Perturbative model for the saturation of energetic-particle-driven modes limited by self-generated zonal modes

TL;DR

This paper introduces a simplified energy-conserving model that captures the nonlinear saturation of energetic-particle-driven modes influenced by self-generated zonal modes, aligning well with gyrokinetic simulation results.

Contribution

The novel aspect is a simplified, energy-conserving model that incorporates zonal mode effects into wave-particle nonlinearities for mode saturation analysis.

Findings

Model reproduces key features of gyrokinetic simulations.

Zonal modes reduce microturbulent particle scattering.

Saturation amplitude decreases with mode growth rate.

Abstract

We present a simplified approach enforcing energy conservation to incorporate the effects of zonal modes alongside wave-particle nonlinearities in the determination of the saturation amplitude. The model assumes that the zonal perturbations grow at a rate twice that of the original (pump) wave, consistent with a beat-driven (or force-driven) generation mechanism. The evolution and saturation of the mode amplitude are investigated both analytically and numerically within our reduced model assumptions, in both the collisionless and scattering-dominated regimes. These studies underscore the crucial role of sources and sinks in accurately capturing the impact and the role of beat-driven zonal perturbations on mode evolution. In the realistic case of saturation set by sources and sinks, we discuss the role of a finite amplitude zonal mode in reducing microturbulent particle scattering, thus limiting the energy source for the resonant mode. We then discuss comparisons between the model predictions and simulation results. The model reproduces key features observed in gyrokinetic simulations as the reduction in saturated mode amplitude and the onset of wave-wave nonlinear effects as functions of mode growth rate and amplitude. Thanks to its simplicity, it can be readily implemented into codes based on reduced models, thereby improving their predictive capability for strongly driven instabilities.