PPT-Simulation of Particle-Material Interactions:

2 Advanced Implementation in the MonteCarlo Codes Nikolai Mokhov CERN Accelerator School Thessaloniki Greece November 1123 2018 This Course at CASThessaloniki Nov 2018 Nov 20 Simulation of ParticleMaterial Interactions. Deposition. F.Velotti. , . C.Bracco. , . B.Goddard. , . M.Meddahi. Outline . Initial parameters. Particle distribution. Kicker waveforms. Simulation for LHC beam. Simulation for HL-LHC beam. Conclusions and next steps. Physical simulation of granular materials . for facilitating artist interaction. Nafees. Ahmed. Sand animation is a performance art technique in which . an artist . tells stories by creating animated images with . Motivation. Control simulated humanoid. Various movements, environment. Without any pre-computation, motion capture data. At real time. Simulation Model. : State ( pose and velocity ). : Control. ( Desire joint angle ). Dieter Jaksch. Outline. Lecture 1: Introduction. What defines a quantum simulator? Quantum simulator criteria. Strongly correlated quantum systems.. Lecture 2: Optical lattices. Bose-Einstein condensation, adiabatic loading of an optical lattice. J1 Particles and Interactions. Particle Physics. Description and classification. State what is meant by an elementary particle (no internal structure). Identify elementary particles (quarks, leptons and exchange particles, Higgs?). Dieter Jaksch. Outline. Lecture 1: Introduction. What defines a quantum simulator? Quantum simulator criteria. Strongly correlated quantum systems.. Lecture 2: Optical lattices. Bose-Einstein condensation, adiabatic loading of an optical lattice. Hamiltonian . Molecular Simluation. Introduction:. Prerequisition: . A powerful computer, fast graphics card,. An efficient way to solve Schroedingers wave equation for many atoms,. A reliable structure or structural model. dynamics. James P. Sethna, Center for Bright Beams . Cornell University. Bunch trains. . Linear stability theory; Differential delay . eqns. ; Pseudospectra?. Particle interactions in a bunch: large-N, continuum vs. discrete . E. Samaridou. 1. , V. Bourganis. 1. , T. Karamanidou. 1. , K. Karidi. 2. ,. O. Kammona. 2. , C. Kiparissides. 1,2. . 1. Department of Chemical Engineering, AUTh. 2. Chemical Process & Energy Resources Institute, CERTH. E. Samaridou. 1. , V. Bourganis. 1. , T. Karamanidou. 1. , K. Karidi. 2. ,. O. Kammona. 2. , C. Kiparissides. 1,2. . 1. Department of Chemical Engineering, AUTh. 2. Chemical Process & Energy Resources Institute, CERTH. in a Developing Turbulence. . Shuichi M. ATSUKIYO. . ESST Kyushu Univ.. Collaborator : T. . Hada. Outline . Background. -- motivation. -- acceleration processes in turbulent plasmas. -- parametric instability (PI). Albert Ryou. Brian Walsh. Mariner 10, 1974-5. Missions to Mercury. MESSENGER, 2008-present. BepiColombo. , 2018. Objective: What can we learn about the electrons around Mercury from kinetic particle modeling?. Unit 1. By: Sarmad Rizvi. Introduction. General Perception of Chemical Engineering is to, “convert or separate raw materials into useful products.” How?. Some processes and industries where chemical engineering is essential:. Presented By. Dr. Laxmikant R Zawar. [Department of Pharmaceutics]. 1. Department of . Pharmaceutics. CONTENTS. . INTRODUCTION. CLASSIFICATION. TECHNIQUES. ROTARY CUTTER MILL. MORTAR AND PESTLE . FLUID ENERGY MILL.