1,289 research outputs found
The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows
The Gauss--Newton with approximated tensors (GNAT) method is a nonlinear
model reduction method that operates on fully discretized computational models.
It achieves dimension reduction by a Petrov--Galerkin projection associated
with residual minimization; it delivers computational efficency by a
hyper-reduction procedure based on the `gappy POD' technique. Originally
presented in Ref. [1], where it was applied to implicit nonlinear
structural-dynamics models, this method is further developed here and applied
to the solution of a benchmark turbulent viscous flow problem. To begin, this
paper develops global state-space error bounds that justify the method's design
and highlight its advantages in terms of minimizing components of these error
bounds. Next, the paper introduces a `sample mesh' concept that enables a
distributed, computationally efficient implementation of the GNAT method in
finite-volume-based computational-fluid-dynamics (CFD) codes. The suitability
of GNAT for parameterized problems is highlighted with the solution of an
academic problem featuring moving discontinuities. Finally, the capability of
this method to reduce by orders of magnitude the core-hours required for
large-scale CFD computations, while preserving accuracy, is demonstrated with
the simulation of turbulent flow over the Ahmed body. For an instance of this
benchmark problem with over 17 million degrees of freedom, GNAT outperforms
several other nonlinear model-reduction methods, reduces the required
computational resources by more than two orders of magnitude, and delivers a
solution that differs by less than 1% from its high-dimensional counterpart
Intelligent Data Storage and Retrieval for Design Optimisation – an Overview
This paper documents the findings of a literature review conducted by the Sir Lawrence Wackett Centre for Aerospace Design Technology at RMIT University. The review investigates aspects of a proposed system for intelligent design optimisation. Such a system would be capable of efficiently storing (and compressing if required) a range of types of design data into an intelligent database. This database would be accessed by the system during subsequent design processes, allowing for search of relevant design data for re-use in later designs, allowing it to become very efficient in reducing the time for later designs as the database grows in size. Extensive research has been performed, in both theoretical aspects of the project, and practical examples of current similar systems. This research covers the areas of database systems, database queries, representation and compression of design data, geometric representation and heuristic methods for design applications.
Data quality evaluation through data quality rules and data provenance.
The application and exploitation of large amounts of data play an ever-increasing role in today’s research, government, and economy. Data understanding and decision making heavily rely on high quality data; therefore, in many different contexts, it is important to assess the quality of a dataset in order to determine if it is suitable to be used for a specific purpose. Moreover, as the access to and the exchange of datasets have become easier and more frequent, and as scientists increasingly use the World Wide Web to share scientific data, there is a growing need to know the provenance of a dataset (i.e., information about the processes and data sources that lead to its creation) in order to evaluate its trustworthiness. In this work, data quality rules and data provenance are used to evaluate the quality of datasets.
Concerning the first topic, the applied solution consists in the identification of types of data constraints that can be useful as data quality rules and in the development of a software tool to evaluate a dataset on the basis of a set of rules expressed in the XML markup language. We selected some of the data constraints and dependencies already considered in the data quality field, but we also used order dependencies and existence constraints as quality rules. In addition, we developed some algorithms to discover the types of dependencies used in the tool. To deal with the provenance of data, the Open Provenance Model (OPM) was adopted, an experimental query language for querying OPM graphs stored in a relational database was implemented, and an approach to design OPM graphs was proposed
From Relations to XML: Cleaning, Integrating and Securing Data
While relational databases are still the preferred approach for storing data, XML is emerging
as the primary standard for representing and exchanging data. Consequently, it has
been increasingly important to provide a uniform XML interface to various data sources—
integration; and critical to protect sensitive and confidential information in XML data —
access control. Moreover, it is preferable to first detect and repair the inconsistencies in
the data to avoid the propagation of errors to other data processing steps. In response to
these challenges, this thesis presents an integrated framework for cleaning, integrating and
securing data.
The framework contains three parts. First, the data cleaning sub-framework makes
use of a new class of constraints specially designed for improving data quality, referred
to as conditional functional dependencies (CFDs), to detect and remove inconsistencies in
relational data. Both batch and incremental techniques are developed for detecting CFD
violations by SQL efficiently and repairing them based on a cost model. The cleaned relational
data, together with other non-XML data, is then converted to XML format by using
widely deployed XML publishing facilities. Second, the data integration sub-framework
uses a novel formalism, XML integration grammars (XIGs), to integrate multi-source XML
data which is either native or published from traditional databases. XIGs automatically
support conformance to a target DTD, and allow one to build a large, complex integration
via composition of component XIGs. To efficiently materialize the integrated data, algorithms
are developed for merging XML queries in XIGs and for scheduling them. Third, to
protect sensitive information in the integrated XML data, the data security sub-framework
allows users to access the data only through authorized views. User queries posed on these
views need to be rewritten into equivalent queries on the underlying document to avoid the
prohibitive cost of materializing and maintaining large number of views. Two algorithms
are proposed to support virtual XML views: a rewriting algorithm that characterizes the
rewritten queries as a new form of automata and an evaluation algorithm to execute the
automata-represented queries. They allow the security sub-framework to answer queries
on views in linear time.
Using both relational and XML technologies, this framework provides a uniform approach
to clean, integrate and secure data. The algorithms and techniques in the framework
have been implemented and the experimental study verifies their effectiveness and efficiency
Updating the Lambda modes of a nuclear power reactor
[EN] Starting from a steady state configuration of a nuclear power reactor some situations arise in which the reactor configuration is perturbed. The Lambda modes are eigenfunctions associated with a given configuration of the reactor, which have successfully been used to describe unstable events in BWRs. To compute several eigenvalues and its corresponding eigenfunctions for a nuclear reactor is quite expensive from the computational point of view. Krylov subspace methods are efficient methods to compute the dominant Lambda modes associated with a given configuration of the reactor, but if the Lambda modes have to be computed for different perturbed configurations of the reactor more efficient methods can be used. In this paper, different methods for the updating Lambda modes problem will be proposed and compared by computing the dominant Lambda modes of different configurations associated with a Boron injection transient in a typical BWR reactor. (C) 2010 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia under projects ENE2008-02669 and MTM2007-64477-AR07, the Generalitat Valenciana under project ACOMP/2009/058, and the Universidad Politecnica de Valencia under project PAID-05-09-4285.González Pintor, S.; Ginestar Peiro, D.; Verdú Martín, GJ. (2011). Updating the Lambda modes of a nuclear power reactor. Mathematical and Computer Modelling. 54(7):1796-1801. https://doi.org/10.1016/j.mcm.2010.12.013S1796180154
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
Unstructured Grid Generation Techniques and Software
The Workshop on Unstructured Grid Generation Techniques and Software was conducted for NASA to assess its unstructured grid activities, improve the coordination among NASA centers, and promote technology transfer to industry. The proceedings represent contributions from Ames, Langley, and Lewis Research Centers, and the Johnson and Marshall Space Flight Centers. This report is a compilation of the presentations made at the workshop
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