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Register a new userVerification of a Fully Implicit Particle-in-Cell Method for the $v_parallel$ Formalism of Electromagnetic Gyrokinetics in the XGC Code
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Benjamin Sturdevant
Publication date
2021
fields
Physics
and research's language is
English
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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}res 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case is compared to results obtained from other $delta f$ gyrokinetic codes/schemes, whose verification has already been archived in the literature.
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As an alternative option to kinetic electrons, the gyrokinetic total-f particle-in-cell (PIC) code XGC1 has been extended to the MHD/fluid type electromagnetic regime by combining gyrokinetic PIC ions with massless drift-fluid electrons analogous to Chen and Parker, Physics of Plasmas 8, 441 (2001). Two representative long wavelength modes, shear Alfven waves and resistive tearing modes, are verified in cylindrical and toroidal magnetic field geometries.
The global total-$f$ gyrokinetic particle-in-cell code XGC, used to study transport in magnetic fusion plasmas, implements a continuum grid to perform the dissipative operations, such as plasma collisions. To transfer the distribution function between marker particles and a rectangular velocity-space grid, XGC employs a bilinear mapping. The conservation of particle density and momentum is accurate enough in this bilinear operation, but the error in the particle energy conservation can become undesirably large in special conditions. In the present work we update XGC to use a novel mapping technique, based on the calculation of a pseudo-inverse, to exactly preserve moments up to the order of the discretization space. We describe the details of the implementation and we demonstrate the reduced interpolation error for a neoclassical tokamak test case by using $1^{mathrm{st}}$- and $2^{mathrm{nd}}$-order elements with the pseudo-inverse method and comparing to the bilinear mapping.
In this work, an implicit scheme for particle-in-cell/Fourier electromagnetic simulations is developed and applied to studies of Alfven waves in one dimension and three-dimensional tokamak plasmas. An analytical treatment is introduced to achieve efficient convergence of the iterative solution of the implicit field-particle system. First, its application to the one-dimensional uniform plasma demonstrates its applicability in a broad range of $beta/m_e$ values. Second, toroidicity induced Alfven eigenmodes (TAE) are simulated in a three-dimensional axisymmetric tokamak plasma, using the widely studied case defined by the International Tokamak Physics Activity (ITPA) Energetic Particle (EP) Topical Group. The real frequency and the growth (or damping) rate of the TAE with (or without) EPs agree with previous results reasonably well. The full f electromagnetic particle scheme established in this work provides a possible natural choice for EP transport studies where large profile variation and arbitrary particle distribution functions need to be treated in kinetic simulations.
Large-scale simulations of plasmas are essential for advancing our understanding of fusion devices, space, and astrophysical systems. Particle-in-Cell (PIC) codes have demonstrated their success in simulating numerous plasma phenomena on HPC systems. Today, flagship supercomputers feature multiple GPUs per compute node to achieve unprecedented computing power at high power efficiency. PIC codes require new algorithm design and implementation for exploiting such accelerated platforms. In this work, we design and optimize a three-dimensional implicit PIC code, called sputniPIC, to run on a general multi-GPU compute node. We introduce a particle decomposition data layout, in contrast to domain decomposition on CPU-based implementations, to use particle batches for overlapping communication and computation on GPUs. sputniPIC also natively supports different precision representations to achieve speed up on hardware that supports reduced precision. We validate sputniPIC through the well-known GEM challenge and provide performance analysis. We test sputniPIC on three multi-GPU platforms and report a 200-800x performance improvement with respect to the sputniPIC CPU OpenMP version performance. We show that reduced precision could further improve performance by 45% to 80% on the three platforms. Because of these performance improvements, on a single node with multiple GPUs, sputniPIC enables large-scale three-dimensional PIC simulations that were only possible using clusters.
We design and develop a new Particle-in-Cell (PIC) method for plasma simulations using Deep-Learning (DL) to calculate the electric field from the electron phase space. We train a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) to solve the two-stream instability test. We verify that the DL-based MLP PIC method produces the correct results using the two-stream instability: the DL-based PIC provides the expected growth rate of the two-stream instability. The DL-based PIC does not conserve the total energy and momentum. However, the DL-based PIC method is stable against the cold-beam instability, affecting traditional PIC methods. This work shows that integrating DL technologies into traditional computational methods is a viable approach for developing next-generation PIC algorithms.
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