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Register a new userSymplectic Maps for Diverted Plasmas
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Nowadays, divertors are used in the main tokamaks to control the magnetic field and to improve the plasma confinement. In this article, we present analytical symplectic maps describing Poincare maps of the magnetic field lines in confined plasmas with a single null poloidal divertor. Initially, we present a divertor map and the tokamap for a diverted configuration. We also introduce the Ullmann map for a diverted plasma, whose control parameters are determined from tokamak experiments. Finally, an explicit, area-preserving and integrable magnetic field line map for a single-null divertor tokamak is obtained using a trajectory integration method to represent toroidal equilibrium magnetic surfaces. In this method, we also give examples of onset of chaotic field lines at the plasma edge due to resonant perturbations.
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Chaotic transport is a subject of paramount importance in a variety of problems in plasma physics, specially those related to anomalous transport and turbulence. On the other hand, a great deal of information on chaotic transport can be obtained from simple dynamical systems like two-dimensional area-preserving (symplectic) maps, where powerful mathematical results like KAM theory are available. In this work we review recent works on transport barriers in area-preserving maps, focusing on systems which do not obey the so-called twist property. For such systems KAM theory no longer holds everywhere and novel dynamical features show up as non-resistive reconnection, shearless curves and shearless bifurcations. After presenting some general features using a standard nontwist mapping, we consider magnetic field line maps for magnetically confined plasmas in tokamaks.
Full-size turbulence simulations of the divertor and scrape-off-layer of existing tokamaks have recently become feasible, allowing direct comparisons of turbulence simulations to experimental measurements. We present a validation of three flux-driven turbulence codes (GBS, GRILLIX and TOKAM3X) against an experimental dataset from diverted Ohmic L-mode discharges on the TCV tokamak. The dataset covers the divertor targets, volume, entrance and OMP via 5 diagnostic systems, giving a total of 45 comparison observables over two toroidal field directions. The simulations show good agreement at the OMP for most observables. At the targets and in the divertor volume, several observables show good agreement, but the overall match is lower than at the OMP. The simulations typically find the correct order-of-magnitude and the approximate shape for the divertor mean profiles. The experimental profiles of the divertor density, potential, current and velocity vary strongly with field direction, while a weaker effect is found in the simulations. The simulated divertor profiles are found to be sensitive to the choice of sheath boundary conditions and the use of increased collisionality. The observed divertor flows suggest that divertor neutral ionisation is non-negligible. This indicates that the match could be improved by using improved boundary conditions, more realistic parameters and including self-consistent neutral physics. Future validation and benchmarking against the TCV-X21 reference dataset will assess the impact of improvements to the codes and will guide their targeted development - a process which can be extended to other turbulence codes via the freely available TCV-X21 reference dataset. As such, this work assesses the current capabilities of edge/divertor turbulence simulations and provides a systematic path towards their improved interpretive and predictive capability.
Advanced spectral and statistical data analysis techniques have greatly contributed to shaping our understanding of microphysical processes in plasmas. We review some of the main techniques that allow for characterising fluctuation phenomena in geospace and in laboratory plasma observations. Special emphasis is given to the commonalities between different disciplines, which have witnessed the development of similar tools, often with differing terminologies. The review is phrased in terms of few important concepts: self-similarity, deviation from self-similarity (i.e. intermittency and coherent structures), wave-turbulence, and anomalous transport.
Beginning from the semiclassical Hamiltonian, the Fermi pressure and Bohm potential for the quantum hydrodynamics application (QHD) at finite temperature are consistently derived in the framework of the local density approximation with the first order density gradient correction. Previously known results are revised and improved with a clear description of the underlying approximations. A fully non-local Bohm potential, which goes beyond of all previous results and is linked to the electron polarization function in the random phase approximation, for the QHD model is presented. The dynamic QHD exchange correlation potential is introduced in the framework of local field corrections, and considered for the case of the relaxation time approximation. Finally, the range of applicability of the QHD is discussed.
We present an approach to construct appropriate and efficient emulators for Hamiltonian flow maps. Intended future applications are long-term tracing of fast charged particles in accelerators and magnetic plasma confinement configurations. The method is based on multi-output Gaussian process regression on scattered training data. To obtain long-term stability the symplectic property is enforced via the choice of the matrix-valued covariance function. Based on earlier work on spline interpolation we observe derivatives of the generating function of a canonical transformation. A product kernel produces an accurate implicit method, whereas a sum kernel results in a fast explicit method from this approach. Both correspond to a symplectic Euler method in terms of numerical integration. These methods are applied to the pendulum and the Henon-Heiles system and results compared to an symmetric regression with orthogonal polynomials. In the limit of small mapping times, the Hamiltonian function can be identified with a part of the generating function and thereby learned from observed time-series data of the systems evolution. Besides comparable performance of implicit kernel and spectral regression for symplectic maps, we demonstrate a substantial increase in performance for learning the Hamiltonian function compared to existing approaches.
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