Controlling the weights of simulation particles: adaptive particle management using k-d trees

Abstract

In particle simulations, the weights of particles determine how many physical particles they represent. Adaptively adjusting these weights can greatly improve the efficiency of the simulation, without creating severe nonphysical artifacts. We present a new method for the pairwise merging of particles. Pairwise merging reduces the number of particles by combining two particles into one. To find particles that are `close' to each other, we use a k-d tree data structure. With a k-d tree, close neighbors can be searched for efficiently, and independently of the mesh used in the simulation. The merging can be done in different ways, conserving for example momentum or energy. We introduce probabilistic schemes, which set properties for the merged particle using random numbers. The effect of various merge schemes on the energy distribution, the momentum distribution and the grid moments is compared.

References in corpus (3)

• KDTREE 2: Fortran 95 and C++ software to efficiently search for near neighbors in a multi-dimensional Euclidean space
• Why isolated streamer discharges hardly exist above the breakdown field in atmospheric air
• Adaptive Multi-Dimensional Particle In Cell

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