What place for mathematicians in plasma physics...Introduction What motivates a mathematician to...
Transcript of What place for mathematicians in plasma physics...Introduction What motivates a mathematician to...
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What place for mathematicians in plasma physics
Eric Sonnendrucker
IRMAUniversite Louis Pasteur, Strasbourg
projet CALVIINRIA Nancy Grand Est
15-19 September 2008
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Introduction
What motivates a mathematician to work on plasma physics problems
Challenging programs with important applicationsInteresting new problems: difficult, novel in mathematics
What kind of mathematics are needed in plasma physics
Modeling: approximate models, links between different scalesAnalysis: existence, uniqueness, long time behavior, regularity ofsolutions,...Numerical analysis: Analyze numerical methods, develop new methods.High performance scientific computing: very large problems need to besolved. At the edge of available computing power.Hamiltonian systems: Tokamak, long time numerical integration.
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Outline
1 Challenging projects
2 From mathematics to plasma physics
3 Mathematical problems arising from plasma physics
4 Gyrokinetic models
5 Development of numerical methods
6 Development of the semi-Lagrangian method
7 High performance scientific computing
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Controlled thermonuclear fusion
Confinement magnetique (ITER)
Confinement inertiel
par laser (LMJ)par ions lourds
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The ITER project
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Political context
ITER project launched, partnership between European Union, Japan,China, South Korea, Russia, USA and India. International agreementsigned 21st november 2006.
Enlarged approach. Convention between EU and Japan on companionprogram. ITER in Cadarache, Japon becomes
Small experimental TokamakParticle accelerator generating 14 MeV neutrons for material testing.Computing center dedicated to magnetic fusion.
Ambitious scientific and technological development program in the7th european Framework Program (FP7) (EURATOM, associationEURATOM-CEA, EFDA).
Magnetic fusion federation founded in France grouping CEA, leCNRS, l’INRIA and several universitys to pilot ITER related research
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Roadmap towards a fusion powerplant
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Laser MegaJoule
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Outline
1 Challenging projects
2 From mathematics to plasma physics
3 Mathematical problems arising from plasma physics
4 Gyrokinetic models
5 Development of numerical methods
6 Development of the semi-Lagrangian method
7 High performance scientific computing
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Mathematical tools of fundamental plasma physics
Complex analysis: Landau Damping, and in general derivation ofkinetic dispersion relations. Residue theorem, integration alongcontours in complex plane.
Differential geometry: Toroidal form of equilibria in magnetic field(Poincare). Coordinate systems used in Tokamaks and stellerators.
Old mathematical tools.
No real interaction between physicists and mathematicians on thesesubjects today.
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Outline
1 Challenging projects
2 From mathematics to plasma physics
3 Mathematical problems arising from plasma physics
4 Gyrokinetic models
5 Development of numerical methods
6 Development of the semi-Lagrangian method
7 High performance scientific computing
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Huge span of time and space scales
Typical space scales in Tokamak
λD = ρe ∼ 5× 105, ρi ∼ 10−3, a ∼ 1, mfp ∼ 103.
Simulation time scales in codes
Gyrokinetic 10−6 to 10−3
MHD 10−4 to 1Fluid transport 10−3 to 10.
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Analysis of models from plasma physics
Vast literature on analysis of kinetic models of plasma physics.
Existence of strong solutions of Vlasov-Poisson, Vlasov-Maxwell andrelated models.Existence of weak solutions of Vlasov-Poisson, Vlasov-Maxwell andrelated models.
Analysis of singular limits in plasma physics
The quasi-neutral limit of Vlasov-Poisson.Plasmas in a large magnetic field. Guiding-center, drift-kinetic,gyrokinetic.
Analysis of numerical methods
Convergence proofs for Particle-In-Cell methods.Convergence proofs for semi-Lagrangian methods in differentframeworks.
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Outline
1 Challenging projects
2 From mathematics to plasma physics
3 Mathematical problems arising from plasma physics
4 Gyrokinetic models
5 Development of numerical methods
6 Development of the semi-Lagrangian method
7 High performance scientific computing
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Derivation of gyrokinetic model
Gyrokinetic equations used in codes have been most of the timeobtained using a Lie transform method in order to find a coordinatesystem allowing to decouple fast and slow motions in a Hilbertexpansion of the characteristics. Littlejohn, Hahm, Brizard, Qin...
In codes mostly 5D model describing the evolution of guiding centersf (r , θ, φ, v‖, µ) obtained and used by Hahm (Phys. Fluids 1988),Hatzky et al. (Phys. Plasmas 2002), and Brizard-Hahm in a reviewarticle (Reviews of Modern Physics 2007).
The limit model is Hamiltonian and in particular verifies exactconservation of particles and energy which are important for long timesimulations.
An other approach (Sosenko) is based on Boboliugov-Mitropolskyaveraging techniques.
The rigorous mathematical justification of these models is still farfrom complete. Partial results have been obtained by. Brenier-Grenier,Golse-Saint-Raymond, Frenod-Sonnendrucker, Bostan.
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Outline
1 Challenging projects
2 From mathematics to plasma physics
3 Mathematical problems arising from plasma physics
4 Gyrokinetic models
5 Development of numerical methods
6 Development of the semi-Lagrangian method
7 High performance scientific computing
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Model coupling
Enhanced fluid models where most particles are treated as fluids, butsome population is extracted and modeled with kinetic equations.
Integrated tokamak modeling: model full tokamak discharge using asuite of several codes.
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Asymptotics preserving schemes
Based on idea of Shi Jin.
Very rich developments for plasma physics applications in Degond’steam.
See Pierre Degond’s lectures on gyro-fluid and gyro-kinetic limit.
Principle is to find a numerical scheme that is robust for two modelsthe one being the limit of the other.
Useful when both models are needed in the problem. Couplingautomatically performed by the numerical scheme.
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Outline
1 Challenging projects
2 From mathematics to plasma physics
3 Mathematical problems arising from plasma physics
4 Gyrokinetic models
5 Development of numerical methods
6 Development of the semi-Lagrangian method
7 High performance scientific computing
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Operator splitting
Consider e.g. the non relativistic Vlasov-Poisson equation
∂f
∂t+ v · ∇x f +
q
mE · ∇v f = 0.
We decompose the equation into the two following steps.
∂f
∂t+ v · ∇x f = 0, (1)
with v fixed and∂f
∂t+
q
mE(x, t) · ∇v f = 0, (2)
with x fixed.
We solve the two equations successively on one time step. At leastdimension reduction and in our example constant coefficientadvections for reduced equations.
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Conservativity
Consider abstract Vlasov equation where z are all the phase spacevariables
∂f
∂t+ a(z, t) · ∇z f = 0 with ∇ · a = 0.
The equation is conservative: ddt
∫f dz = 0.
Consider splitting the equations by decomposing the variables into z1
and z2. Then the split equations read
∂f
∂t+ a1(z, t) · ∇z1f = 0, and
∂f
∂t+ a2(z, t) · ∇z2f = 0.
We have ∇ · a = ∇z1 · a1 +∇z2 · a2 = 0, but in general ∇z1 · a1 and∇z2 · a2 do not vanish separately.
One or more of the split equations may not be conservative.
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Example 1: Vlasov-Poisson
In this case the Vlasov equation reads
∂f
∂t+ v · ∇x f + E · ∇v f = 0.
So a = (v,E(x, t))
Standard splitting yields:
∂f
∂t+ v · ∇x f = 0 and
∂f
∂t+ E · ∇v f = 0.
So that a1 = v and a1 = E(x, t).
In this case ∇x · a1 = 0 and ∇v · a2 = 0.
Splitting yields two conservative equations.
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Example 2: guiding-center model
Classical model for magnetized plasmas. Describes motion in planeperpendicular to magnetic field.
∂ρ
∂t+ vD · ∇ρ = 0, −∆φ = ρ,
vD =−∇φ× B
B2=
(−∂φ
∂y∂φ∂x
)if B = ez unit vector in direction z .
The model is conservative: ∇ · vD = − ∂2φ∂x∂y + ∂2φ
∂y∂x = 0.
Split equations become
∂ρ
∂t− ∂φ
∂y
∂ρ
∂x= 0,
∂ρ
∂t+
∂φ
∂x
∂ρ
∂x= 0.
In general ∂2φ∂x∂y 6= 0.
The split equations are not conservative.
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Problem with non conservative Vlasov solver
When non conservative splitting is used for the numerical solver, thesolver is not exactly conservative.
Does generally not matter when solution is smooth and well resolvedby the grid. The solver is still second order and yields good results.
However: Fine structures develop in non linear simulations and are atsome point locally not well resolved by the phase space grid.
In this case a non conservative solvers can exhibit a large numericalgain or loss of particles which is totally unphysical.
Lack of robustness.
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Vortex in Kelvin-Helmholtz instability
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Outline
1 Challenging projects
2 From mathematics to plasma physics
3 Mathematical problems arising from plasma physics
4 Gyrokinetic models
5 Development of numerical methods
6 Development of the semi-Lagrangian method
7 High performance scientific computing
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Supercomputing
Two supercomputers dedicated for magnetic fusion in near future(Julich 2009, Japan 2012).
More than 10000 processors usable for gyrokinetic codes.
New programming constraint (with respect to < 100 processors) forefficient use:
No transfer from one processor to all.No global data redistribution.Sophisticated adaptive methods probably less competitive due tooverhead.Charge balance problems for particle methods.Advantage to local methods with static charge balance.
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Conclusions
Whole range of interesting mathematical problems arising fromplasma physics.
Most research in mathematics and plasma physics without much realtime interaction: mathematical results not used directly by physicists.
Direct collaboration and interaction is more on codes: numericalalgorithms and parallel computing.
Physicists and mathematicians have a very different approach inproblem solving.
Different ways of thinking complementary and beneficiary in the longterm.
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