In space and astrophysical plasmas, turbulence is responsible for transferring energy from large scales driven by violent events or instabilities, to smaller scales where turbulent energy is ultimately converted into plasma heat by dissipative mechanisms. In the inertial range, the self-similar turbulent energy cascade to smaller spatial scales is driven by the nonlinear interaction between counterpropagating Alfvén waves, denoted Alfvén wave collisions. For the more realistic case of the collision between two initially separated Alfvén wavepackets (rather than previous idealized, periodic cases), we use a nonlinear gyrokinetic simulation code, AstroGK, to demonstrate three key properties of strong Alfvén wave collisions: they (i) facilitate the perpendicular cascade of energy and (ii) generate current sheets self-consistently, and (iii) the modes mediating the nonlinear interaction are simply Alfvén waves. Once the turbulent cascade reaches the ion gyroradius scale, the Alfvén waves become dispersive and the turbulent energy starts to dissipate, energizing the particles via wave-particle interactions with eventual dissipation into plasma heat. The novel Field-Particle Correlation technique determines how turbulent energy dissipates into plasma heat by identifying which particles in velocity-space experience a net gain of energy. By utilizing knowledge of discrete particle arrival times, we devise a new algorithm called PATCH (Particle Arrival Time Correlation for Heliophysics) for implementing a field-particle correlator onboard spacecraft. Using AstroGK, we create synthetic spacecraft data mapped to realistic phase-space resolutions of modern spacecraft instruments. We then utilize Poisson statistics to determine the threshold number of particle counts needed to resolve the velocity-space signature of ion Landau damping using the PATCH algorithm.
Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-8404 |
Date | 01 May 2019 |
Creators | Verniero, J. L. |
Contributors | Howes, Gregory G. |
Publisher | University of Iowa |
Source Sets | University of Iowa |
Language | English |
Detected Language | English |
Type | dissertation |
Format | application/pdf |
Source | Theses and Dissertations |
Rights | Copyright © 2019 J. L. Verniero |
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