
ain
ed (
ato
ms.
m-2)
without bubble
Cb = 1.0*1012 m-3
Cb = 1.0*1013 m-3
Cb = 5.0*1013 m-3
References
1. R. Schneider, X. Bonnin et.al., Plasma Edge Physics with B2-Eirene, Contrib. Plasma Phys. 46, 3 (2006);
2. SOLPS5.1 Manual;
3. M. Warrier, Macroscopic particle balance model of hydrogen reactive-diffusive transport and inventory in porous media (private communication);
4. C. Sang, X. Bonnin, M. Warrier, A. Rai, R. Schneider, J. Sun, D. Wang , Modeling of Hydrogen reactive-diffusive transport and inventory in porous media with mixed materials deposited layers, EPS2011 38th Conference on Plasma Physics. (2011) ;
5. C. Sang, X. Bonnin, M. Warrier, A. Rai, R. Schneider, J. Sun, D. Wang , Modeling of hydrogen isotope inventory in mixed materials including porous deposited layers in fusion devices (submitted to Nucl. Fusion);
6. C. Sang, X. Bonnin, J. Sun, D. Wang, An improved model to simulate the effect of bubble growth on the hydrogen isotope inventory in tungsten (submitted to J. Nucl. Mater.)
7. C. Sang, D. Wang, X. Bonnin, M. Warrier, A. Rai, R. Schneider and J. Sun, Modeling of hydrogen isotope inventory in mixed materials in fusion devices, 53rd Annual Meeting of the APS Division of Plasma Physics (APS-DPP), November 14-18, 2011 • Salt Lake City, Utah – Poster YP9
Introduction of SOLPS code package ;
Hydrogen isotope inventory ;
Recent work
Recent work
Collaborate with Tore Supra about fuel retention;
Fuel retention in the gaps of the divertor tiles (tungsten);
Dust Transport Simulation In EAST Device;
Kinetic simulation of divertor plasma detachment
Deuterium retention and release from pores in tungsten
Others
Prepare for 2012 PSI conference
End
Collaborate with Tore Supra
back
Prepare the abstracts for PSI 2012
Long term outgassing of carbon deposits in Tore Supra
S. Panayotis(1), C. Sang(2,3), B. Pégourié(1), X. Bonnin(2),E. Caprin(1), D. Douai(1), J.-C. Hatchressian(1), V. Negrier(1),J.-Y. Pascal(1), S. Vartanian(1), J. Bucalossi(1), P. Monier-Garbet(1)
(1) IRFM/DSM/CEA, CE Cadarache, F-13108 Saint-Paul-lez-Durance(2) LSPM-CNRS, Université Paris 13, Villetaneuse, France (3) School of Physics and Optoelectronic Technology, Dalian University of Technology,
Dalian, China
Simulation of Fuel retention processes in the carbon-lined wall of Tore Supra
Chaofeng Sang1,2, Xavier Bonnin2, B. Pégourié3, Jizhong Sun1 and Dezhen. Wang1
1 School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian, 116024, China. 2 LSPM-CNRS, Université Paris 13, Villetaneuse, France . 3 IRFM/DSM/CEA, CE Cadarache, F-13108 Saint-Paul-lez-Durance, France.
Fuel retention in the gaps of the divertor tiles
PIC-GAP 2D HIIPC2DHIs flux and energy
Fuel retention amount in the gap can be modeled
Prepare the abstract for PSI 2012
Simulation of Fuel retention in the gap of the deivertor tiles
Chaofeng Sang1,2, Jizhong Sun1, Xavier Bonnin2 , Dezhen. Wang1 , Houyang Guo 3
1 School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian, 116024, China.
2 LSPM CNRS, Université Paris 13, 99 avenue J.-B. Clément, Villetaneuse, 93430, France.
3 Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
We set tungsten as the tile material, and improve the HIIPC to 2D, the plasma flux and energy is handled by PIC-GAP 2D
back
Prepare the abstract for PSI 2012
Dust in the fusion device
Dust Transport Simulation In EAST Device
Zhuang Liu1, Chaofeng Sang1, Jizhong Sun1, Dezhen Wang1
Houyang Guo2, and Sizheng Zhu2 1 School of Physics and Optoelectronic Technology, Dalian University of Technology2 Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
The transport of dust particles in East device is studied using computer simulations with the dust transport code, DUST code. Recent developments in modeling with the DUST code are reported.
DUST code includes five sections. They are charging, force, ablation, transport and dust & wall interaction section, respectively. Taking into account EAST configuration and different parameters, DUST can simulate the transport process of dusts in EAST device.
back
Dust transport simulation in EAST contains five processes:
1. Charging
2. Forces
di e z th see
Z
dQI I I I I
dt Ions, electrons, impurity, thermionic, SEE Ions, electrons, impurity, thermionic, SEE
Dust Transport Simulation In EAST DeviceDust Transport Simulation In EAST Device
22 2
, 2
0
2{ [1 2( )] ( )}
2 4d i i th i u d i
id i
R Z en v Z Z euI e u erf u
u R T
22
0
8exp( )
4e d
e d ee d e
T Z eI e R n
m R T
i E BF F F F G ����������������������������������������������������������������������
Ion drag , E , B, gravityIon drag , E , B, gravity
i iab iscF F F ������������������������������������������
ion absorption, ion Coulomb scatteringion absorption, ion Coulomb scattering
2 ( , / ),i diab d i i iab i e
i d
V vF R n T u T T
V v
������������������������������������������
����������������������������
2 ( , / )i disc d i i isc i e
i d
V vF R n T u T T
V v
������������������������������������������
����������������������������
Force due to absorption of ionsForce due to absorption of ions
Force due to Coulomb scattering of ionsForce due to Coulomb scattering of ions
IonsIons
electronselectrons
3. Energy Balance and Ablation
Dust Transport Simulation In EAST DeviceDust Transport Simulation In EAST Device
22 4 2 2
2
1( , / ) { (2 1 2 ) [4 4 1 2(1 2 ) ] ( )}
2u
iab i e i iu T T u u e u u u erf uu
2
2
( ) 2 exp( ) /( , / ) 4 ln
2isc i e i
erf u u uu T T
u
2
,0 0
( , / )4 4
i d i ei i e d eq
d i i
Z Z e Z e Tu T T
R T T
, , /d eq d eq d eZ R T
/d h cdH dt P P
0
dT
d d pdH M c dT
Total heating / cooling powerTotal heating / cooling power
dust enthalpy, dust mass, dust specific heat dust enthalpy, dust mass, dust specific heat
2 2 2, , , ,
, ,
1[3 12 4 2 (1 2 )] ( )}i z i z i z i z
i z i z
Zu u u erf u
u
2 2 2, , , ,
,
1 2{ [5 2( )]exp( )
4h d i z Ti z i z i zi z
ZP R n v u u
/ 0d ee T
2 2 2, , ,
, ,
1[3 12 4 2 (1 2 )]i z i z i z
i z i z
Zu u u
u
2,2 2 2
, , , ,, ,
3 21 2{ [(5 2 )exp( )
8i z
h d i z Ti z i z i zpi z i z
u ZP R n v u u
u
/ 0d ee T
Dust Transport Simulation In EAST DeviceDust Transport Simulation In EAST Device
2,2 2
, ,, ,
3 2(5 2 )exp( )i z
i z i zmi z i z
u Zu u
u
2 4 44 ( )c radiation d d d wP P R T T emissivity, Stefan–Boltzmann constant, wall temperatureemissivity, Stefan–Boltzmann constant, wall temperature
Integrating the Planck function multiplied by the emissivity over wavelengthIntegrating the Planck function multiplied by the emissivity over wavelength
4 42
0 0
216 Im( ) ( )
1.3 ( ) ( )d d w
c dd w
R T TP R G
T T
4. Transport
5. Interaction with wall
d
d d
dVM F
dt
����������������������������
back
Detachment
Simulation of the divertor plasma detachment using kinetic method
Tengfei Tang, Chaofeng Sang, Dezhen Wang and Jizhong Sun
School of Physics and Optoelectronic Technology, Dalian University of Technology
Prepare the abstract for PSI 2012
Detached divertor is an attractive mode of operation for the tokamak reactor conditions with substantial reduction in the peak heat fluxes on the divertor targets which is created by gas injection near the targets. In this work we develop Particle In Cell Monte Carlo (PIC-MCC) code to simulate divertor plasma detachment. The charge-exchange, ionization, elastic, Coulomb and recombination collisions are included in our model.
Previous results: without recombination.Ar gas, by C. SangHydrogen gas, by T. Tang
Code including recombination is under development (by T. Tang)
back
Retention in Tungsten
Deuterium retention and release from pores in tungsten
Shengguang Liu, Jizhong Sun and Dezhen Wang
School of Physics and Optoelectronic Technology, Dalian University of Technology
Prepare the abstract for PSI 2012
Pores in the tungsten sample after irradiation were observed directly by SEM. However, the physical mechanism of H isotope trapping and migration in W is not completely understood yet. These pores gives rise to great complexity to understand the H transport behaviour in W. Therefore, a model to simulate deuterium retention and release from pores in tungsten is urgent required.
Pores
Cross-section of irradiated area of the sample, irradiated up to maximal fluence of 5×1018 D+/cm2
Model and resultsModel
H 原子
2
2(1 ) ( )
c cD r F x
t x
3
2 12[ exp( )]
3bEY Da Y
uW uYt a kT
2 2
1 1( (0, ) )dn
V A K c t K S pdt
C: solute H Concentration
Y: Trapping H concentration
n: H concentration inside pores
P: pressure
H density in pore H density near pores H density on tungsten surface
back
SOLPS + HIIPC to simulate total fuel retention amount in fusion device ;
Continue developing HIIPC ;
ERO simulation work (for roughness wall),
The effect of plasma disruption to the plasma facing wall,
Runaway electrons
The interaction between plasma and wall in a strong oblique magnetic field
Others
backEnd
SOLPS+HIIPC to simulate HIIPs in fusion device
backJET
ITER
Recent work
αloc
αnom αloc
projectile sputtered & reflected
re-dep
ositio
n
Local angle of incidence αloc differs from nominal angle of incidence αnom
Reflected and sputtered particles can be re-deposited locally in holes.
Roughness description:Y = sinX
Sputtering: Dependent on energy and local angle
Reflection: TRIM database (dependent on angle and local angle)MD database (dependent on energy only)
Surface modification:Erosion, deposition, re-deposition
Modeling of surface roughness effects on erosion and re-deposition
Shuyu Dai and Zhanfu Yao
ERO code
The end
Thank you!