Gyro-average method for global gyrokinetic particle simulation in realistic tokamak geometry

Gyro-average is a crucial operation to capture the essential finite Larmor radius effect (FLR) in gyrokinetic simulation. In order to simulate strongly shaped plasmas, an innovative multi-point average method based on non-orthogonal coordinates has been developed to improve the accuracy of the original multi-point average method in gyrokinetic particle simulation. This new gyro-average method has been implemented in the gyrokinetic toroidal code (GTC). Benchmarks have been carried out to prove the accuracy of this new method. In the limit of concircular tokamak, ion temperature gradient (ITG) instability is accurately recovered for this new method and consistency is achieved. The new gyro-average method is also used to solve the gyrokinetic Poisson equation, and its correctness has been confirmed in the long wavelength limit for realistic shaped plasmas. The improved GTC code with the new gyro-average method has been used to investigate the ITG instability with EAST magnetic geometry. The simulation results show that the correction induced by this new method in the linear growth rate is more significant for short wavelength modes where the finite Larmor radius (FLR) effect becomes important. Due to its simplicity and accuracy, this new gyro-average method can find broader applications in simulating the shaped plasmas in realistic tokamaks.

Particle Simulation of The Strong Magnetic Field Effect On Dust Particle Charging Process

A particle-in-cell simulation is modeled and run on a dusty plasma to determine the effect of the magnetic field on the process dust-particle charging through electron-ion plasma. The electric field is solved through the Poisson equation, and the electron-neutral elastic scattering, excitation, and ionization processes are modeled through Monte Carlo collision method. The effects obderved from the initial density of the plasma, the initial temperature of the electrons, and the changing magnetic field are included in this simulation model. In the dust particle charging process, saturation time and saturation charge are compared. An increase in the magnetic field cannot reduce time to reach the saturation state. Determinig the magnetic field boundaries which depend on the physical properties of the plasma, which can be contributive in some areas of dusty(complex) plasma. The applications of the results obtaind here for fusion plasma conditions and space and laboratory plasmas are discussed. The results here can be applied in future simulation models with a focus on the dust particle movement and their effect on plasma, leading to the modeling of different astrophysical plasmas thorough laboratory experiments.

Role of wave-particle resonance in turbulent transport in toroidal plasmas

The clear understanding of the wave-particle interaction and associated transport mechanism of different particle species in the drift wave instabilities is important for accurate modeling and predictions of the plasma confinement properties. Our global gyrokinetic simulations find that electron particle and heat transport decreases to a very low level, while ion heat transport level has no dramatic change when wave-particle resonance is suppressed in the collisionless trapped electron mode (CTEM) turbulence in the tokamak core. Similarly, ion heat transport in the self-consistent ion temperature gradient (ITG) turbulence simulation is qualitatively similar to that in the test-particle simulation using the static ITG turbulence fields. These simulation results show that electron transport is primarily driven by the wave-particle resonance in the CTEM turbulence, but the ion transport is mostly driven by the nonlinear wave-particle scattering in both the CTEM and ITG turbulence.

Benchmarking LHC background particle simulation with the CMS triple-GEM detector

In 2018, a system of large-size triple-GEM demonstrator chambers was installed in the CMS experiment at CERN's Large Hadron Collider (LHC). The demonstrator's design mimicks that of the final detector, installed for Run-3. A successful Monte Carlo (MC) simulation of the collision-induced background hit rate in this system in proton-proton collisions at 13 TeV is presented. The MC predictions are compared to CMS measurements recorded at an instantaneous luminosity of 1.5 ×1034 cm-2 s-1. The simulation framework uses a combination of the FLUKA and GEANT4 packages. FLUKA simulates the radiation environment around the GE1/1 chambers. The particle flux by FLUKA covers energy spectra ranging from 10-11 to 104 MeV for neutrons, 10-3 to 104 MeV for γ's, 10-2 to 104 MeV for e±, and 10-1 to 104 MeV for charged hadrons. GEANT4 provides an estimate of the detector response (sensitivity) based on an accurate description of the detector geometry, the material composition, and the interaction of particles with the detector layers. The detector hit rate, as obtained from the simulation using FLUKA and GEANT4, is estimated as a function of the perpendicular distance from the beam line and agrees with data within the assigned uncertainties in the range 13.7-14.5%. This simulation framework can be used to obtain a reliable estimate of the background rates expected at the High Luminosity LHC.

Ion Dynamics in the Meso-scale 3-D Kelvin–Helmholtz Instability: Perspectives From Test Particle Simulations

Over three decades of in-situ observations illustrate that the Kelvin–Helmholtz (KH) instability driven by the sheared flow between the magnetosheath and magnetospheric plasma often occurs on the magnetopause of Earth and other planets under various interplanetary magnetic field (IMF) conditions. It has been well demonstrated that the KH instability plays an important role for energy, momentum, and mass transport during the solar-wind-magnetosphere coupling process. Particularly, the KH instability is an important mechanism to trigger secondary small scale (i.e., often kinetic-scale) physical processes, such as magnetic reconnection, kinetic Alfvén waves, ion-acoustic waves, and turbulence, providing the bridge for the coupling of cross scale physical processes. From the simulation perspective, to fully investigate the role of the KH instability on the cross-scale process requires a numerical modeling that can describe the physical scales from a few Earth radii to a few ion (even electron) inertial lengths in three dimensions, which is often computationally expensive. Thus, different simulation methods are required to explore physical processes on different length scales, and cross validate the physical processes which occur on the overlapping length scales. Test particle simulation provides such a bridge to connect the MHD scale to the kinetic scale. This study applies different test particle approaches and cross validates the different results against one another to investigate the behavior of different ion species (i.e., H+ and O+), which include particle distributions, mixing and heating. It shows that the ion transport rate is about 1025 particles/s, and mixing diffusion coefficient is about 1010 m2 s−1 regardless of the ion species. Magnetic field lines change their topology via the magnetic reconnection process driven by the three-dimensional KH instability, connecting two flux tubes with different temperature, which eventually causes anisotropic temperature in the newly reconnected flux.

Bridge Resistance Updating Based on the General Particle Simulation Algorithms of Complex Bayesian Formulas

The existing bridges are subjected to time-variant loading and resistance degradation processes. How to update resistance probability distribution functions with resistance degradation model and proof load effects has become one of the research hotspots in bridge engineering field. To solve with the above issue, this paper proposed the general particle simulation algorithms of complex Bayesian formulas for bridge resistance updating. Firstly, the complex Bayesian formulas for updating resistance probability model are built. For overcoming the difficulty for the analytic calculation of complex Bayesian formulas, the general particle simulation methods are provided to obtain the particles of complex Bayesian formulas; then, with the improved expectation maximization optimization algorithm obtained with the combination of K-MEANS algorithm and Expectation Maximization (EM) algorithm, the above simulated particles can be used to estimate the posteriori probability density functions of resistance probability model; finally, a numerical example is provided to illustrate the feasibility and application of the proposed algorithms.