Search

We propose a second-order temporally implicit, fourth-order-accurate spatial discretization scheme for the strongly anisotropic heat transport equation characteristic of hot, fusion-grade plasmas. Following [Du Toit et al., Comp. Phys. Comm., 228 (2018)], the scheme transforms mixed-derivative diffusion fluxes (which are responsible for the lack of a discrete maximum principle) into nonlinear advective fluxes, amenable to nonlinear-solver-friendly monotonicity-preserving limiters. The scheme enables accurate multi-dimensional heat transport simulations with up to seven orders of magnitude of heat-transport-coefficient anisotropies with low cross-field numerical error pollution and excellent algorithmic performance, with the number of linear iterations scaling very weakly with grid resolution and grid anisotropy, and scaling with the square-root of the implicit timestep. We propose a multigrid preconditioning strategy based on a second-order-accurate approximation that renders the scheme efficient and scalable under grid refinement. Several numerical tests are presented that display the expected spatial convergence rates and strong algorithmic performance, including fully nonlinear magnetohydrodynamics simulations of kink instabilities in a Bennett pinch in 2D helical geometry and of ITER in 3D toroidal geometry.

We extend the recently proposed semi-Lagrangian algorithm for the extremely anisotropic heat transport equation [Chac\'on et al., J. Comput. Phys., 272 (2014)] to deal with arbitrary magnetic field topologies. The original scheme (which showed remarkable numerical properties) was valid for the so-called tokamak-ordering regime, in which the magnetic field magnitude was not allowed to vary much along field lines. The proposed extension maintains the attractive features of the original scheme (including the analytical Green's function, which is critical for tractability) with minor modifications, while allowing for completely general magnetic fields. The accuracy and generality of the approach are demonstrated by numerical experiment with an analytical manufactured solution.

Coulomb collisions in particle simulations for weakly coupled plasmas are modeled by the Landau-Fokker-Planck equation, which is typically solved by Monte-Carlo (MC) methods. One of the main disadvantages of MC is the timestep accuracy constraint {\nu}<<1 to resolve the collision frequency {\nu}. The constraint becomes extremely stringent for self-collisions in the presence of high-charge state species and for inter-species collisions with large mass disparities (such as present in Inertial Confinement Fusion hohlraums), rendering long-time-scale simulations prohibitively expensive or impractical. To overcome these difficulties, we explore a hybrid Maxwellian-MC (HMMC) model for particle simulations. Specifically, we devise a collisional algorithm that describes weakly collisional species with particles, and highly collisional species and fluid components with Maxwellians. We employ the Lemons method for particle-Maxwellian collisions, enhanced with a more careful treatment of low-relative-speed particles, and a five-moment model for Maxwellian-Maxwellian collisions. Particle-particle binary collisions are dealt with classic Takizuka-Abe MC, which we extend to accommodate arbitrary particle weights to deal with large density disparities without compromising conservation properties. HMMC is strictly conservative and significantly outperforms standard MC methods in situations with large mass disparities among species or large charge states, demonstrating orders of magnitude improvement in computational efficiency. We will substantiate the accuracy and performance of the proposed method with several examples of varying complexity, including both zero-dimensional relaxation and one-dimensional transport problems, the latter using a hybrid kinetic-ion/fluid-electron model.

In this paper, we extend the operator-split asymptotic-preserving, semi-Lagrangian algorithm for time dependent anisotropic heat transport equation proposed in [Chac\'on et al., JCP, 272, 719-746, 2014] to use a fully implicit time integration with backward differentiation formulas. The proposed implicit method can deal with arbitrary heat-transport anisotropy ratios \chi_{\parallel}/\chi_{\perp}\ggg1 (with \chi_{\parallel}, \chi_{\perp} the parallel and perpendicular heat diffusivities, respectively) in complicated magnetic field topologies in an accurate and efficient manner. The implicit algorithm is second-order accurate temporally, has favorable positivity preservation properties, and demonstrates an accurate treatment at boundary layers (e.g., island separatrices), which was not ensured by the operator-split implementation. The condition number of the resulting algebraic system is independent of the anisotropy ratio, and is inverted with preconditioned GMRES. We propose a simple preconditioner that renders the linear system compact, resulting in mesh-independent convergence rates for topologically simple magnetic fields, and convergence rates scaling as \sim(N\Delta t)^{1/4} (with N the total mesh size and \Delta t the timestep) in topologically complex magnetic-field configurations. We demonstrate the accuracy and performance of the approach with test problems of varying complexity, including an analytically tractable boundary-layer problem in a straight magnetic field, and a topologically complex magnetic field featuring magnetic islands with extreme anisotropy ratios (\chi_{\parallel}/\chi_{\perp}=10^{10}).

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects., Comment: 48 pages, https://iopscience.iop.org/article/10.1088/1361-6501/ac2dbd

The hybrid kinetic-ion fluid-electron plasma model is widely used to study challenging multi-scale problems in space and laboratory plasma physics. Here, a novel conservative scheme for this model employing implicit particle-in-cell techniques is extended to arbitrary coordinate systems via curvilinear maps from logical to physical space. The scheme features a fully non-linear electromagnetic formulation with a multi-rate time advance - including sub-cycling and orbit-averaging for the kinetic ions. By careful choice of compatible particle-based kinetic-ion and mesh-based fluid-electron discretizations in curvilinear coordinates, as well as particle-mesh interpolations and implicit midpoint time advance, the scheme is proven to conserve total energy for arbitrary curvilinear meshes. In the electrostatic limit, the method is also proven to conserve total momentum for arbitrary curvilinear meshes. Although momentum is not conserved for arbitrary curvilinear meshes in the electromagnetic case, it is for an important subset of Cartesian tensor-packed meshes. The scheme and its novel conservation properties are demonstrated for several challenging numerical problems using different curvilinear meshes, including a merging flux-rope simulation for a space weather application, and a helical $m=1$ mode simulation for magnetic fusion energy application.

A fully implicit particle-in-cell method for handling the $v_\parallel$-formalism of electromagnetic gyrokinetics has been implemented in XGC. By choosing the $v_\parallel$-formalism, we avoid introducing the non-physical skin terms in Amp\`{e}re's law, which are responsible for the well-known ``cancellation problem" in the $p_\parallel$-formalism. The $v_\parallel$-formalism, however, is known to suffer from a numerical instability when explicit time integration schemes are used due to the appearance of a time derivative in the particle equations of motion from the inductive component of the electric field. Here, using the conventional $\delta f$ scheme, we demonstrate that our implicitly discretized algorithm can provide numerically stable simulation results with accurate dispersive properties. We verify the algorithm using a test case for shear Alfv\'{e}n wave propagation in addition to a case demonstrating the ITG-KBM transition. The ITG-KBM transition case is compared to results obtained from other $\delta f$ gyrokinetic codes/schemes, whose verification has already been archived in the literature.

We propose an unsupervised machine-learning checkpoint-restart (CR) algorithm for particle-in-cell (PIC) algorithms using Gaussian mixtures (GM). The algorithm features a particle compression stage and a particle reconstruction stage, where a continuum particle distribution function (PDF) is constructed and resampled, respectively. To guarantee fidelity of the CR process, we ensure the exact preservation of invariants such as charge, momentum, and energy for both compression and reconstruction stages, everywhere on the mesh. We also ensure the preservation of Gauss' law after particle reconstruction. As a result, the GM CR algorithm is shown to provide a clean, conservative restart capability while potentially affording orders of magnitude savings in input/output requirements. We demonstrate the algorithm using a recently developed exactly energy- and charge-conserving PIC algorithm using both electrostatic and electromagnetic tests. The tests demonstrate not only a high-fidelity CR capability, but also its potential for enhancing the fidelity of the PIC solution for a given particle resolution.

The quasi-neutral hybrid particle-in-cell algorithm with kinetic ions and fluid electrons is a popular model to study multi-scale problems in laboratory, space, and astrophysical plasmas. Here, it is shown that the treatment of ions as finite-size particles and electrons as a grid-based fluid can cause significant numerical wave dispersion errors in the magnetohydrodynamic limit ($kd_i \ll 1$, where $d_i$ is the ion skin-depth). Practical requirements on the mesh spacing $\Delta x/d_i$ are suggested to bound these errors from above.

Finite-grid (or aliasing) instabilities are pervasive in particle-in-cell (PIC) plasma simulation algorithms, and force the modeler to resolve the smallest (Debye) length scale in the problem regardless of dynamical relevance. These instabilities originate in the aliasing of interpolation errors between mesh quantities and particles (which live in the space-time continuum). Recently, strictly energy-conserving PIC (EC-PIC) algorithms have been developed that promise enhanced robustness against aliasing instabilities. In this study, we confirm by analysis that EC-PIC is stable against aliasing instabilities for stationary plasmas. For drifting plasmas, we demonstrate by analysis and numerical experiments that, while EC-PIC algorithms are not free from these instabilities in principle, they feature a benign stability threshold for finite-temperature plasmas that make them usable in practice for a large class of problems (featuring ambipolarity and realistic ion-electron mass ratios) without the need to resolve Debye lengths spatially. We also demonstrate that this threshold is absent for the popular momentum-conserving PIC algorithms, which are therefore unstable for both drifting and stationary plasmas., Comment: 17 pages, 12 figures

Recent progress in the study of Cahn-Hilliard Navier-Stokes (CHNS) turbulence is summarized. This is an example of \textit{elastic turbulence}, which can occur in elastic (i.e. self-restoring) media. Such media exhibit memory due freezing-in laws, as does MHD, which in turn constrains the dynamics. We report new results in the theory of CHNS turbulence in 2D, with special emphasis on the role of structure (i.e. `blob') formation and its interaction with the dual cascade. The evolution of a concentration gradient in response to a single eddy -- analogous to flux expulsion in MHD -- is analyzed. Lessons learned are discussed in the context of MHD and other elastic media.

This Letter identifies the physical mechanism for the quench of turbulent resistivity in 2D MHD. Without an imposed, ordered magnetic field, a multi-scale, blob-and-barrier structure of magnetic potential forms spontaneously. Magnetic energy is concentrated in thin, linear barriers, located at the interstices between blobs. The barriers quench the transport and kinematic decay of magnetic energy. The local transport bifurcation underlying barrier formation is linked to the inverse cascade of $\langle A^2\rangle$ and negative resistivity, which induce local bistability. For small scale forcing, spontaneous layering of the magnetic potential occurs, with barriers located at the interstices between layers. This structure is effectively a magnetic staircase.

We propose an efficient, robust, Lagrangian (characteristic-based) transport solver for the time-dependent thermal radiative Transfer (TRT) applications within the context of a moment-accelerated (High-Order/Low-Order, HOLO) algorithm. This novel transport algorithm inherits the best features of both particle methods (e.g., time accuracy, phase-space adaptivity, positivity) and deterministic, grid-based methods (e.g., no stochastic noise). Particles are evolved by the method of characteristics, with a time-dependent weight that accounts for stiff absorption and reemission process self-consistently. As a result, this approach is able to obtain accurate results while employing large time steps and a moderate number of particles (compared to Implicit Monte Carlo).

The quasi-neutral hybrid model with kinetic ions and fluid electrons is a promising approach for bridging the inherent multi-scale nature of many problems in space and laboratory plasmas. Here, a novel, implicit, particle-in-cell based scheme for the hybrid model is derived for multi-dimensional electromagnetic problems with multiple ion species, which features global mass, momentum and energy conservation. The scheme includes sub-cycling and orbit averaging of the ions, with cell-centered finite differences and implicit midpoint time advance. To reduce discrete particle noise, the scheme allows arbitrary-order shape functions for the particle-mesh interpolations and the application of conservative binomial smoothing. The algorithm is verified for a number of test problems to demonstrate the correctness of the implementation, the unique conservation properties, and the favorable stability properties of the new scheme. In particular, there is no indication of unstable growth of the finite-grid instability for a population of cold ions drifting through a uniform spatial mesh, in a set-up where several commonly used non-conservative schemes are highly unstable.

A number of studies have considered how the rate of magnetic reconnection scales in large and weakly collisional systems by the modelling of long reconnecting current sheets. However, this set-up neglects both the formation of the current sheet and the coupling between the diffusion region and a larger system that supplies the magnetic flux. Recent studies of magnetic island merging, which naturally include these features, have found that ion kinetic physics is crucial to describe the reconnection rate and global evolution of such systems. In this paper, the effect of a guide field on reconnection during island merging is considered. In contrast to the earlier current sheet studies, we identify a limited range of guide fields for which the reconnection rate, outflow velocity, and pile-up magnetic field increase in magnitude as the guide field increases. The Hall-MHD fluid model is found to reproduce kinetic reconnection rates only for a sufficiently strong guide field, for which ion inertia breaks the frozen-in condition and the outflow becomes Alfvenic in the kinetic system. The merging of large islands occurs on a longer timescale in the zero guide field limit, which may in part be due to a mirror-like instability that occurs upstream of the reconnection region., Comment: 12 pages, 10 figures. Submitted to PoP

We study the fundamental physics of cascades and spectra in 2D Cahn-Hilliard-Navier-Stokes (CHNS) turbulence, and compare and contrast this system with 2D MagnetoHydroDynamic (MHD) turbulence. The important similarities include basic equations, ideal quadratic invariants, cascades and the role of linear elastic waves. Surface tension induces elasticity, and the balance between surface tension energy and turbulent kinetic energy determines a length scale (Hinze scale) of the system. The Hinze scale may be thought of as the scale of emergent critical balance between fluid straining and elastic restoring forces. The scales between the Hinze scale and dissipation scale constitute the elastic range of the 2D CHNS system. By direct numerical simulation, we find that in the elastic range, the mean square concentration spectrum $H^\psi_k$ of the 2D CHNS system exhibits the same power law ($-7/3$) as the mean square magnetic potential spectrum $H^A_k$ in the inverse cascade regime of 2D MHD. This power law is consistent with an inverse cascade of $H^\psi$, which is observed. The kinetic energy spectrum of the 2D CHNS system is $E^K_k\sim k^{-3}$ if forced at large scale, suggestive of the direct enstrophy cascade power law of 2D Navier-Stokes (NS) turbulence. The difference from the energy spectra of 2D MHD turbulence implies that the back reaction of the concentration field to fluid motion is limited. We suggest this is because the surface tension back reaction is significant only in the interfacial regions. The interfacial regions fill only a small portion of the 2D CHNS system, and their interface packing fraction is much smaller than that for 2D MHD.

The effect of magnetic perturbations (MPs) on the helical self-organization of shaped tokamak plasmas is discussed in the framework of the nonlinear 3D MHD model. Numerical simulations performed in toroidal geometry with the \textsc{pixie3d} code [L. Chac\'on, Phys. Plasmas {\bf 15}, 056103 (2008)] show that $n=1$ MPs significantly affect the spontaneous quasi-periodic sawtoothing activity of such plasmas. In particular, the mitigation of sawtooth oscillations is induced by $m/n=1/1$ and $2/1$ MPs. These numerical findings provide a confirmation of previous circular tokamak simulations, and are in agreement with tokamak experiments in the RFX-mod and DIII-D devices. Sawtooth mitigation via MPs has also been observed in reversed-field pinch simulations and experiments. The effect of MPs on the stochastization of the edge magnetic field is also discussed., Comment: Proceeding paper of the 7th IAEA Technical Meeting on 'Theory of Plasmas Instabilities', Frascati (Italy), March 4-6, 2015. This manuscript will be published on the conference website http://www.iaeatm-inst2015.enea.it/

The fast rates of magnetic reconnection found in both nature and experiments are important to understand theoretically. Recently, it was demonstrated that two-fluid magnetic reconnection remains fast in the strong guide field regime, regardless of the presence of fast-dispersive waves. This conclusion is in agreement with recent results from kinetic simulations, and is in contradiction to the findings in an earlier two-fluid study, where it was suggested that fast-dispersive waves are necessary for fast reconnection. In this paper, we give a more detailed derivation of the analytic model presented in a recent letter, and present additional simulation results to support the conclusions that the magnetic reconnection rate in this regime is independent of both collisional dissipation and system-size. In particular, we present a detailed comparison between fluid and kinetic simulations, finding good agreement in both the reconnection rate and overall length of the current layer. Finally, we revisit the earlier two-fluid study, which arrived at different conclusions, and suggest an alternative interpretation for the numerical results presented therein., Comment: 17 pages, 19 figures, accepted in Physics of Plasmas

A novel approach that enables the study of parallel transport in magnetized plasmas is presented. The method applies to general magnetic fields with local or nonlocal parallel closures. Temperature flattening in magnetic islands is accurately computed. For a wave number $k$, the fattening time scales as $\chi_{\parallel} \tau \sim k^{-\alpha}$ where $\chi$ is the parallel diffusivity, and $\alpha=1$ ($\alpha=2$) for non-local (local) transport. The fractal structure of the devil staircase temperature radial profile in weakly chaotic fields is resolved. In fully chaotic fields, the temperature exhibits self-similar evolution of the form $T=(\chi_{\parallel} t)^{-\gamma/2} L \left[ (\chi_{\parallel} t)^{-\gamma/2} \delta \psi \right]$, where $\delta \psi$ is a radial coordinate. In the local case, $f$ is Gaussian and the scaling is sub-diffusive, $\gamma=1/2$. In the non-local case, $f$ decays algebraically, $L (\eta) \sim \eta^{-3}$, and the scaling is diffusive, $\gamma=1$.

Analytical theory of fast magnetic reconnection with a large guide field is presented for the first time. We confirm that two distinct reconnection regimes are possible depending on whether the diffusion region thickness $\delta$ is larger or smaller than the sound gyroradius $\rho_s$. The reconnection is slow or Sweet-Parker-like for $\delta \gtrsim \rho_s$, and fast otherwise. In the fast regime, however, we find that ion viscosity $\mu$ plays a critical role. In particular, for $\delta < \rho_s$ the diffusion region thickness is proportional to $Ha^{-1}$ with $Ha \propto 1/\sqrt{\eta \mu}$ the Hartmann number, and the reconnection rate is proportional to $Pr^{-1/2}$ with $Pr = \mu/\eta$ the Prandtl number and $\eta$ the resistivity. If the perpendicular ion viscosity is employed for $\mu$ the reconnection rate becomes independent of both plasma $\beta$ and collision frequencies and therefore potentially fast.

Inertial effects in nonlinear magnetic reconnection are studied within the context of 2D electron magnetohydrodynamics (EMHD) with resistive and viscous dissipation. Families of nonlinear solutions for relevant current sheet parameters are predicted and confirmed numerically in all regimes of interest. Electron inertia becomes important for current sheet thicknesses $\delta$ below the inertial length $d_{e}$. In this case, in the absence of electron viscosity, the sheet thickness experiences a nonlinear collapse. Viscosity regularizes solutions at small scales. Transition from resistive to viscous regimes shows a nontrivial dependence on resistivity and viscosity, featuring a hysteresis bifurcation. In all accessible regimes, the nonlinear reconnection rate is found to be explicitly independent of the electron inertia and dissipation coefficients., Comment: submitted to publication