Simulations of space plasma instabilities

PhD 1997 QMThis work describes computer simulations of the behaviour of plasmas similar to those observed in the near Earth environment The work can be split into three main threads Firstly we have developed a set of algorithms to allow the implementation of particle type simulation models on parallel computer architectures ranging from small workstation clusters to massively parallel supercomputers These algorithms allow large simulations with many particles to be performed We address the problems of e cient use of available computational resources and the scaling of algorithms as computers get larger Secondly we use a parallel implementation of a two dimensional hybrid simulation code with periodic boundaries to explore the evolution of ion beam distributions similar to those observed upstream of the Earth s bow shock We follow the evolution of the resonant instabilities of these cool tenuous proton beams both isotropic and anisotropic in temperature into the non linear regime We examine the waves generated their e ects on the ion distribution function the phenomenon of gyrophase bunching and describe the life cycles of two dimensional magnetic features including oblique propagating shocklets We suggest that such two dimensional structures may play a role in the saturation of beam instabilities Coherence lengths of the waves are calculated We see some evidence of anisotropy driven mirror waves late on in these simulations Thirdly we explore the nature of parametric instabilities in two dimensions We examine the role of parametric or wave wave instabilities in the late evolution of beam instability generated waves We nd little evidence of any parametric instability in this case The two dimensional evolution of a wave known to be unstable to one dimensional parametric instability is described We nd that in this case the instability develops in a manner similar to that found in one dimensional simulations although with some angular broadening in wavevector space There is some evidence of anisotropy driven instabilities later in the simulatio

Report from the MPP Working Group to the NASA Associate Administrator for Space Science and Applications

NASA's Office of Space Science and Applications (OSSA) gave a select group of scientists the opportunity to test and implement their computational algorithms on the Massively Parallel Processor (MPP) located at Goddard Space Flight Center, beginning in late 1985. One year later, the Working Group presented its report, which addressed the following: algorithms, programming languages, architecture, programming environments, the way theory relates, and performance measured. The findings point to a number of demonstrated computational techniques for which the MPP architecture is ideally suited. For example, besides executing much faster on the MPP than on conventional computers, systolic VLSI simulation (where distances are short), lattice simulation, neural network simulation, and image problems were found to be easier to program on the MPP's architecture than on a CYBER 205 or even a VAX. The report also makes technical recommendations covering all aspects of MPP use, and recommendations concerning the future of the MPP and machines based on similar architectures, expansion of the Working Group, and study of the role of future parallel processors for space station, EOS, and the Great Observatories era

Emergent Properties of Interacting Populations of Spiking Neurons

Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations

Simple Orchestration Application Framework to Control "Burning Plasma Integrated Code"

We have developed the Simple Orchestration Application Framework (SOAF) on a grid infrastructure to control cooperative and multiple execution of simulation codes on remote computers from a client PC. SOAF enables researchers to generate a scenario of their cooperative and multiple executions by only describing a configuration file which includes the information of execution codes and file flows among them. SOAF does not need substantial modification of the simulation codes. We have applied SOAF to the "Burning Plasma Integrated Code" which consists of various plasma simulation codes. In order to predict and interpret the behavior of fusion burning plasma, it is necessary to cooperatively and concurrently execute various simulation codes to understand complex plasma phenomena with wide temporal and spatial ranges. Those codes exist on distributed heterogeneous computers located in different sites such as universities and institutes. By using SOAF, we succeeded to cooperatively and concurrently execute four plasma simulation codes without substantial modification as described in the configuration file

Fusion Simulation Project. Workshop Sponsored by the U.S. Department of Energy, Rockville, MD, May 16-18, 2007

The mission of the Fusion Simulation Project is to develop a predictive capability for the integrated modeling of magnetically confined plasmas. This FSP report adds to the previous activities that defined an approach to integrated modeling in magnetic fusion. These previous activities included a Fusion Energy Sciences Advisory Committee panel that was charged to study integrated simulation in 2002. The report of that panel [Journal of Fusion Energy 20, 135 (2001)] recommended the prompt initiation of a Fusion Simulation Project. In 2003, the Office of Fusion Energy Sciences formed a steering committee that developed a project vision, roadmap, and governance concepts [Journal of Fusion Energy 23, 1 (2004)]. The current FSP planning effort involved forty-six physicists, applied mathematicians and computer scientists, from twenty-one institutions, formed into four panels and a coordinating committee. These panels were constituted to consider: Status of Physics Components, Required Computational and Applied Mathematics Tools, Integration and Management of Code Components, and Project Structure and Management. The ideas, reported here, are the products of these panels, working together over several months and culminating in a three-day workshop in May 2007

TRAPS - The key to atmospheric nano-science

Die Bedeutung von Nanopartikeln für Prozesse in der Atmosphäre rückt zunehmend in das Interesse von Forschern. In vielen natürlichen Kondensationsprozessen sind die Kondensationskeime kleiner als 10 nm im Durchmesser. Die ist genau der Übergangsbereich vom Cluster zum Festkörper, in dem physikalische und chemische Eigenschaften größenabhängig sind und stark von Obenflächenkontaminationen und Kontakt zu anderen Oberflächen abhängen. Bisher gab es keine Möglichkeit die fundamentalen Eigenschaften von freien, massenselektierten Nanopartikeln mit definiertem Ladungszustand und Durchmessern von 3 bis 30 nm in Laborexperimenten zu untersuchen. Diese Arbeit zeigt die Notwendigkeit auf Laborexperimente zu entwickeln, die freie Nanopartikeln ohne vorherige Abscheidung auf Substraten oder Filtern zu untersuchen. Als Anwendungsbeispiel in der Atmosphärenforschung werden Eiswolken diskutiert, die sich an Nanopartikeln meteorischen Ursprunges in der Mesosphäre bilden. Mit der TRAPS Apparatur ist es nun möglich verschiedene spektroskopische Methoden an freien Nanopartikelstrahlen oder gefangenen Nanopartikelwolken anzuwenden. Massenspekrometrie, optische Extinktionsspektroskopie und Innerschalen-Photoionisationsspektroskopie werden als Anwendungsbeispiele gezeigt. Als wissenschaftliches Novum werden Experimente zur Untersuchung von Eisnukleation an Nanopartikeln und die Bestimmung von Eiswachstumsgeschwindigkeiten unter mesosphärischen Bedingungen sowie XPS Messungen an sub-10 nm SiO2 Partikeln gezeigt

The Performance Effect of Multi-core on ScientificApplications

The historical trend of increasing single CPU performancehas given way to roadmap of increasing core count. The challenge ofeffectively utilizing these multi-core chips is just starting to beexplored by vendors and application developers alike. In this study, wepresent some performance measurements of several complete scientificapplications on single and dual core Cray XT3 and XT4 systems with a viewto characterizing the effects of switching to multi-core chips. Weconsider effects within a node by using applications run at lowconcurrencies, and also effects on node-interconnect interaction usinghigher concurrency results. Finally, we construct a simple performancemodel based on the principle on-chip shared resource--memorybandwidth--and use this to predict the performance of the forthcomingquad-core system

Structural reliability and stochastic finite element methods

PurposeThis paper aims to provide a comprehensive review of uncertainty quantification methods supported by evidence-based comparison studies. Uncertainties are widely encountered in engineering practice, arising from such diverse sources as heterogeneity of materials, variability in measurement, lack of data and ambiguity in knowledge. Academia and industries have long been researching for uncertainty quantification (UQ) methods to quantitatively account for the effects of various input uncertainties on the system response. Despite the rich literature of relevant research, UQ is not an easy subject for novice researchers/practitioners, where many different methods and techniques coexist with inconsistent input/output requirements and analysis schemes.Design/methodology/approachThis confusing status significantly hampers the research progress and practical application of UQ methods in engineering. In the context of engineering analysis, the research efforts of UQ are most focused in two largely separate research fields: structural reliability analysis (SRA) and stochastic finite element method (SFEM). This paper provides a state-of-the-art review of SRA and SFEM, covering both technology and application aspects. Moreover, unlike standard survey papers that focus primarily on description and explanation, a thorough and rigorous comparative study is performed to test all UQ methods reviewed in the paper on a common set of reprehensive examples.FindingsOver 20 uncertainty quantification methods in the fields of structural reliability analysis and stochastic finite element methods are reviewed and rigorously tested on carefully designed numerical examples. They include FORM/SORM, importance sampling, subset simulation, response surface method, surrogate methods, polynomial chaos expansion, perturbation method, stochastic collocation method, etc. The review and comparison tests comment and conclude not only on accuracy and efficiency of each method but also their applicability in different types of uncertainty propagation problems.Originality/valueThe research fields of structural reliability analysis and stochastic finite element methods have largely been developed separately, although both tackle uncertainty quantification in engineering problems. For the first time, all major uncertainty quantification methods in both fields are reviewed and rigorously tested on a common set of examples. Critical opinions and concluding remarks are drawn from the rigorous comparative study, providing objective evidence-based information for further research and practical applications

Multi-input genetic algorithm for experimental optimization of the reattachment downstream of a backward-facing step with surface plasma actuator

The practical interest of flow control approaches is no more debated as flow control provides an effective mean for considerably increasing the performances of ground or air transport systems, among many others applications. Here a fundamental configuration is investigated by using non-thermal surface plasma discharge. A dielectric barrier discharge is installed at the step corner of a backward-facing step (Reh=30000, Re¿=1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step. The primary objective of this paper is the coupling of a numerical optimizer with an experiment. More specifically, optimization by genetic algorithm is implemented experimentally in order to minimize the reattachment point downstream of the step model. Validation through inverse problem is firstly demonstrated. When coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm finds the optimum forcing conditions with a good convergence rate, the best control design variables being in agreement with the literature that uses other types of control devices than plasma. Indeed, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing phenomena. At the best forcing conditions, the mean flow reattachment is reduced by 20%. This article, with its experiment-based approach, demonstrates the robustness of a single-objective multi-design optimization method, and its feasibility for wind tunnel experiments.Postprint (published version

Cray X1 Evaluation Status Report

On August 15, 2002 the Department of Energy (DOE) selected the Center for Computational Sciences (CCS) at Oak Ridge National Laboratory (ORNL) to deploy a new scalable vector supercomputer architecture for solving important scientific problems in climate, fusion, biology, nanoscale materials and astrophysics. ''This program is one of the first steps in an initiative designed to provide U.S. scientists with the computational power that is essential to 21st century scientific leadership,'' said Dr. Raymond L. Orbach, director of the department's Office of Science The Cray X1 is an attempt to incorporate the best aspects of previous Cray vector systems and massively-parallel-processing (MPP) systems into one design. Like the Cray T90, the X1 has high memory bandwidth, which is key to realizing a high percentage of theoretical peak performance. Like the Cray T3E, the X1 has a high-bandwidth, low-latency, scalable interconnect, and scalable system software. And, like the Cray SV1, the X1 leverages commodity off-the-shelf (CMOS) technology and incorporates non-traditional vector concepts, like vector caches and multi-streaming processors. In FY03, CCS procured a 256-processor Cray X1 to evaluate the processors, memory subsystem, scalability of the architecture, software environment and to predict the expected sustained performance on key DOE applications codes. The results of the micro-benchmarks and kernel benchmarks show the architecture of the Cray X1 to be exceptionally fast for most operations. The best results are shown on large problems, where it is not possible to fit the entire problem into the cache of the processors. These large problems are exactly the types of problems that are important for the DOE and ultra-scale simulation