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The level-set method has been recently introduced in the field of shape optimization, enabling a smooth representation of the boundaries on a fixed mesh and therefore leading to fast numerical algorithms. However, most of these algorithms use a Hamilton-Jacobi equation to connect the evolution of the level-set function with the deformation of the contours, and consequently they cannot create any new holes in the domain (at least in 2D). In this work, we propose an evolution equation for the level-set function based on a generalization of the concept of topological gradient. This results in a new algorithm allowing for all kinds of topology changes.

An autoregressive-ARCH model with possible exogeneous variables is treated. We estimate the conditional volatility of the model by applying feedforward networks to the residuals and prove consistency and asymptotic normality for the estimates under the rate of feedforward networks complexity. Recurrent neural networks estimates of GARCH and value-at-risk is studied. We prove consistency and asymptotic normality for the recurrent neural networks ARMA estimator under the rate of recurrent networks complexity. We also overcome the estimation problem in stochastic variance models in discrete time by feedforward networks and the introduction of a new distributions on the innovations. We use the method to calculate market risk such as expected shortfall and Value-at risk. We tested this distribution together with other new distributions on the GARCH family models against other common distributions on the financial market such as Normal Inverse Gaussian, normal and the Student's t- distributions. As an application of the models, some German stocks are studied and the different approaches are compared together with the most common method of GARCH(1,1) fit.

Within the last decades, a remarkable development in materials science took place -- nowadays, materials are not only constructed for the use of inert structures but rather designed for certain predefined functions. This innovation was accompanied with the appearance of smart materials with reliable recognition, discrimination and capability of action as well as reaction. Even though ferroelectric materials serve smartly in real applications, they also possess several restrictions at high performance usage. The behavior of these materials is almost linear under the action of low electric fields or low mechanical stresses, but exhibits strong non-linear response under high electric fields or mechanical stresses. High electromechanical loading conditions result in a change of the spontaneous polarization direction with respect to individual domains, which is commonly referred to as domain switching. The aim of the present work is to develop a three-dimensional coupled finite element model, to study the rate-independent and rate-dependent behavior of piezoelectric materials including domain switching based on a micromechanical approach. The proposed model is first elaborated within a two-dimensional finite element setting for piezoelectric materials. Subsequently, the developed two-dimensional model is extended to the three-dimensional case. This work starts with developing a micromechanical model for ferroelectric materials. Ferroelectric materials exhibit ferroelectric domain switching, which refers to the reorientation of domains and occurs under purely electrical loading. For the simulation, a bulk piezoceramic material is considered and each grain is represented by one finite element. In reality, the grains in the bulk ceramics material are randomly oriented. This property is taken into account by applying random orientation as well as uniform distribution for individual elements. Poly-crystalline ferroelectric materials at un-poled virgin state can consequently be characterized by randomly oriented polarization vectors. Energy reduction of individual domains is adopted as a criterion for the initiation of domain switching processes. The macroscopic response of the bulk material is predicted by classical volume-averaging techniques. In general, domain switching does not only depend on external loads but also on neighboring grains, which is commonly denoted as the grain boundary effect. These effects are incorporated into the developed framework via a phenomenologically motivated probabilistic approach by relating the actual energy level to a critical energy level. Subsequently, the order of the chosen polynomial function is optimized so that simulations nicely match measured data. A rate-dependent polarization framework is proposed, which is applied to cyclic electrical loading at various frequencies. The reduction in free energy of a grain is used as a criterion for the onset of the domain switching processes. Nucleation in new grains and propagation of the domain walls during domain switching is modeled by a linear kinetics theory. The simulated results show that for increasing loading frequency the macroscopic coercive field is also increasing and the remanent polarization increases at lower loading amplitudes. The second part of this work is focused on ferroelastic domain switching, which refers to the reorientation of domains under purely mechanical loading. Under sufficiently high mechanical loading, however, the strain directions within single domains reorient with respect to the applied loading direction. The reduction in free energy of a grain is used as a criterion for the domain switching process. The macroscopic response of the bulk material is computed for the hysteresis curve (stress vs strain) whereby uni-axial and quasi-static loading conditions are applied on the bulk material specimen. Grain boundary effects are addressed by incorporating the developed probabilistic approach into this framework and the order of the polynomial function is optimized so that simulations match measured data. Rate dependent domain switching effects are captured for various frequencies and mechanical loading amplitudes by means of the developed volume fraction concept which relates the particular time interval to the switching portion. The final part of this work deals with ferroelectric and ferroelastic domain switching and refers to the reorientation of domains under coupled electromechanical loading. If this free energy for combined electromechanical loading exceeds the critical energy barrier elements are allowed to switch. Firstly, hysteresis and butterfly curves under purely electrical loading are discussed. Secondly, additional mechanical loads in axial and lateral directions are applied to the specimen. The simulated results show that an increasing compressive stress results in enlarged domain switching ranges and that the hysteresis and butterfly curves flatten at higher mechanical loading levels.

In this thesis we have discussed the problem of decomposing an integer matrix \(A\) into a weighted sum \(A=\sum_{k \in {\mathcal K}} \alpha_k Y^k\) of 0-1 matrices with the strict consecutive ones property. We have developed algorithms to find decompositions which minimize the decomposition time \(\sum_{k \in {\mathcal K}} \alpha_k\) and the decomposition cardinality \(|\{ k \in {\mathcal K}: \alpha_k > 0\}|\). In the absence of additional constraints on the 0-1 matrices \(Y^k\) we have given an algorithm that finds the minimal decomposition time in \({\mathcal O}(NM)\) time. For the case that the matrices \(Y^k\) are restricted to shape matrices -- a restriction which is important in the application of our results in radiotherapy -- we have given an \({\mathcal O}(NM^2)\) algorithm. This is achieved by solving an integer programming formulation of the problem by a very efficient combinatorial algorithm. In addition, we have shown that the problem of minimizing decomposition cardinality is strongly NP-hard, even for matrices with one row (and thus for the unconstrained as well as the shape matrix decomposition). Our greedy heuristics are based on the results for the decomposition time problem and produce better results than previously published algorithms.

In the first part of this work, called Simple node singularity, are computed matrix factorizations of all isomorphism classes, up to shiftings, of rank one and two, graded, indecomposable maximal Cohen--Macaulay (shortly MCM) modules over the affine cone of the simple node singularity. The subsection 2.2 contains a description of all rank two graded MCM R-modules with stable sheafification on the projective cone of R, by their matrix factorizations. It is given also a general description of such modules, of any rank, over a projective curve of arithmetic genus 1, using their matrix factorizations. The non-locally free rank two MCM modules are computed using an alghorithm presented in the Introduction of this work, that gives a matrix factorization of any extension of two MCM modules over a hypersurface. In the second part, called Fermat surface, are classified all graded, rank two, MCM modules over the affine cone of the Fermat surface. For the classification of the orientable rank two graded MCM R-modules, is used a description of the orientable modules (over normal rings) with the help of codimension two Gorenstein ideals, realized by Herzog and Kühl. It is proven (in section 4), that they have skew symmetric matrix factorizations (over any normal hypersurface ring). For the classification of the non-orientable rank two MCM R-modules, we use a similar idea as in the case of the orientable ones, only that the ideal is not any more Gorenstein.

We will give explicit differentiation and integration rules for homogeneous harmonic polynomial polynomials and spherical harmonics in IR^3 with respect to the following differential operators: partial_1, partial_2, partial_3, x_3 partial_2 - x_2 partial_3, x_3 partial_1 - x_1 partial_3, x_2 partial_1 - x_1 partial_2 and x_1 partial_1 + x_2 partial_2 + x_3 partial_3. A numerical application to the problem of determining the geopotential field will be shown.

In the field of gravity determination a special kind of boundary value problem respectively ill-posed satellite problem occurs; the data and hence side condition of our PDE are oblique second order derivatives of the gravitational potential. In mathematical terms this means that our gravitational potential \(v\) fulfills \(\Delta v = 0\) in the exterior space of the Earth and \(\mathscr D v = f\) on the discrete data location which is on the Earth's surface for terrestrial measurements and on a satellite track in the exterior for spaceborne measurement campaigns. \(\mathscr D\) is a first order derivative for methods like geometric astronomic levelling and satellite-to-satellite tracking (e.g. CHAMP); it is a second order derivative for other methods like terrestrial gradiometry and satellite gravity gradiometry (e.g. GOCE). Classically one can handle first order side conditions which are not tangential to the surface and second derivatives pointing in the radial direction employing integral and pseudo differential equation methods. We will present a different approach: We classify all first and purely second order operators \(\mathscr D\) which fulfill \(\Delta \mathscr D v = 0\) if \(\Delta v = 0\). This allows us to solve the problem with oblique side conditions as if we had ordinary i.e. non-derived side conditions. The only additional work which has to be done is an inversion of \(\mathscr D\), i.e. integration.

This diploma thesis examines logistic problems occurring in a container terminal. The thesis focuses on the scheduling of cranes handling containers in a port. Two problems are discussed in detail: the yard crane scheduling of rubber-tired gantry cranes (RMGC) which move freely among the container blocks, and the scheduling of rail-mounted gantry cranes (RMGC) which can only move within a yard zone. The problems are formulated as integer programs. For each of the two problems discussed, two models are presented: In one model, the crane tasks are interpreted as jobs with release times and processing times while in the other model, it is assumed that the tasks can be modeled as generic workload measured in crane minutes. It is shown that the problems are NP-hard in the strong sense. Heuristic solution procedures are developed and evaluated by numerical results. Further ideas which could lead to other solution procedures are presented and some interesting special cases are discussed.

This thesis aims at an overall improvement of the diffusion coefficient predictions. For this reason the theoretical determination of diffusion, viscosity, and thermodynamics in liquid systems is discussed. Furthermore, the experimental determination of diffusion coefficients is also part of this work. All investigations presented are carried out for organic binary liquid mixtures. Diffusion coefficient data of 9 highly nonideal binary mixtures are reported over the whole concentration range at various temperatures, (25, 30, and 35) °C. All mixtures investigated in a Taylor dispersion apparatus consist of an alcohol (ethanol, 1-propanol, or 1-butanol) dissolved in hexane, cyclohexane, carbon tetrachloride, or toluene. The uncertainty of the reported data is estimated to be within 310-11 m2s-1. To compute the thermodynamic correction factor an excess Gibbs energy model is required. Therefore, the applicability of COSMOSPACE to binary VLE predictions is thoroughly investigated. For this purpose a new method is developed to determine the required molecular parameters such as segment types, areas, volumes, and interaction parameters. So-called sigma profiles form the basis of this approach which describe the screening charge densities appearing on a molecule’s surface. To improve the prediction results a constrained two-parameter fitting strategy is also developed. These approaches are crucial to guarantee the physical significance of the segment parameters. Finally, the prediction quality of this approach is compared to the findings of the Wilson model, UNIQUAC, and the a priori predictive method COSMO-RS for a broad range of thermodynamic situations. The results show that COSMOSPACE yields results of similar quality compared to the Wilson model, while both perform much better than UNIQUAC and COSMO-RS. Since viscosity influences also the diffusion process, a new mixture viscosity model has been developed on the basis of Eyring’s absolute reaction rate theory. The nonidealities of the mixture are accounted for with the thermodynamically consistent COSMOSPACE approach. The required model and component parameters are derived from sigma-profiles, which form the basis of the a priori predictive method COSMO-RS. To improve the model performance two segment parameters are determined from a least-squares analysis to experimental viscosity data, whereas a constraint optimisation procedure is applied. In this way the parameters retain their physical meaning. Finally, the viscosity calculations of this approach are compared to the findings of the Eyring-UNIQUAC model for a broad range of chemical mixtures. These results show that the new Eyring-COSMOSPACE approach is superior to the frequently employed Eyring-UNIQUAC method. Finally, on the basis of Eyring’s absolute reaction rate theory a new model for the Maxwell-Stefan diffusivity has been developed. This model, an extension of the Vignes equation, describes the concentration dependence of the diffusion coefficient in terms of the diffusivities at infinite dilution and an additional excess Gibbs energy contribution. This energy part allows the explicit consideration of thermodynamic nonidealities within the modelling of this transport property. If the same set of interaction parameters, which has been derived from VLE data, is applied for this part and for the thermodynamic correction, a theoretically sound modelling of VLE and diffusion can be achieved. The influence of viscosity and thermodynamics on the model accuracy is thoroughly investigated. For this purpose diffusivities of 85 binary mixtures consisting of alkanes, cycloalkanes, halogenated alkanes, aromatics, ketones, and alcohols are computed. The average relative deviation between experimental data and computed values is approximately 8 % depending on the choice of the gE-model. These results indicate that this model is superior to some widely used methods. In summary, it can be said that the new approach facilitates the prediction of diffusion coefficients. The final equation is mathematically simple, universally applicable, and the prediction quality is as good as other models recently developed without having to worry about additional parameters, like pure component physical property data, self diffusion coefficients, or mixture viscosities. In contrast to many other models, the influence of the mixture viscosity can be omitted. Though a viscosity model is not required in the prediction of diffusion coefficients with the new equation, the models presented in this work allow a consistent modelling approach of diffusion, viscosity, and thermodynamics in liquid systems.

The use of polymers subjected to various tribological situations has become state of
the art. Owing to the advantages of self-lubrication and superior cleanliness, more
and more polymer composites are now being used as sliding elements, which were
formerly composed of metallic materials only. The feature that makes polymer composites
so promising in industrial applications is the opportunity to tailor their properties
with special fillers. The main aim of this study was to strength the importance of
integrating various functional fillers in the design of wear-resistant polymer composites
and to understand the role of fillers in modifying the wear behaviour of the materials.
Special emphasis was focused on enhancement of the wear resistance of
thermosetting and thermoplastic matrix composites by nano-TiO2 particles (with a
diameter of 300nm).
In order to optimize the content of various fillers, the tribological performance of a
series of epoxy-based composites, filled with short carbon fibre (SCF), graphite,
PTFE and nano-TiO2 in different proportions and combinations, was investigated.
The patterns of frictional coefficient, wear resistance and contact temperature were
examined by a pin-on-disc apparatus in a dry sliding condition under different contact
pressures and sliding velocities. The experimental results indicated that the addition
of nano-TiO2 effectively reduced the frictional coefficient, and consequently the contact
temperature, of short-fibre reinforced epoxy composites. Based on scanning
electron microscopy (SEM) and atomic force microscopy (AFM) observations of the
worn surfaces, a positive rolling effect of the nanoparticles between the material pairs
was proposed, which led to remarkable reduction of the frictional coefficient. In particular,
this rolling effect protected the SCF from more severe wear mechanisms, especially
in high sliding pressure and speed situations. As a result, the load carrying capacity of materials was significantly improved. In addition, the different contributions
of two solid lubricants, PTFE powders and graphite flakes, on the tribological
performance of epoxy nanocomposites were compared. It seems that graphite contributes
to the improved wear resistance in general, whereas PTFE can easily form a
transfer film and reduce the wear rate, especially in the running-in period. A combination of SCF and solid lubricants (PTFE and graphite) together with TiO2 nanoparticles
can achieve a synergistic effect on the wear behaviour of materials.
The favourable effect of nanoparticles detected in epoxy composites was also found
in the investigations of thermoplastic, e.g. polyamide (PA) 6,6 matrix. It was found
that nanoparticles could reduce the friction coefficient and wear rate of the PA6,6
composite remarkably, when additionally incorporated with short carbon fibres and
graphite flakes. In particular, the addition of nanoparticles contributed to an obvious
enhancement of the tribological performances of the short-fibre reinforced, hightemperature
resistant polymers, e.g. polyetherimide (PEI), especially under extreme
sliding conditions.
A procedure was proposed in order to correlate the contact temperature and the
wear rate with the frictional dissipated energy. Based on this energy consideration, a
better interpretation of the different performance of distinct tribo-systems is possible.
The validity of the model was illustrated for various sliding tests under different conditions.
Although simple quantitative formulations could not be expected at present, the
study may lead to a fundamental understanding of the mechanisms controlling friction
and wear from a general system point of view. Moreover, using the energybased
models, the artificial neural network (ANN) approach was applied to the experimental
data. The well-trained ANN has the potential to be further used for online
monitoring and prediction of wear progress in practical applications.
Die Verwendung von Polymeren im Hinblick auf verschiedene tribologische Anwendungen
entspricht mittlerweile dem Stand der Technik. Aufgrund der Vorteile von
Selbstschmierung und ausgezeichneter Sauberkeit werden polymere Verbundwerkstoffe
immer mehr als Gleitelemente genutzt, welche früher ausschließlich aus metallischen
Werkstoffen bestanden. Die Besonderheit, die polymere Verbundwerkstoffe
so vielversprechend für industrielle Anwendungen macht, ist die Möglichkeit ihre Eigenschaften
durch Zugabe von speziellen Füllstoffen maßzuschneidern. Das Hauptziel
dieser Arbeit bestand darin, die Wichtigkeit der Integration verschiedener funktionalisierter
Füllstoffe in den Aufbau polymerer Verbundwerkstoffe mit hohem Verschleißwiderstand
aufzuzeigen und die Rolle der Füllstoffe hinsichtlich des Verschleißverhaltens
zu verstehen. Hierbei lag besonderes Augenmerk auf der Verbesserung
des Verschleißwiderstandes bei Verbunden mit duromerer und thermoplastischer
Matrix durch die Präsenz von TiO2-Partikeln (Durchmesser 300nm).
Das tribologische Verhalten epoxidharzbasierter Verbunde, gefüllt mit kurzen Kohlenstofffasern
(SCF), Graphite, PTFE und nano-TiO2 in unterschiedlichen Proportionen
und Kombinationen wurde untersucht, um den jeweiligen Füllstoffgehalt zu optimieren.
Das Verhalten von Reibungskoeffizient, Verschleißwiderstand und Kontakttemperatur
wurde unter Verwendung einer Stift-Scheibe Apparatur bei trockenem
Gleitzustand, verschiedenen Kontaktdrücken und Gleitgeschwindigkeiten erforscht.
Die experimentellen Ergebnisse zeigen, dass die Zugabe von nano-TiO2 in kohlenstofffaserverstärkte
Epoxide den Reibungskoeffizienten und die Kontakttemperatur
herabsetzen können. Basierend auf Aufnahmen der verschlissenen Oberflächen
durch Rasterelektronen- (REM) und Rasterkraftmikroskopie (AFM) trat ein positiver
Rolleffekt der Nanopartikel zwischen den Materialpaaren zum Vorschein, welcher zu
einer beachtlichen Reduktion des Reibungskoeffizienten führte. Dieser Rolleffekt schützte insbesondere die SCF vor schwerwiegenderen Verschleißmechanismen,
speziell bei hohem Gleitdruck und hohen Geschwindigkeiten. Als Ergebnis konnte
die Tragfähigkeit dieser Materialien wesentlich verbessert werden. Zusätzlich wurde
die Wirkung zweier fester Schmierstoffe (PTFE-Pulver und Graphit-Flocken) auf die tribologische Leistungsfähigkeit verglichen. Es scheint, daß Graphit generell zur Verbesserung
des Verschleißwiderstandes beiträgt, wobei PTFE einen Transferfilm bilden
kann und die Verschleißrate insbesondere in der Einlaufphase reduziert. Die
Kombination von SCF und festen Schmierstoffen zusammen mit TiO2-Nanopartikeln
kann einen Synergieeffekt bei dem Verschleißverhalten der Materialien hervorrufen.
Der positive Effekt der Nanopartikel in Duromeren wurde ebenfalls bei den Untersuchungen
von Thermoplasten (PA 66) gefunden. Die Nanopartikel konnten den Reibungskoeffizienten
und die Verschleißrate der PA 66-Verbunde herabsetzen, wobei
zusätzlich Kohlenstofffasern und Graphit-Flocken enthalten waren. Die Zugabe von
Nanopartikeln trug offensichtlich auch zur Verbesserung der tribologischen Leistungsfähigkeit
von SCF-verstärkten, hochtemperaturbeständigen Polymeren (PEI)
insbesondere unter extremen Gleitzuständen, bei. Es wurde eine Methode vorgestellt,
um die Kontakttemperatur und die Verschleißrate mit der durch Reibung dissipierten
Energie zu korrelieren. Diese Energiebetrachtung ermöglicht eine bessere
Interpretation der verschiedenen Eigenschaften von ausgewählten Tribo-Systemen.
Die Gültigkeit dieses Models wurde für mehrere Gleittests unter verschiedenen Bedingungen
erklärt.
Vom generellen Blickpunkt eines tribologischen Systems aus mag diese Arbeit zu
einem fundamentalen Verständnis der Mechanismen führen, welche das Reibungs und Verschleißverhalten kontrollieren, obwohl hier einfache quantitative (mathematische)
Zusammenhänge bisher nicht zu erwarten sind. Der auf energiebasierenden
Modellen fußende Lösungsansatz der neuronalen Netzwerke (ANN) wurde darüber
hinaus auf die experimentellen Datensätze angewendet. Die gut trainierten ANN's
besitzen das Potenzial sie in der praktischen Anwendungen zur Online-
Datenauswertung und zur Vorhersage des Verschleißfortschritts einzusetzen.