Experimente zur Strukturbildung in Plasmen

Since the early days of plasma physics, it is known that plasma is more than a hotgaseous state of matter. The electric charge of the plasma constituents (usually electrons and ions) allows for collective behavior and the time scales of electron and iondynamics are well separated. Thus, plasmas exhibit a rich variety of phenomena whichare unknown to ordinary gases and contribute to the compelling properties of thisstate of matter. The wealth of phenomena, their relevance for astrophysical and technological applications, and their impact on other fields have driven plasma researchfor almost a century now and have provided much insight into the formation of staticstructures, oscillatory instabilities, waves, and turbulence

Structure of the medium formed in heavy ion collisions

We investigate the structure of the medium formed in heavy ion collisions using three different models: the Color String Percolation Model (CSPM), the Core-Shell-Color String Percolation Model (CSCSPM), and the Color Glass Condensate (CGC) framework. We analyze the radial distribution function of the transverse representation of color flux tubes in each model to determine the medium's structure. Our results indicate that the CSPM behaves as an ideal gas, while the CSCSPM exhibits a structural phase transition from a gas-like to a liquid-like structure. Additionally, our analysis of the CGC framework suggests that it produces systems that behave like interacting gases for AuAu central collisions at RHIC energies and liquid-like structures for PbPb central collisions at LHC energies.Comment: 15 pages, 8 figure

Microscopic model approaches to fragmentation of nuclei and phase transitions in nuclear matter

The properties of excited nuclear matter and the quest for a phase transition which is expected to exist in this system are the subject of intensive investigations. High energy nuclear collisions between finite nuclei which lead to matter fragmentation are used to investigate these properties. The present report covers effective work done on the subject over the two last decades. The analysis of experimental data is confronted with two major problems, the setting up of thermodynamic equilibrium in a time-dependent fragmentation process and the finite size of nuclei. The present status concerning the first point is presented. Simple classical models of disordered systems are derived starting with the generic bond percolation approach. These lattice and cellular equilibrium models, like percolation approaches, describe successfully experimental fragment multiplicity distributions. They also show the properties of systems which undergo a thermodynamic phase transition. Physical observables which are devised to show the existence and to fix the order of critical behaviour are presented. Applications to the models are shown. Thermodynamic properties of finite systems undergoing critical behaviour are advantageously described in the framework of the microcanonical ensemble. Applications to the designed models and to experimental data are presented and analysed. Perspectives of further developments of the field are suggested.Comment: 150 pages including 28 figures. To be published in Phys. Rep. Corrected discussion in section 3.2.3 and new Fig.5. New caption of Fig.2

Simulation of pore-scale flow using finite element-methods

I present a new finite element (FE) simulation method to simulate pore-scale flow. Within the pore-space, I solve a simplified form of the incompressible Navier-Stoke’s equation, yielding the velocity field in a two-step solution approach. First, Poisson’s equation is solved with homogeneous boundary conditions, and then the pore pressure is computed and the velocity field obtained for no slip conditions at the grain boundaries. From the computed velocity field I estimate the effective permeability of porous media samples characterized by thin section micrographs, micro-CT scans and synthetically generated grain packings. This two-step process is much simpler than solving the full Navier Stokes equation and therefore provides the opportunity to study pore geometries with hundreds of thousands of pores in a computationally more cost effective manner than solving the full Navier-Stoke’s equation. My numerical model is verified with an analytical solution and validated on samples whose permeabilities and porosities had been measured in laboratory experiments (Akanji and Matthai, 2010). Comparisons were also made with Stokes solver, published experimental, approximate and exact permeability data. Starting with a numerically constructed synthetic grain packings, I also investigated the extent to which the details of pore micro-structure affect the hydraulic permeability (Garcia et al., 2009). I then estimate the hydraulic anisotropy of unconsolidated granular packings. With the future aim to simulate multiphase flow within the pore-space, I also compute the radii and derive capillary pressure from the Young-Laplace equation (Akanji and Matthai,2010

Modelling techniques for the study of molecular self-organisation

In this thesis we develop computational techniques for modelling molecular selforganisation. After a short review of the current nanotechnological applications of molecular self-assembly and the main problems encountered in modelling the selforganised behaviour of chemical systems, we introduce a set of methods, from both chemistry and complexity science, for the prediction of self-assembled structures, with particular focus on Monte Carlo (MC) based methods. We apply the MC method to two systems of experimental interest. First we model the silica nanoparticles on the surface of spherical polystyrene latex droplets, synthesised by the S. Bon Group at the University of Warwick, as a set of soft spheres on a spherical surface, to study their packing patterns as a function of the broadening of the nanoparticle size distribution. Then we develop a hexagonal lattice model for the study of the two-dimensional self-organisation of planar molecules capable of complementary interactions, to study their phase diagrams as a function of the strength of their complementary interactions and bonding motif. In both cases, the phases are characterised using a number of order parameters. We show that these simplified models are able to reproduce the experimental observations. We then develop an Agent Based (AB) algorithm, traditionally used for the study of complex systems, for the modelling of molecular self-organisation. In this algorithm, an agent is identified with a stable portion of the system under investigation. The agents can then evolve following a set of rules which include elements of adaptation (new configurations induce new types of moves) and learning (past successful choices are repeated), in order to drive the system towards its lowest energy configuration. We first apply the method to the study of the packing of a set of idealised shapes, then we extend it to the study of a realistic system. The latter is achieved by linking the AB algorithm to an available molecular mechanics code, in order to calculate the interaction energies of atomistic models. In both cases we compare the AB result with that of MC based methods, showing that for all the systems studied, the AB method consistently finds significantly lower energy minima than the MC algorithms in less computing time. Finally, we show how the AB algorithm can be used as a part of the protocol to calculate the phase diagram of a rigid organic molecule (1,4-benzene-dicarboxylic acid or TPA) with less computational effort than standard techniques

Towards Understanding the Self-assembly of Complicated Particles via Computation.

We develop advanced Monte Carlo sampling schemes and new methods of calculating thermodynamic partition functions that are used to study the self-assembly of complicated ``patchy '' particles. Patchy particles are characterized by their strong anisotropic interactions, which can cause critical slowing down in Monte Carlo simulations of their self-assembly. We prove that detailed balance is maintained for our implementation of Monte Carlo cluster moves that ameliorate critical slowing down and use these simulations to predict the structures self-assembled by patchy tetrominoes. We compare structures predicted from our simulations with those generated by an alternative learning-augmented Monte Carlo approach and show that the learning-augmented approach fails to sample thermodynamic ensembles. We prove one way to maintain detailed balance when parallelizing Monte Carlo using the checkerboard domain decomposition scheme by enumerating the state-to-state transitions for a simple model with general applicability. Our implementation of checkerboard Monte Carlo on graphics processing units enables accelerated sampling of thermodynamic properties and we use it to confirm the fluid-hexatic transition observed at high packing fractions of hard disks. We develop a new method, bottom-up building block assembly, which generates partition functions hierarchically. Bottom-up building block assembly provides a means to answer the question of which structures are favored at a given temperature and allows accelerated prediction of potential energy minimizing structures, which are difficult to determine with Monte Carlo methods. We show how the sequences of clusters generated by bottom-up building block assembly can be used to inform ``assembly pathway engineering'', the design of patchy particles whose assembly propensity is optimized for a target structure. The utility of bottom-up building block assembly is demonstrated for systems of CdTe/CdS tetrahedra, DNA-tethered nanospheres, colloidal analogues of patchy tetrominoes and shape-shifting particles.Ph.D.Chemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91509/1/erjank_1.pd

On the inner workings of Monte Carlo codes

We review state-of-the-art Monte Carlo (MC) techniques for computing fluid coexistence properties (Gibbs simulations) and adsorption simulations in nanoporous materials such as zeolites and metal-organic frameworks. Conventional MC is discussed and compared to advanced techniques such as reactive MC, configurational-bias Monte Carlo and continuous fractional MC. The latter technique overcomes the problem of low insertion probabilities in open systems. Other modern methods are (hyper-)parallel tempering, Wang-Landau sampling and nested sampling. Details on the techniques and acceptance rules as well as to what systems these techniques can be applied are provided. We highlight consistency tests to help validate and debug MC codes

Belle II Technical Design Report

The Belle detector at the KEKB electron-positron collider has collected almost 1 billion Y(4S) events in its decade of operation. Super-KEKB, an upgrade of KEKB is under construction, to increase the luminosity by two orders of magnitude during a three-year shutdown, with an ultimate goal of 8E35 /cm^2 /s luminosity. To exploit the increased luminosity, an upgrade of the Belle detector has been proposed. A new international collaboration Belle-II, is being formed. The Technical Design Report presents physics motivation, basic methods of the accelerator upgrade, as well as key improvements of the detector.Comment: Edited by: Z. Dole\v{z}al and S. Un