Name
Title | ||
|---|---|---|
Electromagnetic gyrokinetic PIC simulation with an adjustable control variates method |
Abstract | ||
|---|---|---|
In the last decade, it became clear that electromagnetic (gyro)kinetic particle-in-cell (PIC) simulations are very demanding in respect to numerical methods and the number of markers used. The Monte Carlo discretization of the gyrokinetic equations leads to a severe signal-to-noise problem: the statistical representation of the physically irrelevant but numerically dominant adiabatic current causing a high statistical noise level. The corresponding inaccuracy problem is very pronounced at high plasma @b and/or small perpendicular wave numbers k"@?. We derive several numerical schemes to overcome the problem using an adjustable control variates method which adapts to the dominant adiabatic part of the gyro-center distribution function. We have found that the inaccuracy problem is also present in the quasi-neutrality equation as a consequence of the p"@?-formulation [T.S. Hahm, W.W. Lee, A. Brizard, Nonlinear gyrokinetic theory for finite-beta plasmas, Phys. Fluids 31 (1988) 1940]. For slab simulations in the magnetohydrodynamic (MHD) limit k"@?-0, the number of markers can be reduced by more than four orders of magnitude compared to a conventional @df scheme. The derived schemes represent first steps on a road to fully adaptive control variates method which can significantly reduce the inherent statistical noise of PIC codes. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.jcp.2006.12.019 | J. Comput. Physics |
Keywords | Field | DocType |
inherent statistical noise,high statistical noise level,nonlinear gyrokinetic theory,statistical representation,inaccuracy problem,adjustable control variates method,corresponding inaccuracy problem,pic code,control variates method,52.30.gz,52.65.rr,gyrokinetic simulation,77c10,severe signal-to-noise problem,adaptive control variates method,65m25,electromagnetic,δ f method,magnetohydrodynamics,numerical method,particle in cell,control variates,distribution function,monte carlo,adaptive control,kinetics,variational method | Adiabatic process,Discretization,Mathematical optimization,Monte Carlo method,Nonlinear system,Mathematical analysis,Control variates,Adaptive control,Numerical analysis,Distribution function,Mathematics | Journal |
Volume | Issue | ISSN |
225 | 1 | Journal of Computational Physics |
Citations | PageRank | References |
4 | 1.10 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Hatzky | 1 | 40 | 9.85 |
| A. Könies | 2 | 8 | 2.82 |
| A. Mishchenko | 3 | 4 | 1.10 |