EECE695: Computer Simulation (2005) Particle-in-Cell Techniques HyunChul Kim and J.K. Lee Plasma Application Modeling Group, POSTECH References: Minicourse.

Slides:



Advertisements
Similar presentations
Introduction to RF for Accelerators
Advertisements

4 th order Embedded Boundary FDTD algorithm for Maxwell Equations Lingling Wu, Stony Brook University Roman Samulyak, BNL Tianshi Lu, BNL Application collaborators:
EMLAB 1 Introduction to EM theory 1. EMLAB 2 Electromagnetic phenomena The globe lights up due to the work done by electric current (moving charges).
PyECLOUD G. Iadarola, G. Rumolo Thanks to: F. Zimmermann, G. Arduini, H. Bartosik, C. Bhat, O. Dominguez, M. Driss Mensi, E. Metral, M. Taborelli.
ELEN 3371 Electromagnetics Fall Lecture 6: Maxwell’s Equations Instructor: Dr. Gleb V. Tcheslavski Contact: Office.
ECE490O: Special Topics in EM-Plasma Simulations JK LEE (Spring, 2006) ODE Solvers PIC-MCC PDE Solvers (FEM and FDM) Linear & NL Eq. Solvers.
5/4/2015rew Accuracy increase in FDTD using two sets of staggered grids E. Shcherbakov May 9, 2006.
Modeling laser-plasma interaction with the direct implicit PIC method 7 th Direct Drive and Fast Ignition Workshop, Prague, 3-6 May 2009 M. Drouin a, L.
Plasma Application Modeling, POSTECH
EEE 498/598 Overview of Electrical Engineering
INTRODUCTION OF WAVE-PARTICLE RESONANCE IN TOKAMAKS J.Q. Dong Southwestern Institute of Physics Chengdu, China International School on Plasma Turbulence.
EEE340Lecture 151 Per unit length capacitance. EEE340Lecture Multi-conductor Systems This section is very useful in high speed electronics.
Computational Modeling Capabilities for Neutral Gas Injection Wayne Scales and Joseph Wang Virginia Tech Center for Space Science and Engineering.
1 Particle-In-Cell Monte Carlo simulations of a radiation driven plasma Marc van der Velden, Wouter Brok, Vadim Banine, Joost van der Mullen, Gerrit Kroesen.
Iain D. Boyd University of Michigan Modeling of Ion Sputtering and Product Transport.
WAVE AND ELECTROSTATIC COUPLING IN 2-FREQUENCY CAPACITIVELY COUPLED PLASMAS UTILIZING A FULL MAXWELL SOLVER* Yang Yang a) and Mark J. Kushner b) a) Department.
Two Approaches to Multiphysics Modeling Sun, Yongqi FAU Erlangen-Nürnberg.
An Introduction to Breakdown Simulations With PIC Codes C. Nieter, S.A. Veitzer, S. Mahalingam, P. Stoltz Tech-X Corporation MTA RF Workshop 2008 Particle-in-Cell.
LESSON 4 METO 621. The extinction law Consider a small element of an absorbing medium, ds, within the total medium s.
MCE 561 Computational Methods in Solid Mechanics
VORPAL for Simulating RF Breakdown Kevin Paul VORPAL is a massively-parallel, fully electromagnetic particle- in-cell (PIC) code, originally.
Monte Carlo Methods in Partial Differential Equations.
Principles of the Global Positioning System Lecture 10 Prof. Thomas Herring Room A;
Physics of fusion power Lecture 2: Lawson criterion / Approaches to fusion.
Simulation of streamer propagation using a PIC-MCC code. Application to Sprite discharges. Olivier Chanrion and Torsten Neubert Danish National Space Center.
Lecture 12 Monte Carlo Simulations Useful web sites:
SIMULATION PROGRESS AND PLANS AT ROSTOCK/DESY Aleksandar Markovic ECL2, CERN, March 1, 2007 Gisela Pöplau.
Refractive index dispersion and Drude model Optics, Eugene Hecht, Chpt. 3.
Chapter 7 Electrodynamics
Chapter 5 Diffusion and resistivity
Computational Plasma Physics Kinetic modelling: Part 2 W.J. Goedheer FOM-Instituut voor Plasmafysica Nieuwegein,
Usually a diluted salt solution chemical decomposition
Introduction to Monte Carlo Simulation. What is a Monte Carlo simulation? In a Monte Carlo simulation we attempt to follow the `time dependence’ of a.
Lecture #1 OUTLINE Course overview Circuit Analysis.
-Global Illumination Techniques
Introduction to the Particle In Cell Scheme for Gyrokinetic Plasma Simulation in Tokamak a Korea National Fusion Research Institute b Courant Institute,
Physics for Bioscience (Part II) Electricity Magnetism Waves Sound Optics by Dr. Chittakorn polyon Department of Physics, Faculty of Science,
ELEC 3105 Basic EM and Power Engineering Conductivity / Resistivity Current Flow Resistance Capacitance Boundary conditions.
A 3D tracking algorithm for bunches in beam pipes with elliptical cross-section and a concept for simulation of the interaction with an e-cloud Aleksandar.
Physics II: Electricity & Magnetism Sections 23.1 to 23.9.
The propagation of a microwave in an atmospheric pressure plasma layer: 1 and 2 dimensional numerical solutions Conference on Computation Physics-2006.
Rieben IMA Poster, 05/11/ UC Davis /LLNL/ ISCR High Order Symplectic Integration Methods for Finite Element Solutions to Time Dependent Maxwell Equations.
Multi-beams simulation in PIC1D Hands-on section 4.
Lesson 4: Computer method overview
INFSO-RI Enabling Grids for E-sciencE Workflows in Fusion applications José Luis Vázquez-Poletti Universidad.
Lesson 6: Computer method overview  Neutron transport overviews  Comparison of deterministic vs. Monte Carlo  User-level knowledge of Monte Carlo 
Electromagnetism Zhu Jiongming Department of Physics Shanghai Teachers University.
Physics of electron cloud build up Principle of the multi-bunch multipacting. No need to be on resonance, wide ranges of parameters allow for the electron.
1 EEE 431 Computational Methods in Electrodynamics Lecture 7 By Dr. Rasime Uyguroglu
Waves in Plasma Very short course.
CMS HIP Plasma-Wall Interactions – Part II: In Linear Colliders Helga Timkó Department of Physics University of Helsinki Finland.
Warp LBNL Warp suite of simulation codes: developed to study high current ion beams (heavy-ion driven inertial confinement fusion). High.
The Heavy Ion Fusion Virtual National Laboratory Asymmetric PML for the Absorption of Waves. Application to Mesh Refinement in Electromagnetic Particle-In-Cell.
Global Illumination (3) Path Tracing. Overview Light Transport Notation Path Tracing Photon Mapping.
ELEN 340 Electromagnetics II Lecture 2: Introduction to Electromagnetic Fields; Maxwell’s Equations; Electromagnetic Fields in Materials; Phasor Concepts;
______ APPLICATION TO WAKEFIELD ACCELERATORS EAAC Workshop – Elba – June juillet 2016 | PAGE 1 CEA | 10 AVRIL 2012 X. Davoine 1, R. Lehe 2, A.
Unstructured Meshing Tools for Fusion Plasma Simulations
Lecture 6: Maxwell’s Equations
Computational Methods for Kinetic Processes in Plasma Physics
ELEC 3105 Basic EM and Power Engineering
University of California, Los Angeles
DOE Plasma Science Center Control of Plasma Kinetics
Computational Methods for Kinetic Processes in Plasma Physics
Maxwell’s Equations.
Office: Science Bldg, Rm 137
Introduction Motivation Objective
Coulomb’s Law Charges with the same sign repel each other, and charges with opposite signs attract each other. The electrostatic force between two particles.
UPB / ETTI O.DROSU Electrical Engineering 2
Physics 1 Electric current Ing. Jaroslav Jíra, CSc.
Presentation transcript:

EECE695: Computer Simulation (2005) Particle-in-Cell Techniques HyunChul Kim and J.K. Lee Plasma Application Modeling Group, POSTECH References: Minicourse by Dr. J. P. Verboncoeur (PTS Group of UC Berkeley) in IEEE International Conference on Plasma Science (2002) “Plasma Physics via Computer Simulation” by C.K. Birdsall and A.B. Langdon (Adam Hilger, 1991)

PIC Overview  Applications of PIC model Basic plasma physics: waves and instabilities Magnetic fusion Gaseous discharges Electron and ion optics Microwave-beam devices Plasma-filled microwave-beam devices

PIC Overview PIC codes simulate plasma behavior of a large number of charges particles using a few representative “super particles”. These type of codes solve the Newton-Lorentz equation of motion to move particles in conjunction with Maxwell’s equations (or a subset). Boundary conditions are applied to the particles and the fields to solve the set of equations. PIC codes are quite successful in simulating kinetic and nonlinear plasma phenomenon like ECR, stochastic heating, etc.  PIC Codes Overview

PIC-MCC Flow Chart Fig: Flow chart for an explicit PIC-MCC scheme Particles in continuum space Fields at discrete mesh locations in space Coupling between particles and fields III IIIIV V

I. Particle Equations of Motion  Newton-Lorentz equations of motion  In finite difference form, the leapfrog method Fig: Schematic leapfrog integration

I. Particle Equations of Motion Second order accurate Requires minimal storage Requires few operations Stable for

I. Particle Equations of Motion Boris algorithm

I. Particle Equations of Motion Finally,

II. Particle Boundary Conductor : absorb charge, add to the global σ Dielectric : deposit charge, weight q locally to mesh  Absorption  Reflection Physical reflection Specular reflection  1 st order error  Thermionic Emission  Fowler-Nordheim Field Emission  Child’s Law Field Emission  Gauss’s Law Field Emission

II. Particle Boundary  Secondary electron emission + –, – Ion impact secondary emission Electron impact secondary emission  Important in processes related to high-power microwave sources Photoemission

III. Electrostatic Field Model  Possion’s equation Finite difference form in 1D planar geometry  Boundary condition : External circuit Fig: Schematic one-dimensional bounded plasma with external circuit

III. Electrostatic Field Model Short circuit Open circuit Voltage driven series RLC circuit From Kirchhoff’s voltage law, From Gauss’s law,

IV. Coupling Fields to Particles  Particle and force weighting : connection between grid and particle quantities Weighting of charge to grid Weighting of fields to particles a point charge grid point

IV. Coupling Fields to Particles Nearest grid point (NGP) weighting  fast, simple bc, noisy Linear weighting : particle-in-cell (PIC) or cloud-in-cell (CIC)  relatively fast, simple bc, less noisy Higher order weighting schemes  slow, complicated bc, low noisy NGP Linear spline Quadratic spline Cubic spline Fig: Density distribution function of a particle at for various weightings in 1D Position (x)

IV. Coupling Fields to Particles Fig: Charge assignment for linear weighting in 2D Areas are assigned to grid points; i.e., area a to grid point A, b to B, etc

V. Monte-Carlo Collision Model The MCC model statistically describes the collision processes, using cross sections for each reaction of interest. Probability of a collision event For a pure Monte Carlo method, the timestep is chosen as the time interval between collisions. where 0< R< 1is a uniformly distributed random number. However, this method can only be applied when space charge and self-field effects can be neglected.

V. Monte-Carlo Collision Model There is a finite probability that the i-th particle will undergo more than one collision in the timestep. Thus, the total number of missed collisions (error in single-event codes) Hence, traditional PIC-MCC codes are constrained by for accuracy.

V. Monte-Carlo Collision Model Computing the collision probability for each particle each timestep is computationally expensive. → Null collision method 1. The fraction of particles undergoing a collision each time step is given by 3. The type of collisions for each particle is determined by choosing a random number, 2. The particles undergoing collisions are chosen at random from the particle list. Fig: Summed collision frequencies for the null collision method. Null collision Collision type 3 Collision type 1 Collision type 2