Computer-Aided Geometry Modeling

Techniques in computer-aided geometry modeling and their application are addressed. Mathematical modeling, solid geometry models, management of geometric data, development of geometry standards, and interactive and graphic procedures are discussed. The applications include aeronautical and aerospace structures design, fluid flow modeling, and gas turbine design

Tunable Laser-Plasma Acceleration with Ionization Injection

Accelerating electrons to relativistic energies by an intense laser field interacting with a plasma is a widely considered concept to drive future applications such as compact light sources. The strong requirements on the electron beam quality imposed by these applications requires to precisely control the injection and acceleration dynamics and hence the parameters of the laser-plasma accelerated electrons. This thesis studies electron beam generation with ionization injection in a nitrogen doped hydrogen plasma, focused on tunability and improvement of electron beam parameters. A capillary type plasma target was developed and characterized with Computational Fluid Dynamic (CFD) simulations allowing extensive parameter scans. It is demon- strated that electron beam parameters can be tuned in a wide range with peak energies between 200MeV and 350MeV, bunch charges between 100pC and 350pC at percent- level shot-to-shot stability, by varying the laser focus position, laser pulse energy, plasma density and the nitrogen concentration. The accelerator performance could be optimized by controlling beam loading effects with a combination of the nitrogen concentration and the laser pulse energy, resulting in electron beams with reduced energy spread at simultaneously increased peak charge density. The laser pulse energy showed the strongest influence on the transverse beam parameters, allowing to fine-tune beam divergence and beam emittance, a crucial prerequisite to optimize electron beams for the transport with electron beam optics. The broad parameter scans could be reproduced with Particle-In-Cell (PIC) simulations, providing an in-depth understanding of the injection and acceleration dynamics in the ionization injection scheme. The presented results and the identified scalings can give a guideline for the operation regime for future experiments and to develop improved plasma targets to further enhance the electron beam quality

Coupled parameterized reduced order modelling of thermomechanical phenomena arising in blast furnaces

Blast furnace operations are subjected to temperatures up to 1500º C, causing high thermal stresses in blast furnace hearth walls. First, an axisymmetric isotropic homogeneous model is introduced and solved using finite element method. Next, we introduced the relevant geometric parameters and material parameters. We used the Proper Orthogonal Decomposition (POD) to construct the reduced basis space. For the computation of degrees of freedom, we used Galerkin projection and artificial neural network. Further to the simplified model, we introduced the mathematical model characterised by temperature dependence of material properties and presence of different materials. Homogenization is used to identify an equivalent orthotropic material from the periodic assembly of homogeneous isotropic materials. Finite element formulation is used to solve the complex thermomechanical model. Finally, we extended the POD-artificial neural network approach to the complex thermomechanical model

ISGSR 2011 - Proceedings of the 3rd International Symposium on Geotechnical Safety and Risk

Scientific standards applicable to publication of BAWProceedings: http://izw.baw.de/publikationen/vzb_dokumente_oeffentlich/0/2020_07_BAW_Scientific_standards_conference_proceedings.pd

Design and analysis of multi-element antenna systems and agile radiofrequency frontends for automotive applications

Vehicular connectivity serves as one of the major enabling technologies for current applications like driver assistance, safety and infotainment as well as upcoming features like highly automated vehicles - all of which having certain quality of service requirements, e. g. datarate or reliability. This work focuses on vehicular integration of multiple-input-multiple-output (MIMO) capable multielement antenna systems and frequency-agile radio frequency (RF) front ends to cover current and upcoming connectivity needs. It is divided in four major parts. For each part, mostly physical layer effects are analyzed (any performance lost on physical layer, cannot be compensated in higher layers), sensitivities are identified and novel concepts are introduced based on the status-quo findings.Fahrzeugvernetzung dient als eine der wesentlichsten Befähigungstechnologien für moderne Fahrerassistenzsysteme und zukünftig auch hochautomatisiertes Fahren. Sowohl die heutigen als auch zukünftige Anwendungen haben besondere Dienstgüteanforderungen, z.B. in Bezug auf die Datenrate oder Verlässlichkeit. Im Rahmen dieser Arbeit wird die Integration von Mehrantennensystemen für MIMO-Funkanwendungen (MIMO: engl. Multiple Input Multiple Output) sowie von frequenzagilen Hochfrequenzfrontends im Fahrzeugumfeld untersucht, um so eine technische Grundlage für zukünftige Anforderungen an die automobile Vernetzung anbieten zu können. Die dabei gewonnenen Erkenntnisse lassen sich in vier Teile gliedern. Grundsätzlich konzentrieren sich die Untersuchungen vorrangig auf die physikalische Ebene. Auf Basis des aktuellen Status Quo werden Sensitivitäten herausgearbeitet, neue Konzepte hergeleitet und entwickelt

High-Dimensional Inference on Dense Graphs with Applications to Coding Theory and Machine Learning

We are living in the era of "Big Data", an era characterized by a voluminous amount of available data. Such amount is mainly due to the continuing advances in the computational capabilities for capturing, storing, transmitting and processing data. However, it is not always the volume of data that matters, but rather the "relevant" information that resides in it. Exactly 70 years ago, Claude Shannon, the father of information theory, was able to quantify the amount of information in a communication scenario based on a probabilistic model of the data. It turns out that Shannon's theory can be adapted to various probability-based information processing fields, ranging from coding theory to machine learning. The computation of some information theoretic quantities, such as the mutual information, can help in setting fundamental limits and devising more efficient algorithms for many inference problems. This thesis deals with two different, yet intimately related, inference problems in the fields of coding theory and machine learning. We use Bayesian probabilistic formulations for both problems, and we analyse them in the asymptotic high-dimensional regime. The goal of our analysis is to assess the algorithmic performance on the first hand and to predict the Bayes-optimal performance on the second hand, using an information theoretic approach. To this end, we employ powerful analytical tools from statistical physics. The first problem is a recent forward-error-correction code called sparse superposition code. We consider the extension of such code to a large class of noisy channels by exploiting the similarity with the compressed sensing paradigm. Moreover, we show the amenability of sparse superposition codes to perform joint distribution matching and channel coding. In the second problem, we study symmetric rank-one matrix factorization, a prominent model in machine learning and statistics with many applications ranging from community detection to sparse principal component analysis. We provide an explicit expression for the normalized mutual information and the minimum mean-square error of this model in the asymptotic limit. This allows us to prove the optimality of a certain iterative algorithm on a large set of parameters. A common feature of the two problems stems from the fact that both of them are represented on dense graphical models. Hence, similar message-passing algorithms and analysis tools can be adopted. Furthermore, spatial coupling, a new technique introduced in the context of low-density parity-check (LDPC) codes, can be applied to both problems. Spatial coupling is used in this thesis as a "construction technique" to boost the algorithmic performance and as a "proof technique" to compute some information theoretic quantities. Moreover, both of our problems retain close connections with spin glass models studied in statistical mechanics of disordered systems. This allows us to use sophisticated techniques developed in statistical physics. In this thesis, we use the potential function predicted by the replica method in order to prove the threshold saturation phenomenon associated with spatially coupled models. Moreover, one of the main contributions of this thesis is proving that the predictions given by the "heuristic" replica method are exact. Hence, our results could be of great interest for the statistical physics community as well, as they help to set a rigorous mathematical foundation of the replica predictions