6,953 research outputs found
Time-Dependent 2-D Vector Field Topology: An Approach Inspired by Lagrangian Coherent Structures
This paper presents an approach to a time-dependent variant of the concept of
vector field topology for 2-D vector fields. Vector field topology is defined
for steady vector fields and aims at discriminating the domain of a vector
field into regions of qualitatively different behaviour. The presented approach
represents a generalization for saddle-type critical points and their
separatrices to unsteady vector fields based on generalized streak lines, with
the classical vector field topology as its special case for steady vector
fields. The concept is closely related to that of Lagrangian coherent
structures obtained as ridges in the finite-time Lyapunov exponent field. The
proposed approach is evaluated on both 2-D time-dependent synthetic and vector
fields from computational fluid dynamics
Segregated Runge–Kutta time integration of convection-stabilized mixed finite element schemes for wall-unresolved LES of incompressible flows
In this work, we develop a high-performance numerical framework for the large eddy simulation (LES) of incompressible flows. The spatial discretization of the nonlinear system is carried out using mixed finite element (FE) schemes supplemented with symmetric projection stabilization of the convective term and a penalty term for the divergence constraint. These additional terms introduced at the discrete level have been proved to act as implicit LES models. In order to perform meaningful wall-unresolved simulations, we consider a weak imposition of the boundary conditions using a Nitsche’s-type scheme, where the tangential component penalty term is designed to act as a wall law. Next, segregated Runge–Kutta (SRK) schemes (recently proposed by the authors for laminar flow problems) are applied to the LES simulation of turbulent flows. By the introduction of a penalty term on the trace of the acceleration, these methods exhibit excellent stability properties for both implicit and explicit treatment of the convective terms. SRK schemes are excellent for large-scale simulations, since they reduce the computational cost of the linear system solves by splitting velocity and pressure computations at the time integration level, leading to two uncoupled systems. The pressure system is a Darcy-type problem that can easily be preconditioned using a traditional block-preconditioning scheme that only requires a Poisson solver. At the end, only coercive systems have to be solved, which can be effectively preconditioned by multilevel domain decomposition schemes, which are both optimal and scalable. The framework is applied to the Taylor–Green and turbulent channel flow benchmarks in order to prove the accuracy of the convection-stabilized mixed FEs as LES models and SRK time integrators. The scalability of the preconditioning techniques (in space only) has also been proven for one step of the SRK scheme for the Taylor–Green flow using uniform meshes. Moreover, a turbulent flow around a NACA profile is solved to show the applicability of the proposed algorithms for a realistic problem.Peer ReviewedPostprint (author's final draft
Large-Eddy Simulations of Flow and Heat Transfer in Complex Three-Dimensional Multilouvered Fins
The paper describes the computational procedure and
results from large-eddy simulations in a complex three-dimensional
louver geometry. The three-dimensionality in the
louver geometry occurs along the height of the fin, where the
angled louver transitions to the flat landing and joins with the
tube surface. The transition region is characterized by a swept
leading edge and decreasing flow area between louvers.
Preliminary results show a high energy compact vortex jet
forming in this region. The jet forms in the vicinity of the louver
junction with the flat landing and is drawn under the louver in
the transition region. Its interaction with the surface of the
louver produces vorticity of the opposite sign, which aids in
augmenting heat transfer on the louver surface. The top surface
of the louver in the transition region experiences large velocities
in the vicinity of the surface and exhibits higher heat transfer
coefficients than the bottom surface.Air Conditioning and Refrigeration Project 9
Recommended from our members
Surface-based flow visualization
This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/computers-and-graphics/.With increasing computing power, it is possible to process more complex fluid simulations. However, a gap between increasing\ud
data size and our ability to visualize them still remains. Despite the great amount of progress that has been made in the field of\ud
flow visualization over the last two decades, a number of challenges remain. Whilst the visualization of 2D flow has many good\ud
solutions, the visualization of 3D flow still poses many problems. Challenges such as domain coverage, speed of computation, and\ud
perception remain key directions for further research. Flow visualization with a focus on surface-based techniques forms the basis\ud
of this literature survey, including surface construction techniques and visualization methods applied to surfaces. We detail our\ud
investigation into these algorithms with discussions of their applicability and their relative strengths and drawbacks. We review the\ud
most important challenges when considering such visualizations. The result is an up-to-date overview of the current state-of-the-art\ud
that highlights both solved and unsolved problems in this rapidly evolving branch of research
Doctor of Philosophy
dissertationStochastic methods, dense free-form mapping, atlas construction, and total variation are examples of advanced image processing techniques which are robust but computationally demanding. These algorithms often require a large amount of computational power as well as massive memory bandwidth. These requirements used to be ful lled only by supercomputers. The development of heterogeneous parallel subsystems and computation-specialized devices such as Graphic Processing Units (GPUs) has brought the requisite power to commodity hardware, opening up opportunities for scientists to experiment and evaluate the in uence of these techniques on their research and practical applications. However, harnessing the processing power from modern hardware is challenging. The di fferences between multicore parallel processing systems and conventional models are signi ficant, often requiring algorithms and data structures to be redesigned signi ficantly for efficiency. It also demands in-depth knowledge about modern hardware architectures to optimize these implementations, sometimes on a per-architecture basis. The goal of this dissertation is to introduce a solution for this problem based on a 3D image processing framework, using high performance APIs at the core level to utilize parallel processing power of the GPUs. The design of the framework facilitates an efficient application development process, which does not require scientists to have extensive knowledge about GPU systems, and encourages them to harness this power to solve their computationally challenging problems. To present the development of this framework, four main problems are described, and the solutions are discussed and evaluated: (1) essential components of a general 3D image processing library: data structures and algorithms, as well as how to implement these building blocks on the GPU architecture for optimal performance; (2) an implementation of unbiased atlas construction algorithms|an illustration of how to solve a highly complex and computationally expensive algorithm using this framework; (3) an extension of the framework to account for geometry descriptors to solve registration challenges with large scale shape changes and high intensity-contrast di fferences; and (4) an out-of-core streaming model, which enables developers to implement multi-image processing techniques on commodity hardware
Towards optimal advection using stretch-maximizing stream surfaces
We investigate a class of stream surfaces that expand in time as much as possible. Given a vector field, we look for seed curves that locally propagate in time in a stretch-maximizing manner, i.e., curves that infinitesimally expand most progressively. We show that such a curve is generically unique at every point in an incompressible flow and offers a very good initial guess for a stretch-maximizing stream surface. With the application of efficient fluid advection-diffusion in mind, we optimize fluid injection towards optimal advection and show several examples on benchmark datasets
Kassiopeia: A Modern, Extensible C++ Particle Tracking Package
The Kassiopeia particle tracking framework is an object-oriented software
package using modern C++ techniques, written originally to meet the needs of
the KATRIN collaboration. Kassiopeia features a new algorithmic paradigm for
particle tracking simulations which targets experiments containing complex
geometries and electromagnetic fields, with high priority put on calculation
efficiency, customizability, extensibility, and ease of use for novice
programmers. To solve Kassiopeia's target physics problem the software is
capable of simulating particle trajectories governed by arbitrarily complex
differential equations of motion, continuous physics processes that may in part
be modeled as terms perturbing that equation of motion, stochastic processes
that occur in flight such as bulk scattering and decay, and stochastic surface
processes occuring at interfaces, including transmission and reflection
effects. This entire set of computations takes place against the backdrop of a
rich geometry package which serves a variety of roles, including initialization
of electromagnetic field simulations and the support of state-dependent
algorithm-swapping and behavioral changes as a particle's state evolves. Thanks
to the very general approach taken by Kassiopeia it can be used by other
experiments facing similar challenges when calculating particle trajectories in
electromagnetic fields. It is publicly available at
https://github.com/KATRIN-Experiment/Kassiopei
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