Two statistical particle split and merge methods for Particle-in-Cell codes

Pfeiffer, Marcel; Mirza, A.; Munz, Claus‐Dieter; Fasoulas, Stefanos

doi:10.1016/j.cpc.2015.01.010

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…This constraints is more important than the one generally considered [23], [24], only associated to its integrated values: mass/charge, impulsion, energy. This point has already been pointed out [25] while conserving the full distribution function needs a heavier numerical effort. From a computational point of view, the method we present has the advantage of having absolutely no overhead associated to the calculations of the coefficients (weight and locus) if these values are calculated and tabulated.…”

Section: Discussionmentioning

confidence: 85%

“…We also outlined the existence of an exact splitting at the cost of a large number of split particles. Considering the existence of efficient rezoning algorithm [25], one can wonder how the approximate splitting we present in this paper can compare with the exact one, followed by a rezoning procedure. Both options could be used in order to have the same number of split particles.…”

Section: Discussionmentioning

confidence: 99%

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A new method to dispatch split particles in Particle-In-Cell codes

Smets

Aunai

Ciardi

et al. 2021

Computer Physics Communications

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Particle-In-Cell codes are widely used for plasma physics simulations. It is often the case that particles within a computational cell need to be split to improve the statistics or, in the case of non-uniform meshes, to avoid the development of fictitious self-forces. Existing particle splitting methods are largely empirical and their accuracy in preserving the distribution function has not been evaluated in a quantitative way. Here we present a new method

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

A new method to dispatch split particles in Particle-In-Cell codes

Smets

Aunai

Ciardi

et al. 2021

Computer Physics Communications

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“…Pfeiffer et al 14 proposed two algorithms for particle splitting and merging that use a 3D unstructured hexahedral mesh and are expandable to any cell geometry. The first method is computationally feasible and enables preserving particle charges, currents, and energies exactly, while the grid quantities of charge and current are only preserved up to a given accuracy.…”

Section: Introductionmentioning

confidence: 99%

Moment preserving constrained resampling with applications to particle-in-cell methods

Faghihi

Carey

Michoski

et al. 2020

Journal of Computational Physics

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In simulations of partial differential equations using particle-in-cell (PIC) methods, it is often advantageous to resample the particle distribution function to increase simulation accuracy, reduce compute cost, and/or avoid numerical instabilities. We introduce an algorithm for particle resampling called Moment Preserving Contrained Resampling (MPCR). The general algorithm partitions the system space into smaller subsets and is designed to conserve any number of particle and grid quantities with a high degree of accuracy (i.e. machine accuracy). The resampling scheme can be integrated into any PIC code. The advantages of MPCR, including performance, accuracy, and stability, are presented by examining several numerical tests, including a use-case study in gyrokinetic fusion plasma simulations. The tests demonstrate that while the computational cost of MPCR is negligible compared to the nascent particle evolution in PIC methods, periodic particle resampling yields a significant improvement in the accuracy and stability of the results.

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“…This idea has been employed in Ref. 18 in the context of Particle-In-Cell (PIC) methods. Yet, the mentioned study utilized a quite expensive scheme for generating new particles.…”

Section: Introductionmentioning

confidence: 99%

“…20,21 On the reduction side, elaborate schemes have been introduced in numerous works. 18,22,23 For example, in Ref. 23, an octree structure is employed for merging nearest neighbor particles in the sample space.…”

Section: Introductionmentioning

confidence: 99%