Introduction of SOLPS and Modeling of Hydrogen Isotope Inventory in mixed materials

1. 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 PSI&AD: http:// sites.google.com/site/dlutplasma 2011.11.26, Hefei

2. Outline • Introduction of SOLPS code package； • Hydrogen isotope inventory； • Recent work

3. Outline • Introduction of SOLPS code package ； • Hydrogen isotope inventory； • Recent work

4. Introduction of SOLPS SOLPS (Scrape-off Layer Plasma simulator) is code package which can simulate the 2D SOL plasma. The package mainly includes： • 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 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.

5. Introduction of SOLPS 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

6. Introduction of SOLPS Simulation domain of SOLPS： Region for single-null geometries

7. Introduction of SOLPS Region for double-null geometries

8. 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.

9. 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)

10. Introduction of SOLPS An example of EAST case： SOLPS 5.0 SOLPS 5.1

11. Introduction of SOLPS code package ； • Hydrogen isotope inventory； • Recent work

12. 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 codesuch SOLPS, a standalone module which can simulate fuel retention is required.

13. including Heating Temperature distribution WALLDYN HIIPs Components of the wall Simulation flow chart Plasma flux to the real wall Plasma background Wall model PIC-MCC SOLPS Impurity recycling 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

14. Simulation flow chart Plasma flux to the real wall Plasma background Wall model PIC-MCC SOLPS Heating Heating Impurity recycling Temperature distribution Hydrogen Isotope Inventory Processes code. WALLDYN HIIPs Components of the wall Input data, database AMNS database

15. Heating HIIPs Based on rate equation Outline 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）

16. 1. Heating model The heating model is applied to calculate the temporal evolution of temperature distribution in the bulk target, which can be applied to the HIIPs Equations: The heating conductivity Constants determined by experiments Incident energy load The density of the materials The specific heat of the materials z The direction normal to the target surface Schematic of the simulation domain

17. 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.

18. Simulation results For wall thickness (distance of surface to the cool side) L = 1 cm, variation of the steady-state surface temperature with the heating flux. 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 Larger energy load leads to higher surface temperature The thicker the wall is, the higher the surface temperature can achieve. Higher temperature need more time to get steady state.

19. 2. Metal model：HIIPs in metal materials Recombinationprocess only occurs on the surface of the metal wall: The solute HIs concentration The backscattering coefficient The trapped HIs concentration The diffusivity The HIs implantation profile The lattice constant The detrapping energy The incident particle flux

20. 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.

21. Simulation results 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. 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 When the temperature is calculated by the heating model, the retention amount is very different in the first 10 s (discharge time).

22. Simulation results Depth profiles of HIs retained in the wall after 50 s as a function of impinging HIs flux 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. 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). Total retention amount decrease with time; the fuel diffuse deeper with time.

23. 3. Porosity model：HII in porous media Porous media is made up of granules and voids, the granules are consist of surface and bulk. 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 . Some experimental data about mixed materials is demanded（IPP-Garching）

24. Basic equations Detail 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 Real flux inside the wall The surface void fraction Eley-Rideal processes The area of the surface The volume of the bulk Langmuir-Hinshelwood processes thermal desorption rate

25. 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)

26. 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. 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) .

27. Simulation results 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. Given a fixed co-deposited growth rate (0.1 μm.s-1), the HIs retained density distribution evolution (a) σI + σIHtrap and σIII + σIIIHtrap, (b) nII and nIV

28. Conclusions The HIIPC code is applied to simulate Hydrogen isotope inventory in the mixed materials. The code include three modules: heating，metal，porosity module • 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.

29. 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.

30. Equations Density of Bubbles Pressure inside bubble：Greenwood’s equilibrium condition Number of HIs molecules inside Bubble Absorption rate Shear modulus of tungsten Recombination rate Fugacity in the bubble The temperature should be much lower than the melting temperature of tungsten.

31. 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：

32. 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 Key temperature (520 K) MES

33. Simulation results T = 500 K, bubble can grow， Cs, rb, Nbdistribution at different time. T = 600 K, bubble can not grow， Cs, rb, Nbdistribution at different time.

34. Simulation result and conclusions 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. 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.

35. 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

36. Introduction of SOLPS code package ； • Hydrogen isotope inventory； • Recent work

37. Recent work Prepare for 2012 PSI conference • 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 End

38. Collaborate with Tore Supra Prepare the abstracts for PSI 2012 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 1School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian, 116024, China. 2LSPM-CNRS, Université Paris 13, Villetaneuse, France . 3IRFM/DSM/CEA, CE Cadarache, F-13108 Saint-Paul-lez-Durance, France. 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 back

39. Fuel retention in the gaps of the divertor tiles We set tungsten as the tile material, and improve the HIIPC to 2D, the plasma flux and energy is handled by PIC-GAP 2D HIsflux and energy PIC-GAP 2D HIIPC2D 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 1School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian, 116024, China. 2LSPM CNRS, Université Paris 13, 99 avenue J.-B. Clément, Villetaneuse, 93430, France. 3 Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China Fuel retention amount in the gap can be modeled back

40. Dust in the fusion device Prepare the abstract for PSI 2012 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 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

41. Dust Transport Simulation In EAST Device • Dust transport simulation in EAST contains five processes： • Charging • Forces Ions, electrons, impurity, thermionic, SEE Ions electrons Ion drag , E , B, gravity ion absorption, ion Coulomb scattering Force due to absorption of ions Force due to Coulomb scattering of ions

42. Dust Transport Simulation In EAST Device 3. Energy Balance and Ablation Total heating / cooling power dust enthalpy, dust mass, dust specific heat

43. Dust Transport Simulation In EAST Device 4. Transport 5. Interaction with wall emissivity, Stefan–Boltzmann constant, wall temperature Integrating the Planck function multiplied by the emissivity over wavelength back

44. Detachment Prepare the abstract for PSI 2012 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 Detached divertor isan 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

45. Pores Retention in Tungsten Prepare the abstract for PSI 2012 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 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. Cross-section of irradiated area of the sample, irradiated up to maximal fluence of 5×1018 D+/cm2

46. H原子 Model and results Model 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

47. Others • 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 End back

48. SOLPS+HIIPC to simulate HIIPs in fusion device Recent work JET ITER back

49. αnom αloc sputtered & reflected projectile αloc re-deposition ERO code Modeling of surface roughness effects on erosion and re-deposition Shuyu Dai and Zhanfu Yao • 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

50. The end Thank you! PSI&AD group of DLUT