28,582 research outputs found
Entropic lattice Boltzmann methods
We present a general methodology for constructing lattice Boltzmann models of
hydrodynamics with certain desired features of statistical physics and kinetic
theory. We show how a methodology of linear programming theory, known as
Fourier-Motzkin elimination, provides an important tool for visualizing the
state space of lattice Boltzmann algorithms that conserve a given set of
moments of the distribution function. We show how such models can be endowed
with a Lyapunov functional, analogous to Boltzmann's H, resulting in
unconditional numerical stability. Using the Chapman-Enskog analysis and
numerical simulation, we demonstrate that such entropically stabilized lattice
Boltzmann algorithms, while fully explicit and perfectly conservative, may
achieve remarkably low values for transport coefficients, such as viscosity.
Indeed, the lowest such attainable values are limited only by considerations of
accuracy, rather than stability. The method thus holds promise for
high-Reynolds number simulations of the Navier-Stokes equations.Comment: 54 pages, 16 figures. Proc. R. Soc. London A (in press
Towards the Design of Heuristics by Means of Self-Assembly
The current investigations on hyper-heuristics design have sprung up in two
different flavours: heuristics that choose heuristics and heuristics that
generate heuristics. In the latter, the goal is to develop a problem-domain
independent strategy to automatically generate a good performing heuristic for
the problem at hand. This can be done, for example, by automatically selecting
and combining different low-level heuristics into a problem specific and
effective strategy. Hyper-heuristics raise the level of generality on automated
problem solving by attempting to select and/or generate tailored heuristics for
the problem at hand. Some approaches like genetic programming have been
proposed for this. In this paper, we explore an elegant nature-inspired
alternative based on self-assembly construction processes, in which structures
emerge out of local interactions between autonomous components. This idea
arises from previous works in which computational models of self-assembly were
subject to evolutionary design in order to perform the automatic construction
of user-defined structures. Then, the aim of this paper is to present a novel
methodology for the automated design of heuristics by means of self-assembly
A Verified Information-Flow Architecture
SAFE is a clean-slate design for a highly secure computer system, with
pervasive mechanisms for tracking and limiting information flows. At the lowest
level, the SAFE hardware supports fine-grained programmable tags, with
efficient and flexible propagation and combination of tags as instructions are
executed. The operating system virtualizes these generic facilities to present
an information-flow abstract machine that allows user programs to label
sensitive data with rich confidentiality policies. We present a formal,
machine-checked model of the key hardware and software mechanisms used to
dynamically control information flow in SAFE and an end-to-end proof of
noninterference for this model.
We use a refinement proof methodology to propagate the noninterference
property of the abstract machine down to the concrete machine level. We use an
intermediate layer in the refinement chain that factors out the details of the
information-flow control policy and devise a code generator for compiling such
information-flow policies into low-level monitor code. Finally, we verify the
correctness of this generator using a dedicated Hoare logic that abstracts from
low-level machine instructions into a reusable set of verified structured code
generators
A Programming Environment Evaluation Methodology for Object-Oriented Systems
The object-oriented design strategy as both a problem decomposition and system development paradigm has made impressive inroads into the various areas of the computing sciences. Substantial development productivity improvements have been demonstrated in areas ranging from artificial intelligence to user interface design. However, there has been very little progress in the formal characterization of these productivity improvements and in the identification of the underlying cognitive mechanisms. The development and validation of models and metrics of this sort require large amounts of systematically-gathered structural and productivity data. There has, however, been a notable lack of systematically-gathered information on these development environments. A large part of this problem is attributable to the lack of a systematic programming environment evaluation methodology that is appropriate to the evaluation of object-oriented systems
kmos: A lattice kinetic Monte Carlo framework
Kinetic Monte Carlo (kMC) simulations have emerged as a key tool for
microkinetic modeling in heterogeneous catalysis and other materials
applications. Systems, where site-specificity of all elementary reactions
allows a mapping onto a lattice of discrete active sites, can be addressed
within the particularly efficient lattice kMC approach. To this end we describe
the versatile kmos software package, which offers a most user-friendly
implementation, execution, and evaluation of lattice kMC models of arbitrary
complexity in one- to three-dimensional lattice systems, involving multiple
active sites in periodic or aperiodic arrangements, as well as site-resolved
pairwise and higher-order lateral interactions. Conceptually, kmos achieves a
maximum runtime performance which is essentially independent of lattice size by
generating code for the efficiency-determining local update of available events
that is optimized for a defined kMC model. For this model definition and the
control of all runtime and evaluation aspects kmos offers a high-level
application programming interface. Usage proceeds interactively, via scripts,
or a graphical user interface, which visualizes the model geometry, the lattice
occupations and rates of selected elementary reactions, while allowing
on-the-fly changes of simulation parameters. We demonstrate the performance and
scaling of kmos with the application to kMC models for surface catalytic
processes, where for given operation conditions (temperature and partial
pressures of all reactants) central simulation outcomes are catalytic activity
and selectivities, surface composition, and mechanistic insight into the
occurrence of individual elementary processes in the reaction network.Comment: 21 pages, 12 figure
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