Fundamental science and synergy of multi-species surface interactions in high-plasma--flux environments

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1 Fundamental science and synergy of multi-species surface interactions in high-plasma--flux environments Our thanks to John Hogan Managed by UT-Battelle for the Department of Energy ReNew PMI, UCLA, March 2009 Predrag S. Krstic 1, Fred W. Meyer 1, Jeffrey H. Harris 2 Richard H. Goulding 2, Donald L. Hillis 2 Y. K. Martin Peng 2 Larry R. Baylor 2 Oak Ridge National Laboratory: 1 Physics Division, CFADC 2 Fusion Energy Division

2 PMI has many fundamental processes & synergies elastic reflection trapping/detrapping retention diffusion, permeation implantation Plasma Drivers: Multi -T, -n, -species, plasma irradiation, neutrons sheath acceleration re-emission & sputtering & chemistry Material Damage Effects: Vacancies, bubbles, blisters, dislocations, voids, neutrons? 2 Give rise to synergistic effects

3 Goal: understand other matls as well as carbon (most studied PMI) Experiments with Ar+ and H: Sputtering = (chemical) + (physical) Surface preparation by H impact for chemical sputtering Impurity atoms in plasma are efficient precursors for erosion PM processes very dependent on inventory of H in the material Exposure of pyrolytic graphite to 5 MeV C+ simulates neutron damage: more nucleation enhanced erosion sites for H retention increased HC density? increased ejection probability? Ion flux = 3.5 * cm -2 s -1 H flux = 1.4 * cm -2 s -1 H and Ar + physical 3 H alone Hopf & von Keudell, 2003 Ar + alone B.I. Khripunov et al. 2009

4 Integration of theory & experiment basis of PMI research Beam-surface experiments Plasma-surface experiments Well -defined impact particles -prepared surface Multi-specie, -state, -energy Evolving irradation, damage history PWI interactions Terascale simulation of PWI, validated by experiments Predictability of PWI 4

5 Simulation is computationally intensive and multi-scale Creation of the surface; damaging, annealing, hydrogenation FAST ps-ns nm Short-time scale MD simulation H collisional cascade; chemical processes Short-time products sputtering, reflection, implantation Probability rates: diffusion, reactions Terascalepetascale challenge SLOW ms-s μm Long-time scale transport equation (sources and sinks) for various particles Monte Carlo simulation Development of damage, Diffusion of damage Diffusion of hydrogen, sputtering products 5 Total erosion, sputtering yield, retention Surface desorption

6 Molecular dynamics (MD): computes pico-to-nano scale Simultaneous evolution of a many-particle system over t ~ ps-ns Equations of motion solved for each particle at series of smaller time steps (energy conservation) δt ~ 0.1 fs Potential energy U: predefined function Classical MD F = ma F = U 1000 s of randomized trajectories repeated: Level of randomization simulates beam or plasma Krstic,

7 Classical MD is only as good as the interatomic potential model used Most advanced: hydro-carbon potential developed for chemistry Brenner, 1990, 2002 : REBO, short range, 0.2nm more sophisticated AIREBO (Stuart, 2000, 2004, 1.1 nm) > 400 semi-empirical parameters, bond order, chemistry E = Vrep( rij ) bij ( rij ) Vattr( rij ) i, j EX: MD calc. of reflection coeff. Significant sensitivity to changes in potential model for some processes Experimental validation essential to establish credible MD simulation. Interatomic potentials for W and Be are less mature than for carbon and require more experimental validation. 7 Reinhold & Krstic, 2008

8 Beam-surface exp t: precision control of projectiles & targets... Sputtering yield (/D) Control of impact energy & angle. Incident flux up to m -2 s -1 Clean, well-characterized surfaces, p b ~10-10 torr Temperature control of target Absolute yields of interaction products Direct line of sight for diagnostics (TOF, etc.) ORNL: F.W. Meyer,... enabled development & validation of MD approach total C hydrocarbon CD 3 +CD 4 Remarkable agreement of theory & exp t when simulation mimics exp t. No fitting parameters! Key: simulation prepares surface by bombardment! Fluence (not flux) like that in experiment Type, internal state, energy, angle as in exp t 10-3 (*) MD with D 2 MD with D MD with D (g) 2 exp with D + 2 exp with D + exp with D 3 + Krstic & Meyer, Impact energy (ev/d)

9 New developments in MD drive PMI simulation Potential models for W and Be need validation W,C,H potential developed, [Juslin (2005) + He (2006)] Be,C Be,H Be,C,H [Bjorkas (2008)] Add W? All based on Density Functional Theory (DFT) Problematic for mixed materials Research on improvement of potentials has top priority. Can we improve calculation of short-scale PMI processes? Continuing acceleration in computing ab-initio calculations Improved parameterization of MD potentials is possible! Quantum-Classical Molecular Dynamics! Potential energy calculated at each time step by solving Schrödinger equation for electrons with adiabatic instantaneous Hamiltonian Current DOE capabilities: Cray XT5 (~petaflops): Jaguar (120,000 processors) Kraken (67,000 processors) 9

10 H impact Typical Monte Carlo trajectory approach (ex: carbon) Macroscopic master equation for the long-time evolution Boltzmann like collisional relaxation operator (rates form MD) desorption Conversion to CH 4 sp 2 diffusion H sp x H H sp 3 H H diffusion carbon 10 CH 3 is created

11 Synergies in tungsten: dramatic effects in plasma experiments Fuzz nanostructures on W irradiated by He high W temperatures (>1000K) He suppresses H retention He penetrates deeper than H Strong dependence on energy He bubbles: barrier to H diffusion? He: 0.1% He: 0% Baldwin, 2008 Blistering related to D, H retention Sparser for T > 600 K H implantation(2-20 nm) T=653 K H grain boundaries > 1 µm Dome-like blisters grain ejection 11 Ueda et al,2008

12 QuickTime and a decompressor are needed to see this picture. Coordinate experiments, theory & modeling of plasma-surface systems of increasing complexity, to develop predictive capability for DEMO Beam-surface experiments: Prepared beam & target PMI synergy experiments: Well-diagnosed plasma & target MD and MC simulation MD and MC with plasma synergy Potential models Quantum-classical MD Increases in computational power Toroidal confinement experiments PMI Design & Qualification DEMO 12

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