Implementing Distributed-Weight Particle Loading for BARS with PIC Codes
POSTER
Abstract
To implement the mini-BARS algorithm [1] for improved sampling of velocity-partitioned phase space, we use a methodology for generating mixed-Maxwellian-weighted electron velocity distribution functions (VDF) as initial conditions. We test these methods on kinetic, nonlinear initial value plasma waves. We show how to down-sample from a master VDF (e.g. ≥108 particles) so that low order moments are conserved as is entropy per velocity partition. From many samples those with the least pathological features are selected. Some advantages of this approach are observed over traditional PIC sampling, including the so called quiet start or random sampling strategy that Monte Carlo techniques such as PIC most naturally adhere to. This work is performed using the versatile and sophisticated LANL DPIC code [2] together with mini-BARS.
Publication: [1] B. Afeyan, S. Finnegan, L. Chacon, BARS: Bidirectional Adaptive Refinement Scheme for learned, adaptive particle-in-cell simulations of plasma kinetics, Manuscript in preparation, (2022); Afeyan, Finnegan and Chacon, J. Hittinger, D. Larson, Accelerated Ensembles of Plasma Kinetic Simulations through Adaptive Learning [ NSCAR, BARS, mini-BARS, TPOR and all that], APS-DPP (2022)<br>[2] G. Chen and L Chacon, Comp. Phys. Comm. 197, (2015)
Presenters
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Sean M Finnegan
Los Alamos National Laboratory
Authors
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Sean M Finnegan
Los Alamos National Laboratory
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Bedros B Afeyan
Polymath Research Inc
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Luis Chacon
Los Alamos Natl Lab, Los Alamos National Lab
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David J Bernstein
Los Alamos National Laboratory