9 research outputs found
Model order reduction by convex displacement interpolation
We present a nonlinear interpolation technique for parametric fields that
exploits optimal transportation of coherent structures of the solution to
achieve accurate performance. The approach generalizes the nonlinear
interpolation procedure introduced in [Iollo, Taddei, J. Comput. Phys., 2022]
to multi-dimensional parameter domains and to datasets of several snapshots.
Given a library of high-fidelity simulations, we rely on a scalar testing
function and on a point set registration method to identify coherent structures
of the solution field in the form of sorted point clouds. Given a new parameter
value, we exploit a regression method to predict the new point cloud; then, we
resort to a boundary-aware registration technique to define bijective mappings
that deform the new point cloud into the point clouds of the neighboring
elements of the dataset, while preserving the boundary of the domain; finally,
we define the estimate as a weighted combination of modes obtained by composing
the neighboring snapshots with the previously-built mappings. We present
several numerical examples for compressible and incompressible, viscous and
inviscid flows to demonstrate the accuracy of the method. Furthermore, we
employ the nonlinear interpolation procedure to augment the dataset of
simulations for linear-subspace projection-based model reduction: our data
augmentation procedure is designed to reduce offline costs -- which are
dominated by snapshot generation -- of model reduction techniques for nonlinear
advection-dominated problems
Posture adjustment of workpiece based on stepwise matching by self-adaptive differential evolution algorithm
The workpiece contour errors from previous process affect current and
subsequent process. In order to improve the uniformity of workpiece allowance
distribution, a stepwise workpiece matching adjustment method with contact
inspection is developed. This method includes two stepwise registration
processes. At first, some pairs of measured points on the theoretical and
actual surfaces are selected to build the corresponding local coordinate
systems, and then the rough matching matrixes are obtained by the coordinate
systems alignment. During the fine matching process, the objective function
based on the least square method is established by the measured point sets
adjusted through the rough matching. The fine matching matrixes can be
obtained by self-adaptive differential evolution algorithm. The posture
adjustment can be realized by transforming coordinate systems of 5Â axis
machine tool, of which adjusted values can be calculated by the matching
matrixes and the machine tool topology. At last, some experiments were
presented to demonstrate the performance of the method.</p
Accelerating off-lattice kinetic Monte Carlo simulations to predict hydrogen vacancy-cluster interactions in α–Fe
We present an enhanced off-lattice kinetic Monte Carlo (OLKMC) model, based on a new method for tolerant classification of atomistic local-environments that is invariant under Euclidean-transformations and permutations of atoms. Our method ensures that environments within a norm-based tolerance are classified as equivalent. During OLKMC simulations, our method guarantees to elide the maximum number of redundant saddle-point searches in symmetrically equivalent local-environments. Hence, we are able to study the trapping/detrapping of hydrogen from up to five-vacancy clusters and simultaneously the effect hydrogen has on the diffusivity of these clusters. These processes occur at vastly different timescales at room temperature in body-centred cubic iron. We predict the diffusion pathways of clusters/complexes without a priori assumptions of their mechanisms, not only reproducing previously reported mechanisms but also discovering new ones for larger complexes. We detail the hydrogen-induced changes in the clusters’ diffusion mechanisms and find evidence that, in contrast to mono-vacancies, the introduction of hydrogen to larger clusters can increase their diffusivity. We compare the effective hydrogen diffusivity to Oriani’s classical theory of trapping, finding general agreement and some evidence that hydrogen may not always be in equilibrium with traps, when the traps are mobile. Finally, we compute the trapping atmosphere of meta-stable states surrounding non-point traps, opening new avenues to better understand and predict hydrogen embrittlement in complex alloys
A review of point set registration: from pairwise registration to groupwise registration
Abstract: This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm [1] and JRMPC groupwise registration algorithm [2,3] seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified
Algorithms for the reconstruction, analysis, repairing and enhancement of 3D urban models from multiple data sources
Over the last few years, there has been a notorious growth in the field of digitization of 3D buildings and urban environments. The substantial improvement of both scanning hardware and reconstruction algorithms has led to the development of representations of buildings and cities that can be remotely transmitted and inspected in real-time. Among the applications that implement these technologies are several GPS navigators and virtual globes such as Google Earth or the tools provided by the Institut Cartogrà fic i Geològic de Catalunya.
In particular, in this thesis, we conceptualize cities as a collection of individual buildings. Hence, we focus on the individual processing of one structure at a time, rather than on the larger-scale processing of urban environments.
Nowadays, there is a wide diversity of digitization technologies, and the choice of the appropriate one is key for each particular application. Roughly, these techniques can be grouped around three main families:
- Time-of-flight (terrestrial and aerial LiDAR).
- Photogrammetry (street-level, satellite, and aerial imagery).
- Human-edited vector data (cadastre and other map sources).
Each of these has its advantages in terms of covered area, data quality, economic cost, and processing effort.
Plane and car-mounted LiDAR devices are optimal for sweeping huge areas, but acquiring and calibrating such devices is not a trivial task. Moreover, the capturing process is done by scan lines, which need to be registered using GPS and inertial data. As an alternative, terrestrial LiDAR devices are more accessible but cover smaller areas, and their sampling strategy usually produces massive point clouds with over-represented plain regions. A more inexpensive option is street-level imagery. A dense set of images captured with a commodity camera can be fed to state-of-the-art multi-view stereo algorithms to produce realistic-enough reconstructions. One other advantage of this approach is capturing high-quality color data, whereas the geometric information is usually lacking.
In this thesis, we analyze in-depth some of the shortcomings of these data-acquisition methods and propose new ways to overcome them. Mainly, we focus on the technologies that allow high-quality digitization of individual buildings. These are terrestrial LiDAR for geometric information and street-level imagery for color information.
Our main goal is the processing and completion of detailed 3D urban representations. For this, we will work with multiple data sources and combine them when possible to produce models that can be inspected in real-time. Our research has focused on the following contributions:
- Effective and feature-preserving simplification of massive point clouds.
- Developing normal estimation algorithms explicitly designed for LiDAR data.
- Low-stretch panoramic representation for point clouds.
- Semantic analysis of street-level imagery for improved multi-view stereo reconstruction.
- Color improvement through heuristic techniques and the registration of LiDAR and imagery data.
- Efficient and faithful visualization of massive point clouds using image-based techniques.Durant els darrers anys, hi ha hagut un creixement notori en el camp de la digitalització d'edificis en 3D i entorns urbans. La millora substancial tant del maquinari d'escaneig com dels algorismes de reconstrucció ha portat al desenvolupament de representacions d'edificis i ciutats que es poden transmetre i inspeccionar remotament en temps real. Entre les aplicacions que implementen aquestes tecnologies hi ha diversos navegadors GPS i globus virtuals com Google Earth o les eines proporcionades per l'Institut Cartogrà fic i Geològic de Catalunya. En particular, en aquesta tesi, conceptualitzem les ciutats com una col·lecció d'edificis individuals. Per tant, ens centrem en el processament individual d'una estructura a la vegada, en lloc del processament a gran escala d'entorns urbans. Avui en dia, hi ha una à mplia diversitat de tecnologies de digitalització i la selecció de l'adequada és clau per a cada aplicació particular. Aproximadament, aquestes tècniques es poden agrupar en tres famÃlies principals: - Temps de vol (LiDAR terrestre i aeri). - Fotogrametria (imatges a escala de carrer, de satèl·lit i aèries). - Dades vectorials editades per humans (cadastre i altres fonts de mapes). Cadascun d'ells presenta els seus avantatges en termes d'à rea coberta, qualitat de les dades, cost econòmic i esforç de processament. Els dispositius LiDAR muntats en avió i en cotxe són òptims per escombrar à rees enormes, però adquirir i calibrar aquests dispositius no és una tasca trivial. A més, el procés de captura es realitza mitjançant lÃnies d'escaneig, que cal registrar mitjançant GPS i dades inercials. Com a alternativa, els dispositius terrestres de LiDAR són més accessibles, però cobreixen à rees més petites, i la seva estratègia de mostreig sol produir núvols de punts massius amb regions planes sobrerepresentades. Una opció més barata són les imatges a escala de carrer. Es pot fer servir un conjunt dens d'imatges capturades amb una cà mera de qualitat mitjana per obtenir reconstruccions prou realistes mitjançant algorismes estèreo d'última generació per produir. Un altre avantatge d'aquest mètode és la captura de dades de color d'alta qualitat. Tanmateix, la informació geomètrica resultant sol ser de baixa qualitat. En aquesta tesi, analitzem en profunditat algunes de les mancances d'aquests mètodes d'adquisició de dades i proposem noves maneres de superar-les. Principalment, ens centrem en les tecnologies que permeten una digitalització d'alta qualitat d'edificis individuals. Es tracta de LiDAR terrestre per obtenir informació geomètrica i imatges a escala de carrer per obtenir informació sobre colors. El nostre objectiu principal és el processament i la millora de representacions urbanes 3D amb molt detall. Per a això, treballarem amb diverses fonts de dades i les combinarem quan sigui possible per produir models que es puguin inspeccionar en temps real. La nostra investigació s'ha centrat en les següents contribucions: - Simplificació eficaç de núvols de punts massius, preservant detalls d'alta resolució. - Desenvolupament d'algoritmes d'estimació normal dissenyats explÃcitament per a dades LiDAR. - Representació panorà mica de baixa distorsió per a núvols de punts. - Anà lisi semà ntica d'imatges a escala de carrer per millorar la reconstrucció estèreo de façanes. - Millora del color mitjançant tècniques heurÃstiques i el registre de dades LiDAR i imatge. - Visualització eficient i fidel de núvols de punts massius mitjançant tècniques basades en imatges