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Max-Planck-Institut für Plasmaphysik, EURATOM Association Different numerical approaches to 3D transport modelling of fusion devices Alexander Kalentyev.

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Presentation on theme: "Max-Planck-Institut für Plasmaphysik, EURATOM Association Different numerical approaches to 3D transport modelling of fusion devices Alexander Kalentyev."— Presentation transcript:

1 Max-Planck-Institut für Plasmaphysik, EURATOM Association Different numerical approaches to 3D transport modelling of fusion devices Alexander Kalentyev Max-Planck Institut für Plasmaphysik, EURATOM Association Stellarator Theory Division

2 Max-Planck-Institut für Plasmaphysik, EURATOM Association Introduction 3D effects: In tokamaks near divertor plates stellarators are intrinsically 3D Ergodicity: Perturbation coils in tokamaks (TEXTOR-DED, DIII-D) In stellarators ergodic region always present

3 Max-Planck-Institut für Plasmaphysik, EURATOM Association Transport equations

4 Max-Planck-Institut für Plasmaphysik, EURATOM Association Finite volume approach (BoRiS) plasma core (non- ergodic) ergodic region island (non- ergodic) Divertors Generalized Newton solver Special application - W7-X using Boozer coordinates for 7 separate domains

5 Max-Planck-Institut für Plasmaphysik, EURATOM Association Flexibility of BoRiS Solution of the Navier-Stokes equations for a flow in a square cavity Predicted streamlines Influence of the under-relaxation parameters on convergence rate Convergence region Peric et al. 1988

6 Scrape Off Layer Plasma core Wall Parallel direction Radial direction Ergodic region Enhancement of radial transport due to contribution from parallel transport Rechester Rosenbluth, Physical Review Letters, 1978 Electron temperature r Max-Planck-Institut für Plasmaphysik, EURATOM Association Transport in an ergodic region

7 Kolmogorov length L K is a measure of field line ergodicity exponential divergence Typical value in W7-X : L K = 10 – 30 m Max-Planck-Institut für Plasmaphysik, EURATOM Association Kolmogorov length

8 central cut backward cut forward cut x1x1 x2x2 x3x3 One coordinate aligned with the magnetic field to minimize numerical diffusion Area is conserved Use a full metric tensor Local system shorter than Kolmogorov length to handle ergodicity Max-Planck-Institut für Plasmaphysik, EURATOM Association Local magnetic coordinates

9 Max-Planck-Institut für Plasmaphysik, EURATOM Association Interface problem 1) Optimized mesh (finite-difference scheme)  Problem: numerical diffusion induced by interpolation on the interface

10 Max-Planck-Institut für Plasmaphysik, EURATOM Association Monte-Carlo 1st Order Algorithm Random process random step Realization Diffusion Convection Monte-Carlo combined with Interpolated Cell Mapping High accuracy transformation of the perpendicular coordinates of a particle (mapping between cuts) needed!

11 Max-Planck-Institut für Plasmaphysik, EURATOM Association Finite Difference Approach Fieldline tracing Triangulation Metric coefficients Transport code Grid Neighborhoods Temperature solution Magnetic field Linearization matrix Mesh optimization

12 Max-Planck-Institut für Plasmaphysik, EURATOM Association “Semi-implicit” scheme Implicit scheme „Semi-implicit“ scheme Memory usage: 7 times less Solver: 50 times faster

13 Max-Planck-Institut für Plasmaphysik, EURATOM Association Results

14 Max-Planck-Institut für Plasmaphysik, EURATOM Association Conclusion and Future Work Conclusion Comparisons between three different codes for a W7-X geometry were done. Future Work To complete the physics (including all transport equations). To compare results in more realistic cases (including target plates, finite beta).

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16 Max-Planck-Institut für Plasmaphysik, EURATOM Association Conduction-convection Convection-conduction equation for a „fluid quantity“ f: x 1 =const x 2 =const x3x3 reference cut „Magnetic“ coordinate system: - contribution from D || in D 33 only Metric tensor: determined by field line tracing

17 Max-Planck-Institut für Plasmaphysik, EURATOM Association Monte-Carlo 1 st Order Algorithm Fokker-Planck Eq. for pseudoscalar density of test particles, Random process Requirement Realization diffusion, convectionsink, source random step independent random numbers physics: diffusion and convection of the “fluid quantity” Higher order schemes in 3D get much too complex Interpretation as probabilistic approximation of Green functions possible


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