A global approach to the refinement of manifold data

A refinement of manifold data is a computational process, which produces a denser set of discrete data from a given one. Such refinements are closely related to multiresolution representations of manifold data by pyramid transforms, and approximation of manifold-valued functions by repeated refinements schemes. Most refinement methods compute each refined element separately, independently of the computations of the other elements. Here we propose a global method which computes all the refined elements simultaneously, using geodesic averages. We analyse repeated refinements schemes based on this global approach, and derive conditions guaranteeing strong convergence.Comment: arXiv admin note: text overlap with arXiv:1407.836

A New Strategy for Exact Determination of the Joint Spectral Radius

Computing the joint spectral radius of a finite matrix family is, though interesting for many applications, a difficult problem. This work proposes a method for determining the exact value which is based on graph-theoretical ideas. In contrast to some other algorithms in the literature, the purpose of the approach is not to find an extremal norm for the matrix family. To validate that the finiteness property (FP) is satisfied for a certain matrix product, a tree is to be analyzed whose nodes code sets of matrix products. A sufficient, and in certain situations also necessary, criterion is given by existence of a finite tree with special properties, and an algorithm for searching such a tree is proposed. The suggested method applies in case of several FP-products as well and is not limited to asymptotically simple matrix families. In the smoothness analysis of subdivision schemes, joint spectral radius determination is crucial to detect Hölder regularity. The palindromic symmetry of matrices, which results from symmetric binary subdivision, is considered in the context of set-valued trees. Several illustrating examples explore the capabilities of the approach, consolidated by examples from subdivision

Subdivision and manifold techniques for isogeometric design and analysis of surfaces

Design of surfaces and analysis of partial differential equations defined on them are of great importance in engineering applications, e.g., structural engineering, automotive and aerospace. This thesis focuses on isogeometric design and analysis of surfaces, which aims to integrate engineering design and analysis by using the same representation for both. The unresolved challenge is to develop a desirable surface representation that simultaneously satisfies certain favourable properties on meshes of arbitrary topology around the extraordinary vertices (EVs), i.e., vertices not shared by four quadrilaterals or three triangles. These properties include high continuity (geometric or parametric), optimal convergence in finite element analysis as well as simplicity in terms of implementation. To overcome the challenge, we further develop subdivision and manifold surface modelling techniques, and explore a possible scheme to combine the distinct appealing properties of the two. The unique advantages of the developed techniques have been confirmed with numerical experiments. Subdivision surfaces generate smooth surfaces from coarse control meshes of arbitrary topology by recursive refinement. Around EVs the optimal refinement weights are application-dependent. We first review subdivision-based finite elements. We then proceed to derive the optimal subdivision weights that minimise finite element errors and can be easily incorporated into existing implementations of subdivision schemes to achieve the same accuracy with much coarser meshes in engineering computations. To this end, the eigenstructure of the subdivision matrix is extensively used and a novel local shape decomposition approach is proposed to choose the optimal weights for each EV independently. Manifold-based basis functions are derived by combining differential-geometric manifold techniques with conformal parametrisations and the partition of unity method. This thesis derives novel manifold-based basis functions with arbitrary prescribed smoothness using quasi-conformal maps, enabling us to model and analyse surfaces with sharp features, such as creases and corners. Their practical utility in finite element simulation of hinged or rigidly joined structures is demonstrated with Kirchhoff-Love thin shell examples. We also propose a particular manifold basis reproducing subdivision surfaces away from EVs, i.e., B-splines, providing a way to combine the appealing properties of subdivision (available in industrial software) for design and manifold basis (relatively new) for analysis.Cambridge International Scholarship Scheme (CISS) by Cambridge Trus

Trajectory optimization based on recursive B-spline approximation for automated longitudinal control of a battery electric vehicle

Diese Arbeit beschreibt ein neuartiges Verfahren zur linearen und nichtlinearen gewichteten Kleinste-Quadrate-Approximation einer unbeschränkten Anzahl von Datenpunkten mit einer B-Spline-Funktion. Das entwickelte Verfahren basiert auf iterativen Algorithmen zur Zustandsschätzung und sein Rechenaufwand nimmt linear mit der Anzahl der Datenpunkte zu. Das Verfahren ermöglicht eine Verschiebung des beschränkten Definitionsbereichs einer B-Spline-Funktion zur Laufzeit, sodass der aktuell betrachtete Datenpunkt ungeachtet des anfangs gewählten Definitionsbereichs bei der Approximation berücksichtigt werden kann. Zudem ermöglicht die Verschiebeoperation die Reduktion der Größen der Matrizen in den Zustandsschätzern zur Senkung des Rechenaufwands sowohl in Offline-Anwendungen, in denen alle Datenpunkte gleichzeitig zur Verarbeitung vorliegen, als auch in Online-Anwendungen, in denen in jedem Zeitschritt weitere Datenpunkte beobachtet werden. Das Trajektorienoptimierungsproblem wird so formuliert, dass das Approximationsverfahren mit Datenpunkten aus Kartendaten eine B-Spline-Funktion berechnet, die die gewünschte Geschwindigkeitstrajektorie bezüglich der Zeit repräsentiert. Der Rechenaufwand des resultierenden direkten Trajektorienoptimierungsverfahrens steigt lediglich linear mit der unbeschränkten zeitlichen Trajektorienlänge an. Die Kombination mit einem adaptiven Modell des Antriebsstrangs eines batterie-elektrischen Fahrzeugs mit festem Getriebeübersetzungsverhältnis ermöglicht die Optimierung von Geschwindigkeitstrajektorien hinsichtlich Fahrzeit, Komfort und Energieverbrauch. Das Trajektorienoptimierungsverfahren wird zu einem Fahrerassistenzsystem für die automatisierte Fahrzeuglängsführung erweitert, das simulativ und in realen Erprobungsfahrten getestet wird. Simulierte Fahrten auf der gewählten Referenzstrecke benötigten bis zu 3,4 % weniger Energie mit der automatisierten Längsführung als mit einem menschlichen Fahrer bei derselben Durchschnittsgeschwindigkeit. Für denselben Energieverbrauch erzielt die automatisierte Längsführung eine 2,6 % höhere Durchschnittsgeschwindigkeit als ein menschlicher Fahrer

Surrogate - Assisted Optimisation -Based Verification & Validation

This thesis deals with the application of optimisation based Validation and Verification (V&V) analysis on aerospace vehicles in order to determine their worst case performance metrics. To this end, three aerospace models relating to satellite and launcher vehicles provided by European Space Agency (ESA) on various projects are utilised. As a means to quicken the process of optimisation based V&V analysis, surrogate models are developed using polynomial chaos method. Surro- gate models provide a quick way to ascertain the worst case directions as computation time required for evaluating them is very small. A sin- gle evaluation of a surrogate model takes less than a second. Another contribution of this thesis is the evaluation of operational safety margin metric with the help of surrogate models. Operational safety margin is a metric defined in the uncertain parameter space and is related to the distance between the nominal parameter value and the first instance of performance criteria violation. This metric can help to gauge the robustness of the controller but requires the evaluation of the model in the constraint function and hence could be computationally intensive. As surrogate models are computationally very cheap, they are utilised to rapidly compute the operational safety margin metric. But this metric focuses only on finding a safe region around the nominal parameter value and the possibility of other disjoint safe regions are not explored. In order to find other safe or failure regions in the param- eter space, the method of Bernstein expansion method is utilised on surrogate polynomial models to help characterise the uncertain param- eter space into safe and failure regions. Furthermore, Binomial failure analysis is used to assign failure probabilities to failure regions which might help the designer to determine if a re-design of the controller is required or not. The methodologies of optimisation based V&V, surrogate modelling, operational safety margin, Bernstein expansion method and risk assessment have been combined together to form the WCAT-II MATLAB toolbox

Blending techniques in Curve and Surface constructions

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Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal