Presentation on theme: "Introduction of SOLPS and Modeling of Hydrogen Isotope Inventory in mixed materials Chaofeng Sang, Dezhen Wang, Xavier Bonnin and PSI&AD group Dalian University."— Presentation transcript:
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, Hefei PSI&AD: http:// sites.google.com/site/dlutplasmahttp:// 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 Eirene Carre, grid generator Carre DG, Kukushkin's pre-processor DG b2plot, Coster's post-processor b2plot 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. Eirene 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) ； DG Carre, to creat grid, （ use the output of DG as input data ）； Carre b2plot, used for plotting results from simulation runs. b2plot 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)Sparse Solvers 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 5.0SOLPS 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-MCCSOLPS Wall model Plasma background Plasma 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, W 2 C, Be 2 C Be 2 W, Be 12 W, Be 22 W HIIPC: Hydrogen Isotope Inventory Processes Code
PIC-MCCSOLPS Wall model Plasma background Plasma flux to the real wall WALLDYN Heating Impurity recycling HIIPs Components of the wall Temperature distribution Heating Hydrogen Isotope Inventory Processes code. AMNS database Input 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 layer ） Based on rate equation
1. Heating model Equations: The heating conductivity Constants determined by experiments z The direction normal to the target surface The density of the materials Incident energy load 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 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/m 2, (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 The solute HIs concentration The trapped HIs concentration The HIs implantation profile The incident particle flux Recombination process only occurs on the surface of the metal wall: The diffusivity The backscattering coefficient The lattice constant The detrapping energy
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 Where The surface concentration of solute HIs in regions I and III The volume concentration of solute HIs in regions II and IV Inter-regional transport from bulk to surface The surface void fraction The area of the surface The volume of the bulk thermal desorption rate Eley-Rideal processes Langmuir-Hinshelwood processes 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) σ I + σ I Htrap and σ III + σ III Htrap, (b) n II and n IV After the implanting flux (Γ 0 ) 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 r b 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 MES Key temperature (520 K)
Simulation results T = 500 K, bubble can grow ， C s, r b, N b distribution at different time. T = 600 K, bubble can not grow ， C s, r b, N b 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 (C b ) 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.
References 1. R. Schneider, X. Bonnin et.al., Plasma Edge Physics with B2-Eirene, Contrib. Plasma Phys. 46, 3 (2006);Plasma Edge Physics with B2-Eirene 2. SOLPS5.1 Manual;SOLPS5.1 Manual 3. M. Warrier, Macroscopic particle balance model of hydrogen reactive-diffusive transport and inventory in porous media (private communication);Macroscopic particle balance model of hydrogen reactive-diffusive transport and inventory in porous media 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) ;Modeling of Hydrogen reactive-diffusive transport and inventory in porous media with mixed materials deposited layers, 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);Modeling of hydrogen isotope inventory in mixed materials including porous deposited layers in fusion devices 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.)An improved model to simulate the effect of bubble growth on the hydrogen isotope inventory in tungsten 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 YP9Modeling of hydrogen isotope inventory in mixed materials in fusion devices
Introduction of SOLPS code package ； Hydrogen isotope inventory ； Recent work
Recent work Collaborate with Tore Supra about fuel retention ； Collaborate with Tore Supra about fuel retention Fuel retention in the gaps of the divertor tiles (tungsten ）； Fuel retention in the gaps of the divertor tiles (tungsten ） Dust Transport Simulation In EAST Device; Dust Transport Simulation In EAST Device Kinetic simulation of divertor plasma detachment Kinetic simulation of divertor plasma detachment Deuterium retention and release from pores in tungsten Deuterium retention and release from pores in tungsten Others 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. P é gouri é (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, Universit é 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 Sang 1,2, Xavier Bonnin 2, B. P é gouri é 3, Jizhong Sun 1 and Dezhen. Wang 1 1 School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian, 116024, China. 2 LSPM-CNRS, Universit é 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 2DHIIPC2D HIs 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 Sang 1,2, Jizhong Sun 1, Xavier Bonnin 2, Dezhen. Wang 1, Houyang Guo 3 1 School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian, 116024, China. 2 LSPM CNRS, Universit é Paris 13, 99 avenue J.-B. Cl é ment, 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 Liu 1, Chaofeng Sang 1, Jizhong Sun 1, Dezhen Wang 1 Houyang Guo 2, and Sizheng Zhu 2 1 School of Physics and Optoelectronic Technology, Dalian University of Technology 2 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 Ions, electrons, impurity, thermionic, SEE Dust Transport Simulation In EAST Device Ion drag, E, B, gravity ion absorption, ion Coulomb scattering Force due to absorption of ions Force due to Coulomb scattering of ions Ions electrons
3. Energy Balance and Ablation Dust Transport Simulation In EAST Device Total heating / cooling power dust enthalpy, dust mass, dust specific heat
Dust Transport Simulation In EAST Device emissivity, Stefan–Boltzmann constant, wall temperature Integrating the Planck function multiplied by the emissivity over wavelength 4. Transport 5. Interaction with wall 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. Sang Hydrogen 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 5×10 18 D + /cm 2
Model and results Model H 原子 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 ； SOLPS + HIIPC to simulate total fuel retention amount in fusion device Continue developing HIIPC ； Continue developing HIIPC ERO simulation work (for roughness wall), ERO simulation work (for roughness wall), The effect of plasma disruption to the plasma facing wall, The effect of plasma disruption to the plasma facing wall Runaway electrons Runaway electrons The interaction between plasma and wall in a strong oblique magnetic field The interaction between plasma and wall in a strong oblique magnetic field Others backEnd
SOLPS+HIIPC to simulate HIIPs in fusion device back JET ITER Recent work
α loc α nom α loc projectile sputtered & reflected re-deposition Local angle of incidence α loc differs from nominal angle of incidence α nom 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
Your consent to our cookies if you continue to use this website.