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Research in the Centre for Fusion, Space and Astrophysics

Some of the most fascinating intellectual challenges in plasma physics arise from the generic requirement to understand self-consistent nonlinear phenomenology. For example, recent CFSA projects have investigated cosmic ray acceleration at supernova remnant shocks, magnetohydrodynamic (MHD) wave dynamics, MHD coupling and flux emergence in the solar corona, nonlinear laser-plasma interactions, strong MHD turbulence, and avalanching transport in fusion plasmas.

Such phenomena also present some of the key challenges to HPC, and CFSA develops codes that cover the full range of spatiotemporal scales: Particle-in-Cell, which addresses the many-particle Maxwell-Lorentz system; Vlasov, which combines Maxwell’s equations with a kinetic description of the plasma; Hybrid treatments of kinetic ions and fluid electrons; and adaptive MHD.

In the solar corona, the study of MHD waves leads to the concept of MHD coronal seismology, a novel technique for the remote diagnostics of plasma structures. Observations include imaging and spectral data from current missions such as SOHO, TRACE, Hinode and RHESSI, and ground-based facilities, such as the Nobeyama Radioheliograph, and with the recently launched Solar Dynamics Observatory.

The Centre also specialises in developing and applying novel theoretical approaches to global plasma behaviour, and to the analysis of plasma data. For example, complex systems approaches for out-of-equilibrium phenomena are applied to plasma confinement, both in the solar system and in the fusion context. Intermittent plasma turbulence is studied in the solar wind through missions such as Cluster, WIND, ULYSSES and ACE which provide in-situ measurements, and in the context of turbulent transport in fusion experiments, with data from JET and MAST at CCFE Culham. Novel data analysis techniques for the recognition of patterns in multi-dimensional data cubes are developed and applied, e.g. for the automated detection of waves and oscillations in the solar corona in imaging datasets.

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