Bulletin of the American Physical Society
64th Annual Meeting of the APS Division of Plasma Physics
Volume 67, Number 15
Monday–Friday, October 17–21, 2022; Spokane, Washington
Session TP11: Poster Session VII: In-Person, Hall A (9:30-11:00am) and Virtual Poster Presentations (11:15am-12:30pm)
MFE: FRC, RFPs etc
ICF: Fast Ignition; Diagnostics; Computational; Laser Plasma Interactions
FUND: Computation
9:30 AM - 12:30 PM
Thursday, October 20, 2022
Room: Exhibit Hall A and Online
Abstract: TP11.00118 : Meshfree particle model for kinetic plasma simulations*
Presenter:
John M Finn
(Los Alamos Natl Lab)
Authors:
John M Finn
(Los Alamos Natl Lab)
Evstati G Evstatiev
(Sandia National Laboratories)
plasma, using kernel density estimation and a similar method for the electric
field E. The kernel K(x − y) represents the macroparticle charge distribution.
Two length scales enter, the width w of K and the interparticle spacing λ. This
model conserves momentum and energy. Similarly, continuity is satisfied exactly,
and the Gauss’s law and Ampere’s law formulations are exactly equivalent. A
unified analysis is used for numerical stability and noise properties. The force
can be computed directly using the convolution K2 = K ∗ K, and K2 is positive
definite. We discuss the analogy in the presence of a grid. We can specify a
single kernel Kp , related to the `kernel trick’ of machine learning. Numerical
instability can occur unless Kp is positive definite, related to a breakdown in
energy conservation. For the noise analysis, the covariance matrix for the electric field shows a plasma dispersion function modified by w and λ. The number of particles per cell does not enter, and the noise is characterized by the number of particles per kernel width, i.e. w/λ. We present the bias-variance optimization (BVO) for the electric field, and compare it to the density BVO.
*SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.
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