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Long global gyrokinetic turbulence simulations are particularly challengingin situations where the system deviates strongly from its initial state andwhen fluctuation level are high e.g. in strong gradient regions. ForParticle-in-Cell simulations, statistical sampling noise accumulation fromlarge marker weights due to large deviations from the control variate of adelta-f scheme make such simulations often impractical. An adaptive controlvariate in the form of a flux-surface-averaged Maxwellian with a time-dependenttemperature profile is introduced in an attempt to alleviate the formerproblem. Under simplified collisionless physics, this adaptive delta-f schemeis shown to reduce noise accumulation in the zonal flows and the simulated heatflux in a quasi-steady turbulent state. The method also avoids the collapse ofthe signal-to-noise ratio which occurs in the standard non-adaptive scheme, andtherefore, allows one to reach numerically converged results even with lowermarker numbers.

physics of plasmas

Gyrokinetic simulations of turbulence and zonal flows driven by steep  profile gradients using a delta-f approach with an evolving background  Maxwellian