Challenges in edge modeling IPP-Teilinstitut Greifswald, EURATOM Association, Wendelsteinstraße 1,...

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Challenges in edge modeling

IPP-Teilinstitut Greifswald, EURATOM Association, Wendelsteinstraße 1, D-17491 Greifswald, Germany

Outline:1. Motivation2. Plasma model3. Neutral model4. Turbulence5. Multi-scale strategies6. Plasma-wall interaction7. Remarks on Integrated Modeling

Max-Planck-Institut für Plasmaphysik, EURATOM Association

Ralf Schneider

Motivation

• ultimate goal: predictive quality for tokamaks and stellarators (‘integrated modeling’)

What are the problems to be solved in edge modeling?

Which strategies should be followed?

What are the consequences of that?

Motivation

• edge modeling made considerable progress: yes!but: long list of topics

Summary report of the American Divertor and Edge Plasma Theory Working Group, 22.12.1992

J. Neuhauser: ITER Workshop 17.-21.7.1989

Motivation

• edge modeling made considerable progress: yes!but: long list of topics

A. Kukushkin: ITER meeting 1993

Plasma edge physics

drifts and currents: open questions

• Bruce Scott: Physics of Plasmas 10 (2003) 963only anomalous viscosity – either Reynolds stress or correction from gyroviscosity – can create anomalous transportanomalous resistivity cannot create anomalous transport or radial electric field, because resistivity is a momentum conserving friction between electrons and ions-> classical equation for the potential (div j = 0)

• numerical performance: time-step limits for complete Newton solvers (UEDGE) and equation sub-cycling (B2);semi-implicit solvers? (Zagorski et al.; see also talk by A. Kalyentev)

Plasma models

kinetic corrections: fluid corrections or coupling with kinetics

Plasma models

Coupling with kinetic code (BGK): 2 kinetic equations for thermal conductivity and viscosity(Kukushkin, Runov, Igitkhanov 1994)

3D and ergodic effects (see also talk by A. Kalyentev)

Plasma models

• Monte Carlo vs. fluid: different level of accuracy and complexity; flux limits necessary for making the fluid model realistic

Neutral models

D. Coster et al., EPS2005

• atomic and molecular data: below 5 eV??

Molecular physics

Franck-Condon atoms: low plasma temperature -> mostly molecules reflected from saturated walls

MAD and MAI

THE central problem

Turbulence

• fitting of transport coefficients

Turbulence

• fitting of transport coefficients

Turbulence

• physics-based scaling• full coupling

Multi-scales

sputtered and backscattered species and fluxes

Plasma-wall interaction

Moleculardynamics

Binary collisionapproximation

KineticMonte Carlo

Kineticmodel

Fluidmodel

impinging particle and energy fluxes

• Chemical Erosion of carbon by hydrogen produces hydrocarbon species (CxHy)

• Dissociation & Recombination's leads to amorphous hydrocarbon layer formation

• Carbon acts as sponge for hydrogen• Tritium is retained by co-deposition with carbon, on the plasma facing

sides or on remote areas.

HydrogenG F Counsell, Plasma Sources Sci. Technol. 11 (2002) A80–A85

Hydrocarbon-codeposition

2eV CH3 onto amorphous hydrocarbon

Classical MD

MD studies of interaction of hydrocarbons with amorphous carbon:• empricial Brenner potential• reflection coefficients of hydrocarbons on amorphous hydrocarbon(collaboration with K. Nordlund, Univ. Helsinki, U. v. Toussaint, IPP Garching, D. Naujoks, IPP Greifswald)

PhDs (HGF funded), Amit Raj Sharma, Abha Rai

Energy (eV)

00.01 0.1 1 10

CHCH2 CH3CH4

Multi-scale strategies

WWW Uppsala Univ. Sweden

Classification:

A) Microscale info. local

B) Microscale info. global

C) Combination of (A) and (B)

D) Self-similarity in scales

http://www.math.princeton.edu/multiscale/review.pdf

Serial coupling

Concurrent “on the fly” coupling

Renormalization group

Multi-scaling Paradigms:

Multi-scale approach

Microscales

Molecular Dynamics (MD)

Mesoscales

Kinetic Monte Carlo (KMC)

Macroscales

KMC and Monte Carlo Diffusion (MCD)

´Intelligent´ coupling necessary

Meso/macro-scales

)s/cm(D 2

T1000 / )( 1K

Strong dependence on void sizes and not void fraction

Large variation in observed diffusion coefficients

standardgraphites

highly saturatedgraphite

Diffusion coefficients without knowledge of structure are meaningless

Multi-scale modeling of hydrogen transport in porous graphite• inclusion of molecules for re-emission, • extension to chemical sputtering (Küppers-Hopf-cycle) (collaboration with M. Warrier, Max-Planck-India-Fellowship)

PhD (HGF funded): Abha RaiExperiment: P. Franzen, E. Vietzke, J. Vac. Sci. Technology A12(3), 1994

• H-atom release limited by detrapping process, not by diffusion

Modeling:

• results matches very well exp.

Hydrogen re-emission

Temperature (K)

200 600 1000 1400 1800 2200

Temperature (K)600 1000 1400 1800

-0.2 800 1200 1600 2000

H 5% Void

H2 5% Void

H 9% Void

H2 9% Void

• a VERY personal view!

• technical remarks:• open source• standard interfaces• benchmarking

Integrated modeling

• physics remarks:• hierarchical models needed (upgrading, downgrading)• validation (experiment, theory)

• Combination and coupling of more and more codes will not improve the reliability and predictive quality• Depending on the problem and the question one needs very different tools (intelligent interpolation, interpretation, basic physics studies, …)

T. Angot et al., University of Provence, Marseille

STM of graphite surface Simulation

Modelling of Hydrogen bombardement of single crystal

surface science and low-temperature plasma physics

Model systems needed

Summary

• Multi-scale physics: combination of methods • ´Intelligent´ coupling necessary !??

• Hierarchy of models (downgrading, upgrading)

• Real structure of the material to be included

• Model systems needed

• Standards for the modules: open source code, interfaces, benchmarking, model validation

Challenges in edge modeling IPP-Teilinstitut Greifswald, EURATOM Association, Wendelsteinstraße 1,...

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