73,501 research outputs found
A non-hybrid method for the PDF equations of turbulent flows on unstructured grids
In probability density function (PDF) methods of turbulent flows, the joint
PDF of several flow variables is computed by numerically integrating a system
of stochastic differential equations for Lagrangian particles. A set of
parallel algorithms is proposed to provide an efficient solution of the PDF
transport equation, modeling the joint PDF of turbulent velocity, frequency and
concentration of a passive scalar in geometrically complex configurations. An
unstructured Eulerian grid is employed to extract Eulerian statistics, to solve
for quantities represented at fixed locations of the domain (e.g. the mean
pressure) and to track particles. All three aspects regarding the grid make use
of the finite element method (FEM) employing the simplest linear FEM shape
functions. To model the small-scale mixing of the transported scalar, the
interaction by exchange with the conditional mean model is adopted. An adaptive
algorithm that computes the velocity-conditioned scalar mean is proposed that
homogenizes the statistical error over the sample space with no assumption on
the shape of the underlying velocity PDF. Compared to other hybrid
particle-in-cell approaches for the PDF equations, the current methodology is
consistent without the need for consistency conditions. The algorithm is tested
by computing the dispersion of passive scalars released from concentrated
sources in two different turbulent flows: the fully developed turbulent channel
flow and a street canyon (or cavity) flow. Algorithmic details on estimating
conditional and unconditional statistics, particle tracking and particle-number
control are presented in detail. Relevant aspects of performance and
parallelism on cache-based shared memory machines are discussed.Comment: Accepted in Journal of Computational Physics, Feb. 20, 200
Sensitivity-based multistep MPC for embedded systems
In model predictive control (MPC), an optimization problem is solved every sampling instant to determine an optimal control for a physical system. We aim to accelerate this procedure for fast systems applications and address the challenge of implementing the resulting MPC scheme on an embedded system with limited computing power. We present the sensitivity-based multistep MPC, a strategy which considerably reduces the computing requirements in terms of floating point operations (FLOPs), compared to a standard MPC formulation, while fulfilling closed- loop performance expectations. We illustrate by applying the method to a DC-DC converter model and show how a designer can optimally trade off closed-loop performance considerations with computing requirements in order to fit the controller into a resource-constrained embedded system
A Rewriting-Logic-Based Technique for Modeling Thermal Systems
This paper presents a rewriting-logic-based modeling and analysis technique
for physical systems, with focus on thermal systems. The contributions of this
paper can be summarized as follows: (i) providing a framework for modeling and
executing physical systems, where both the physical components and their
physical interactions are treated as first-class citizens; (ii) showing how
heat transfer problems in thermal systems can be modeled in Real-Time Maude;
(iii) giving the implementation in Real-Time Maude of a basic numerical
technique for executing continuous behaviors in object-oriented hybrid systems;
and (iv) illustrating these techniques with a set of incremental case studies
using realistic physical parameters, with examples of simulation and model
checking analyses.Comment: In Proceedings RTRTS 2010, arXiv:1009.398
Pegasus: A New Hybrid-Kinetic Particle-in-Cell Code for Astrophysical Plasma Dynamics
We describe Pegasus, a new hybrid-kinetic particle-in-cell code tailored for
the study of astrophysical plasma dynamics. The code incorporates an
energy-conserving particle integrator into a stable, second-order--accurate,
three-stage predictor-predictor-corrector integration algorithm. The
constrained transport method is used to enforce the divergence-free constraint
on the magnetic field. A delta-f scheme is included to facilitate a
reduced-noise study of systems in which only small departures from an initial
distribution function are anticipated. The effects of rotation and shear are
implemented through the shearing-sheet formalism with orbital advection. These
algorithms are embedded within an architecture similar to that used in the
popular astrophysical magnetohydrodynamics code Athena, one that is modular,
well-documented, easy to use, and efficiently parallelized for use on thousands
of processors. We present a series of tests in one, two, and three spatial
dimensions that demonstrate the fidelity and versatility of the code.Comment: 27 pages, 12 figures, accepted for publication in Journal of
Computational Physic
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