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Solver comparison for Poisson-like equations on tokamak geometries

Abstract : The solution of Poisson-like equations defined on complex geometry is required for gyrokinetic simulations, which are important for the modelling of plasma turbulence in nuclear fusion devices such as the ITER tokamak. In this paper, we compare three solvers capable of solving this problem, in terms of the accuracy of the solution, and their computational efficiency. The first, the Spline FEM solver, uses C 1 polar splines to construct a finite elements method which solves the equation on curvilinear coordinates. The resulting linear system is solved using a conjugate gradient method. The second, the GmgPolar solver, uses a symmetric finite differences method to discretise the differential equation. The resulting linear system is solved using a tailored geometric multigrid scheme, with a combination of zebra circle and radial line smoothers, together with an implicit extrapolation scheme. The third, the Embedded Boundary solver, uses a finite volumes method on Cartesian coordinates with an embedded boundary scheme. The resulting linear system is solved using a multigrid scheme. The Spline FEM solver is shown to be the most accurate. The GmgPolar solver is shown to use the least memory. The Embedded Boundary solver is shown to be the fastest in most cases. All three solvers are shown to be capable of solving the equation on a realistic non-analytical geometry. The Embedded Boundary solver is additionally used to attempt to solve an X-point geometry, highlighting the problems with concave boundaries.
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https://hal-cea.archives-ouvertes.fr/cea-03786723
Contributor : Emily Bourne Connect in order to contact the contributor
Submitted on : Friday, September 23, 2022 - 4:02:40 PM
Last modification on : Thursday, November 24, 2022 - 4:04:52 AM

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  • HAL Id : cea-03786723, version 1

Citation

Emily Bourne, Philippe Leleux, Katharina Kormann, Carola Kruse, Virginie Grandgirard, et al.. Solver comparison for Poisson-like equations on tokamak geometries. 2022. ⟨cea-03786723⟩

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