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Composite materials are used in many modern tools and engineering applications and
consist of two or more materials that are intermixed. Features like inclusions in a matrix
material are often very small compared to the overall structure. Volume elements that
are characteristic for the microstructure can be simulated and their elastic properties are
then used as a homogeneous material on the macroscopic scale.
Moulinec and Suquet [2] solve the so-called Lippmann-Schwinger equation, a reformulation of the equations of elasticity in periodic homogenization, using truncated
trigonometric polynomials on a tensor product grid as ansatz functions.
In this thesis, we generalize their approach to anisotropic lattices and extend it to
anisotropic translation invariant spaces. We discretize the partial differential equation
on these spaces and prove the convergence rate. The speed of convergence depends on
the smoothness of the coefficients and the regularity of the ansatz space. The spaces of
translates unify the ansatz of Moulinec and Suquet with de la Vallée Poussin means and
periodic Box splines, including the constant finite element discretization of Brisard and
Dormieux [1].
For finely resolved images, sampling on a coarser lattice reduces the computational
effort. We introduce mixing rules as the means to transfer fine-grid information to the
smaller lattice.
Finally, we show the effect of the anisotropic pattern, the space of translates, and the
convergence of the method, and mixing rules on two- and three-dimensional examples.
References
[1] S. Brisard and L. Dormieux. “FFT-based methods for the mechanics of composites:
A general variational framework”. In: Computational Materials Science 49.3 (2010),
pp. 663–671. doi: 10.1016/j.commatsci.2010.06.009.
[2] H. Moulinec and P. Suquet. “A numerical method for computing the overall response
of nonlinear composites with complex microstructure”. In: Computer Methods in
Applied Mechanics and Engineering 157.1-2 (1998), pp. 69–94. doi: 10.1016/s00457825(97)00218-1.

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.

Multiphase materials combine properties of several materials, which makes them interesting for high-performing components. This thesis considers a certain set of multiphase materials, namely silicon-carbide (SiC) particle-reinforced aluminium (Al) metal matrix composites and their modelling based on stochastic geometry models.
Stochastic modelling can be used for the generation of virtual material samples: Once we have fitted a model to the material statistics, we can obtain independent three-dimensional “samples” of the material under investigation without the need of any actual imaging. Additionally, by changing the model parameters, we can easily simulate a new material composition.
The materials under investigation have a rather complicated microstructure, as the system of SiC particles has many degrees of freedom: Size, shape, orientation and spatial distribution. Based on FIB-SEM images, that yield three-dimensional image data, we extract the SiC particle structure using methods of image analysis. Then we model the SiC particles by anisotropically rescaled cells of a random Laguerre tessellation that was fitted to the shapes of isotropically rescaled particles. We fit a log-normal distribution for the volume distribution of the SiC particles. Additionally, we propose models for the Al grain structure and the Aluminium-Copper (\({Al}_2{Cu}\)) precipitations occurring on the grain boundaries and on SiC-Al phase boundaries.
Finally, we show how we can estimate the parameters of the volume-distribution based on two-dimensional SEM images. This estimation is applied to two samples with different mean SiC particle diameters and to a random section through the model. The stereological estimations are within acceptable agreement with the parameters estimated from three-dimensional image data
as well as with the parameters of the model.

Fucoidan is a class of biopolymers mainly found in brown seaweeds. Due to its diverse medical importance, homogenous supply as well as a GMP-compliant product is of a special interest. Therefore, in addition to optimization of its extraction and purification from classical resources, other techniques were tried (e.g., marine tissue culture and heterologous expression of enzymes involved in its biosynthesis). Results showed that 17.5% (w/w) crude fucoidan after pre-treatment and extraction was obtained from the brown macroalgae F. vesiculosus. Purification by affinity chromatography improved purity relative to the commercial purified product. Furthermore, biological investigations revealed improved anti-coagulant and anti-viral activities compared with crude fucoidan. Furthermore, callus-like and protoplast cultures as well as bioreactor cultivation were developed from F. vesiculosus representing a new horizon to produce fucoidan biotechnologically. Moreover, heterologous expression of several enzymes involved in its biosynthesis by E. coli (e.g., FucTs and STs) demonstrated the possibility to obtain active enzymes that could be utilized in enzymatic in vitro synthesis of fucoidan. All these competitive techniques could provide the global demands from fucoidan.

Using valuation theory we associate to a one-dimensional equidimensional semilocal Cohen-Macaulay ring \(R\) its semigroup of values, and to a fractional ideal of \(R\) we associate its value semigroup ideal. For a class of curve singularities (here called admissible rings) including algebroid curves the semigroups of values, respectively the value semigroup ideals, satisfy combinatorial properties defining good semigroups, respectively good semigroup ideals. Notably, the class of good semigroups strictly contains the class of value semigroups of admissible rings. On good semigroups we establish combinatorial versions of algebraic concepts on admissible rings which are compatible with their prototypes under taking values. Primarily we examine duality and quasihomogeneity.
We give a definition for canonical semigroup ideals of good semigroups which characterizes canonical fractional ideals of an admissible ring in terms of their value semigroup ideals. Moreover, a canonical semigroup ideal induces a duality on the set of good semigroup ideals of a good semigroup. This duality is compatible with the Cohen-Macaulay duality on fractional ideals under taking values.
The properties of the semigroup of values of a quasihomogeneous curve singularity lead to a notion of quasihomogeneity on good semigroups which is compatible with its algebraic prototype. We give a combinatorial criterion which allows to construct from a quasihomogeneous semigroup \(S\) a quasihomogeneous curve singularity having \(S\) as semigroup of values.
As an application we use the semigroup of values to compute endomorphism rings of maximal ideals of algebroid curves. This yields an explicit description of the intermediate rings in an algorithmic normalization of plane central arrangements of smooth curves based on a criterion by Grauert and Remmert. Applying this result to hyperplane arrangements we determine the number of steps needed to compute the normalization of a the arrangement in terms of its Möbius function.

The phase field approach is a powerful tool that can handle even complicated fracture phenomena within an apparently simple framework. Nonetheless, a profound understanding of the model is required in order to be able to interpret the obtained results correctly. Furthermore, in the dynamic case the phase field model needs to be verified in comparison to experimental data and analytical results in order to increase the trust in this new approach. In this thesis, a phase field model for dynamic brittle fracture is investigated with regard to these aspects by analytical and numerical methods

The complexity of modern real-time systems is increasing day by day. This inevitable rise in complexity predominantly stems from two contradicting requirements, i.e., ever increasing demand for functionality, and required low cost for the final product. The development of modern multi-processors and variety of network protocols and architectures have enabled such a leap in complexity and functionality possible. Albeit, efficient use of these multi-processors and network architectures is still a major problem. Moreover, the software design and its development process needs improvements in order to support rapid-prototyping for ever changing system designs. Therefore, in this dissertation, we provide solutions for different problems faced in the development and deployment process of real-time systems. The contributions presented in this thesis enable efficient utilization of system resources, rapid design & development and component modularity & portability.
In order to ease the certification process, time-triggered computation model is often used in distributed systems. However, time-triggered scheduling is NP-hard, due to which the process of schedule generation for complex large systems becomes convoluted. Large scheduler run-times and low scalability are two major problems with time-triggered scheduling. To solve these problems, we present a modular real-time scheduler based on a novel search-tree pruning technique, which consumes less time (compared to the state-of-the-art) in order to schedule tasks on large distributed time-triggered systems. In order to provide end-to-end guarantees, we also extend our modular scheduler to quickly generate schedules for time-triggered network traffic in large TTEthernet based networks. We evaluate our schedulers on synthetic but practical task-sets and demonstrate that our pruning technique efficiently reduces scheduler run-times and exhibits adequate scalability for future time-triggered distributed systems.
In safety critical systems, the certification process also requires strict isolation between independent components. This isolation is enforced by utilizing resource partitioning approach, where different criticality components execute in different partitions (each temporally and spatially isolated from each other). However, existing partitioning approaches use periodic servers or tasks to service aperiodic activities. This approach leads to utilization loss and potentially leads to large latencies. On the contrary to the periodic approaches, state-of-the-art aperiodic task admission algorithms do not suffer from problems like utilization loss. However, these approaches do not support partitioned scheduling or mixed-criticality execution environment. To solve this problem, we propose an algorithm for online admission of aperiodic tasks which provides job execution flexibility, jitter control and leads to lower latencies of aperiodic tasks.
For safety critical systems, fault-tolerance is one of the most important requirements. In time-triggered systems, modes are often used to ensure survivability against faults, i.e., when a fault is detected, current system configuration (or mode) is changed such that the overall system performance is either unaffected or degrades gracefully. In literature, it has been asserted that a task-set might be schedulable in individual modes but unschedulable during a mode-change. Moreover, conventional mode-change execution strategies might cause significant delays until the next mode is established. In order to address these issues, in this dissertation, we present an approach for schedulability analysis of mode-changes and propose mode-change delay reduction techniques in distributed system architecture defined by the DREAMS project. We evaluate our approach on an avionics use case and demonstrate that our approach can drastically reduce mode-change delays.
In order to manage increasing system complexity, real-time applications also require new design and development technologies. Other than fulfilling the technical requirements, the main features required from such technologies include modularity and re-usability. AUTOSAR is one of these technologies in automotive industry, which defines an open standard for software architecture of a real-time operating system. However, being an industrial standard, the available proprietary tools do not support model extensions and/or new developments by third-parties and, therefore, hinder the software evolution. To solve this problem, we developed an open-source AUTOSAR toolchain which supports application development and code generation for several modules. In order to exhibit the capabilities of our toolchain, we developed two case studies. These case studies demonstrate that our toolchain generates valid artifacts, avoids dirty workarounds and supports application development.
In order to cope with evolving system designs and hardware platforms, rapid-development of scheduling and analysis algorithms is required. In order to ease the process of algorithm development, a number of scheduling and analysis frameworks are proposed in literature. However, these frameworks focus on a specific class of applications and are limited in functionality. In this dissertation, we provide the skeleton of a scheduling and analysis framework for real-time systems. In order to support rapid-development, we also highlight different development components which promote code reuse and component modularity.

The core muscles play a central role in stabilizing the head during headers in soccer. The objective of this study was to examine the influence of a fatigued core musculature on the acceleration of the head during jump headers and run headers. Acceleration of the head was measured in a pre-post-design in 68 soccer players (age: 21.5 ± 3.8 years, height: 180.0 ± 13.9 cm, weight: 76.9 ± 8.1 kg). Data were recorded by means of a telemetric 3D acceleration sensor and with a pendulum header. The treatment encompassed two exercises each for the ventral, lateral, and dorsal muscle chains. The acceleration of the head between pre- and post-test was reduced by 0.3 G (p = 0.011) in jump headers and by 0.2 G (p = 0.067) in run headers. An additional analysis of all pretests showed an increased acceleration in run headers when compared to stand headers (p < 0.001) and jump headers (p < 0.001). No differences were found in the sub-group comparisons: semi-professional vs. recreational players, offensive vs. defensive players. Based on the results, we conclude that the acceleration of the head after fatiguing the core muscles does not increase, which stands in contrast to postulated expectations. More tests with accelerated soccer balls are required for a conclusive statement.

Motivation: Mathematical models take an important place in science and engineering.
A model can help scientists to explain dynamic behavior of a system and to understand
the functionality of system components. Since length of a time series and number of
replicates is limited by the cost of experiments, Boolean networks as a structurally simple
and parameter-free logical model for gene regulatory networks have attracted interests
of many scientists. In order to fit into the biological contexts and to lower the data
requirements, biological prior knowledge is taken into consideration during the inference
procedure. In the literature, the existing identification approaches can only deal with a
subset of possible types of prior knowledge.
Results: We propose a new approach to identify Boolean networks fromtime series data
incorporating prior knowledge, such as partial network structure, canalizing property,
positive and negative unateness. Using vector form of Boolean variables and applying
a generalized matrix multiplication called the semi-tensor product (STP), each Boolean
function can be equivalently converted into a matrix expression. Based on this, the
identification problem is reformulated as an integer linear programming problem to
reveal the system matrix of Boolean model in a computationally efficient way, whose
dynamics are consistent with the important dynamics captured in the data. By using
prior knowledge the number of candidate functions can be reduced during the inference.
Hence, identification incorporating prior knowledge is especially suitable for the case of
small size time series data and data without sufficient stimuli. The proposed approach is
illustrated with the help of a biological model of the network of oxidative stress response.
Conclusions: The combination of efficient reformulation of the identification problem
with the possibility to incorporate various types of prior knowledge enables the
application of computational model inference to systems with limited amount of time
series data. The general applicability of thismethodological approachmakes it suitable for
a variety of biological systems and of general interest for biological and medical research.

Asynchronous concurrency is a wide-spread way of writing programs that
deal with many short tasks. It is the programming model behind
event-driven concurrency, as exemplified by GUI applications, where the
tasks correspond to event handlers, web applications based around
JavaScript, the implementation of web browsers, but also of server-side
software or operating systems.
This model is widely used because it provides the performance benefits of
concurrency together with easier programming than multi-threading. While
there is ample work on how to implement asynchronous programs, and
significant work on testing and model checking, little research has been
done on handling asynchronous programs that involve heap manipulation, nor
on how to automatically optimize code for asynchronous concurrency.
This thesis addresses the question of how we can reason about asynchronous
programs while considering the heap, and how to use this this to optimize
programs. The work is organized along the main questions: (i) How can we
reason about asynchronous programs, without ignoring the heap? (ii) How
can we use such reasoning techniques to optimize programs involving
asynchronous behavior? (iii) How can we transfer these reasoning and
optimization techniques to other settings?
The unifying idea behind all the results in the thesis is the use of an
appropriate model encompassing global state and a promise-based model of
asynchronous concurrency. For the first question, We start from refinement
type systems for sequential programs and extend them to perform precise
resource-based reasoning in terms of heap contents, known outstanding
tasks and promises. This extended type system is known as Asynchronous
Liquid Separation Types, or ALST for short. We implement ALST in for OCaml
programs using the Lwt library.
For the second question, we consider a family of possible program
optimizations, described by a set of rewriting rules, the DWFM rules. The
rewriting rules are type-driven: We only guarantee soundness for programs
that are well-typed under ALST. We give a soundness proof based on a
semantic interpretation of ALST that allows us to show behavior inclusion
of pairs of programs.
For the third question, we address an optimization problem from industrial
practice: Normally, JavaScript files that are referenced in an HTML file
are be loaded synchronously, i.e., when a script tag is encountered, the
browser must suspend parsing, then load and execute the script, and only
after will it continue parsing HTML. But in practice, there are numerous
JavaScript files for which asynchronous loading would be perfectly sound.
First, we sketch a hypothetical optimization using the DWFM rules and a
static analysis.
To actually implement the analysis, we modify the approach to use a
dynamic analysis. This analysis, known as JSDefer, enables us to analyze
real-world web pages, and provide experimental evidence for the efficiency
of this transformation.