Diese Arbeit beschäftigt sich mit der Untersuchung der Ligandeneinflüsse bei Verbindungen mit alpha-P4S3-, alpha-P4Se3- und P3Se4-Gerüst. Dazu wurden die Verbindungen alpha-P4S3L2 (L = N(C3H7)(C6H5), NC9H10, SeCH3, SeC6H5),alpha-P4Se3L2 (L = NH(C6H5), N(CH3)(C6H5), N(C2H5)(C6H5), N(C3H7)(C6H5),NC9H10, SCH3, SeCH3) und P3Se4L (L = NH(C6H5), NH(C13H9), NH(C25H19),N(CH3)(C6H5), N(C2H5)(C6H5), N(C3H7)(C6H5), NC9H10, SCH3, SeCH3, SeC6H5)synthetisiert und die 31P-NMR-Parameter bestimmt. Durch Vergleich mit31P-NMR-Daten aus der Literatur konnten die Ligandeneinflüsse untersucht werden. Dabei ergibt sich, daß der Einfluß am ligandentragenden Phosphoratom erwartungsgemäß am größten ist. Die 1J(AB)-Kopplungen der drei Käfigmolekülfamilien mit jeweils gleichem Substituenten unterscheiden sich nur wenig. Der Einfluß des Liganden auf die31P-NMR-Parameter ist bei den P3Se4-Derivaten am kleinsten. Die Umsetzung von P3Se4I mit den sekundären Aminen HN(CH3)2, HN(C2H5)2 und HN(CH3)(C6H5) in Toluol führt zu den Verbindungen alpha-P4Se3(SeP(Se)NR2)2 (NR2 = N(CH3)2,N(C2H5)2) und P3Se4(SeP(Se)NR2) (NR2 = N(CH3)2, N(C2H5)2 undN(CH3)(C6H5)). Bei den Verbindungen mit P3Se4-Gerüst handelt es sich um fluktuierende Systeme. Die Fluktuationsraten wurden bei verschiedenen Temperaturen bestimmt.

This paper investigates the impact of the European Union membership referendum in the UK on the correlations and volatility between three different broad stock market indices, utilizing an econometric time series model called DCC GARCH. Findings support the claim of higher volatility peaks on the stock market as an immediate response to the event. Evidence indicate higher shortrun correlations between the indices as a response to higher volatility. In addition, the study present evidence that the correlation between the UK stock index and the other two indices declines after the referendum in 2016. / Studien undersöker konsekvenserna av folkomröstningen om Storbritanniens medlemskap i EU. Korrelationen och volatiliteten mellan tre olika aktiemarknadsindex jämförs med hjälp av en ekonometrisk modell för tidsserier kallad DCC GARCH. Resultaten från studien visar på omedelbart högre nivåer av volatilitet på aktiemarknaden dagarna efter omröstningen. Analysen ger stöd för hypotesen om högre nivåer av kortsiktiga korrelationer mellan indexen som en konsekvens av högre nivåer av volatilitet. Resultat visar även på att korrelationen mellan det brittiska aktieindexet och de övriga två minskar efter det undersökta eventet.

Society depends on unbiased, efficient and replicable measurement tools to tell us more truthfully what is happening when our senses would otherwise fool us. A new approach is made to consistently detect the start and end of historic recessions as defined by the US Federal Reserve. To do this, three measures, correlation (Spearman and Pearson), Baur comovement and Kolmogorov complexity, are used to quantify market behaviour to detect recessions. To compare the effectiveness of each measure the normalized correct Area Under Curve (AUC) fraction is introduced. It is found that for all three measures, the performance is mostly dependent on the type of data and that financial market data does not perform as good as fundamental economical data to detect recessions. Furthermore, comovement is found to be the most efficient individual measure and also most efficient of all measures when compared against several measures merged together. / Samhället är beronde förväntningsriktiga, effektiva och replikerbara mätverktyg för att mer sanningsenligt informera vad som händer när våra sinnen lurar oss. Ett nytt tillvägagångssätt utvecklas för att konsekvent uppmäta början och slut av historiska lågkonjunkturer så som definierats av US Federal Reserve. För att göra detta används tre mätmetoder, korrelation (Spearman och Pearson), Baur comovement och Kolmogorovkomplexitet, för att kvantifiera marknadsbeteendet i avsikt att upptäcka lågkonjunkturer. För att jämföra effektiviteten hos varje metod introduceras normalized correct Area Under Curve (AUC) fraktionen. Det konstateras att effektiviteten hos alla tre metoder är främst beroende av vilken typ av data som används och att finansiell data inte fungerar lika bra som real ekonomiska data för att upptäcka lågkonjunkturer. Vidare visas att comovement är den mest effektiva individualla mätmetoden och även den mest effektiva metoden jämfört med sammanslagna metoder

In dieser Bachelorarbeit wird eine LTE Cell Search Procedure auf Basis von SDR entwickelt, um eine Synchronisation mit einem LTE-System in Zeit und Frequenz zu erreichen. Dabei werden mehrere Verarbeitungsblöcke implementiert, die zur LTE Cell Search Procedure gehören. Diese beinhalten die Erkennung des Integer Frequency Offsets (IFO), Primary Synchronisation Signals (PSS), Fractional Frequency Offset (FFO) und des Secondary Synchronisation Signals (SSS). Die Arbeit umfasst eine Literaturrecherche, die Implementierung der Verarbeitungsblöcke, die Simulation verschiedener Szenarien, statistische Analysen und die Anwendung auf ein gemessenes LTE-Signal. Sie liefert Antworten auf spezifische Forschungsfragen zur Effizienz, zum Einfluss des SNR und des CFO, sowie zur Eignung für reale LTE-Systeme. Darüber hinaus wird ein Ausblick auf mögliche Anwendungen im Kontext von 5G NR, WLAN und Erweiterungen gegeben.:Kurzfassung.................................... III Abbildungsverzeichnis .............................. VII Tabellenverzeichnis................................ VIII Codeverzeichnis.................................. IX Abkürzungsverzeichnis .............................. X Symbolverzeichnis ................................ XIV 1. Einleitung................................... 1 1.1. Forschungsfragen ............................ 1 1.2. Untersuchungsdesign .......................... 2 2. Grundlagen .................................. 3 2.1. Zadoff-Chu (ZC)-Sequenzen ...................... 3 2.2. Maximum Length (M)-Sequenzen................... 5 2.3. Orthogonal Frequency-Division Multiplexing (OFDM) ................ 7 2.4. Orthogonal Frequency-Division Multiplexing (OFDM): Kanal und Equalization............................... 10 2.5. Orthogonal Frequency-Division Multiplexing (OFDM): Carrier Frequency Offset (CFO).......................... 11 2.6. Carrier Frequency Offset (CFO)-Erkennung ................... 14 2.7. Short Time Fourier Transform (STFT) und Spektrogramm ......... 16 2.8. Software Defined Radio (SDR) .................... 17 2.9. 3rd Generation Partnership Project (3GPP)-Long Term Evolution (LTE).................................. 18 3. Praktische Untersuchung ........................... 23 3.1. Simulationsumgebung ......................... 27 3.2. Software Defined Radio (SDR)-basierte Long Term Evolution (LTE)- Messung................................. 29 3.3. Integer Carrier Frequency Offset (IFO)-Erkennung ............ 30 3.4. Primary Synchronization Signal (PSS)-Erkennung .............. 38 3.5. Fractional Carrier Frequency Offset (FFO)-Erkennung ........ 44 3.6. Secondary Synchronization Signal (SSS)-Erkennung ............ 49 3.7. Simulation der gesamten Implementierung .................... 57 4. Zusammenfassung und Ausblick ....................... 62 Literaturverzeichnis................................ 65

When modelling more that one asset, it is desirable to apply multivariate modeling to capture the co-movements of the underlying assets. The GARCH models has been proven to be successful when it comes to volatility forecast- ing. Hence it is natural to extend from a univariate GARCH model to a multivariate GARCH model when examining portfolio volatility. This study aims to evaluate a specific multivariate GARCH model, the DCC-GARCH model, which was developed by Engle and Sheppard in 2001. In this pa- per different DCC-GARCH models have been implemented, assuming both Gaussian and multivariate Student’s t distribution. These distributions are compared by a set of tests as well as Value at Risk backtesting. / I portföljanalys så är det åtråvärt att applicera flerdimensionella modeller för att kunna fånga hur de olika tillgångarna rör sig tillsammans. GARCH-modeller har visat sig vara framgångsrika när det kommer till prognoser av volatilitet. Det är därför naturligt att gå från endimensionella till flerdimensionella GARCH-modeller när volatiliteten av en portfölj skall utvärderas. Den här studien ämnar att utvärdera tillvägagångssättet för prognoser av en viss typ av flerdimensionell GARCH-modell, DCC-GARCH-modellen, vilken utvecklades av Engle och Sheppard 2001. I den här uppsatsen har olika DCC-GARCH modeller blivit implementerade, som antar innovationer enligt både flerdimensionell normalfördelning samt flerdimensionell student's t-fördelning. Dessa jämförs med hjälp av en handfull tester samt Value-at-Risk backtesting.

Syfte  Syftet med arbetet var att undersöka sambandet mellan träningsmängd och prestation för svenska konståkare i A- och Nationella serien inom klasserna junior och senior. Det arbetet syftat till att ta reda på var följande frågeställningar:  Finns det något samband mellan det totala antalet träningstimmar per vecka och uppnådd poäng på tävling? Finns det något samband mellan isträningstimmar per vecka och uppnådd poäng på tävling? Finns det något samband mellan fysträningstimmar per vecka och uppnådd poäng på tävling?  Hur väl kan den totala träningsmängden förklara den uppnådda poängen på tävling? Metod Data samlades in med hjälp av en webbenkät skapad i Sunet Survey. Frågorna i enkäten handlade om tävlingsklass, total träningsmängd per vecka, totalt antal istimmar per vecka, totalt antal fystimmar per vecka, annan idrott och eventuell skada. Enkäterna mejlades ut till de 120 klubbarna i Sverige med åkare i urvalsgruppen. De statistiska testerna genomfördes i programmet Jamovi där Pearsons sambandsanalys användes för att undersöka sambanden. Utifrån r-värdet kunde även determinationskoefficienten (r2) beräknas genom att ta kvadratroten av korrelationskoefficienten (r). Den valda signifikansnivån för analysen var 0,05. Resultat Totalt samlades 73 enkäter in varvid 66 inkluderades i studiens slutgiltiga analys. Resultatet från de statistiska testerna visade inget samband mellan total träningsmängd och uppnådd poäng på tävling (p=0,097), mängden isträning och uppnådd poäng på tävling (p=0,094) eller mängden fysträning och uppnådd poäng på tävling (p=0,088). 4,2 procent av den uppnådda poängen kunde förklaras av den totala träningsmängden (r2 = 0,042). Slutsats Resultaten från studien visar att det inte finns något samband mellan total träningsmängd och uppnådd poäng på tävling. Tränare som avser förbättra prestationen hos sina åkare bör överväga att reducera mängden träning med tanke på skaderisken och i stället se över träningens innehåll. Det tycks således vara andra faktorer än träningsmängden som är avgörande för prestation hos konståkare på A- och Nationell serienivå. / Aim  The aim of this study was to research the relationship between training load and performance for Swedish figure skaters in the A- and National series within the junior and senior category. The study aims to research the following questions:  Is there any relationship between the total amount of training hours per week and achieved score at competition?  Is there any relationship between the amount of training hours on ice per week and achieved score at competition?  Is there any relationship between the amount of training hours off ice per week and achieved score at competition?  How well can the total training load explain the achieved score at competition? Method  Data was collected using a web survey created in Sunet Survey. The questions in the survey focused on competition level, total training hours per week, total training hours on ice per week, total training hours off-ice per week, other sports and possible injury. The surveys were emailed to 120 clubs in Sweden with skaters in the sample group. The statistical tests were completed in the program Jamovi and Person’s correlation analysis was used to study the relationships. Based on the r-value the coefficient of determination (r2) could be calculated by taking the square root of the correlation coefficient (r). The chosen significance level for the analysis was 0,05.  Results  In total 73 surveys were collected and 66 of them was included in the study’s final analysis. The results from the statistical tests showed no relationship between total training load and achieved score at competition (p=0,097), the amount of ice training and achieved score at competition (p=0,094) or the amount of off-ice training and achieved score at competition (p=0,088). 4,2 percent of the achieved score at competition could be explained by the total training load (r2 = 0,042).  Conclusion  The results from this study shows that there is no relationship between total training load and achieved score at competition. Coaches aiming to enhance performance for their skaters should consider reducing the training load considering the risk of injury and instead focus on the training content. It seems to be other factors than training load that are crucial for performance for figure skaters at A- and National series level.

The design and optimization of entrained flow gasifiers is conducted more and more via computational fluid dynamics (CFD). A detailed resolution of single coal particles within such simulations is nowadays not possible due to computational limitations. Therefore the coal particle conversion is often represented by simple 0-D models. For an optimization of such 0-D models a precise understanding of the physical processes at the boundary layer and within the particle is necessary. In real gasifiers the particles experience Reynolds numbers up to 10000. However in the literature the conversion of coal particles is mainly regarded under quiescent conditions. Therefore an analysis of the conversion of single particles is needed. Thereto the computational fluid dynamics can be used. For the detailed analysis of single reacting particles under flow conditions a CFD model is presented. Practice-oriented parameters as well as features of the CFD model result from CFD simulations of a Siemens 200MWentrained flow gasifier. The CFD model is validated against an analytical model as well as two experimental data-sets taken from the literature. In all cases good agreement between the CFD and the analytics/experiments is shown. The numerical model is used to study single moving solid particles under combustion conditions. The analyzed parameters are namely the Reynolds number, the ambient temperature, the particle size, the operating pressure, the particle shape, the coal type and the composition of the gas. It is shown that for a wide range of the analyzed parameter range no complete flame exists around moving particles. This is in contrast to observations made by other authors for particles in quiescent atmospheres. For high operating pressures, low Reynolds numbers, large particle diameters and high ambient temperatures a flame exists in the wake of the particle. The impact of such a flame on the conversion of the particle is low. For high steam concentrations in the gas a flame appears, which interacts with the particle and influences its conversion. Furthermore the impact of the Stefan-flow on the boundary layer of the particle is studied. It is demonstrated that the Stefan-flow can reduce the drag coefficient and the Nusselt number for several orders of magnitude. On basis of the CFD results two new correlations are presented for the drag coefficient and the Nusselt number. The comparison between the correlations and the CFD shows a significant improvement of the new correlations in comparison to archived correlations. The CFD-model is further used to study moving single porous particles under gasifying conditions. Therefore a 2-D axis-symmetric system of non-touching tori as well as a complex 3-D geometry based on the an inverted settlement of monodisperse spheres is utilized. With these geometries the influence of the Reynolds number, the ambient temperature, the porosity, the intrinsic surface and the size of the radiating surface is analyzed. The studies show, that the influence of the flow on the particle conversion is moderate. In particular the impact of the flow on the intrinsic transport and conversion processes is mainly negligible. The size of the radiating surface has a similar impact on the conversion as the flow in the regarded parameter range. On basis of the CFD calculations two 0-D models for the combustion and gasification of moving particles are presented. These models can reproduce the results predicted by the CFD sufficiently for a wide parameter range.

The design and optimization of entrained flow gasifiers is conducted more and more via computational fluid dynamics (CFD). A detailed resolution of single coal particles within such simulations is nowadays not possible due to computational limitations. Therefore the coal particle conversion is often represented by simple 0-D models. For an optimization of such 0-D models a precise understanding of the physical processes at the boundary layer and within the particle is necessary. In real gasifiers the particles experience Reynolds numbers up to 10000. However in the literature the conversion of coal particles is mainly regarded under quiescent conditions. Therefore an analysis of the conversion of single particles is needed. Thereto the computational fluid dynamics can be used. For the detailed analysis of single reacting particles under flow conditions a CFD model is presented. Practice-oriented parameters as well as features of the CFD model result from CFD simulations of a Siemens 200MWentrained flow gasifier. The CFD model is validated against an analytical model as well as two experimental data-sets taken from the literature. In all cases good agreement between the CFD and the analytics/experiments is shown. The numerical model is used to study single moving solid particles under combustion conditions. The analyzed parameters are namely the Reynolds number, the ambient temperature, the particle size, the operating pressure, the particle shape, the coal type and the composition of the gas. It is shown that for a wide range of the analyzed parameter range no complete flame exists around moving particles. This is in contrast to observations made by other authors for particles in quiescent atmospheres. For high operating pressures, low Reynolds numbers, large particle diameters and high ambient temperatures a flame exists in the wake of the particle. The impact of such a flame on the conversion of the particle is low. For high steam concentrations in the gas a flame appears, which interacts with the particle and influences its conversion. Furthermore the impact of the Stefan-flow on the boundary layer of the particle is studied. It is demonstrated that the Stefan-flow can reduce the drag coefficient and the Nusselt number for several orders of magnitude. On basis of the CFD results two new correlations are presented for the drag coefficient and the Nusselt number. The comparison between the correlations and the CFD shows a significant improvement of the new correlations in comparison to archived correlations. The CFD-model is further used to study moving single porous particles under gasifying conditions. Therefore a 2-D axis-symmetric system of non-touching tori as well as a complex 3-D geometry based on the an inverted settlement of monodisperse spheres is utilized. With these geometries the influence of the Reynolds number, the ambient temperature, the porosity, the intrinsic surface and the size of the radiating surface is analyzed. The studies show, that the influence of the flow on the particle conversion is moderate. In particular the impact of the flow on the intrinsic transport and conversion processes is mainly negligible. The size of the radiating surface has a similar impact on the conversion as the flow in the regarded parameter range. On basis of the CFD calculations two 0-D models for the combustion and gasification of moving particles are presented. These models can reproduce the results predicted by the CFD sufficiently for a wide parameter range.:List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .XIII Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIX 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 1.1 State of the Art in Carbon Conversion Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Combustion of Solid Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Gasification of Porous Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Classification of the Present Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 1.3 Overview of the Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 2 Basic Theory and Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Geometry and Length Scales of Coal Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 2.2 Conditions in a Siemens Like 200 MW Entrained Flow Gasifier . . . . . . . . . . . . . . . . . . . . 11 2.2.1 Velocity Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.2.2 Temperature Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Particle Volume Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19 2.3 Time Scales of the Physical Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 Basic Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21 2.5 Conservation Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6 Gas Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 2.7 Boundary Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.8 Numerics and Solution Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30 2.9 Mesh and Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31 3 CFD-based Oxidation Modeling of a Non-Porous Carbon Particle . . . . . . . . . . . . . . . . . . . . .37 3.1 Chemical Reaction System for Combustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 3.1.1 Heterogeneous Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 3.1.2 Homogeneous Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40 3.1.3 Comparison of the Semi-Global vs. Reduced Reaction Mechanisms for the Gas Phase . .41 3.2 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43 3.2.1 Validation Against an Analytical Solution of the Two-Film Model . . . . . . . . . . . . . . . . . .43 3.2.2 Validation Against Experiments I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.3 Validation Against Experiments II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49 3.3 Influence of Ambient Temperature and Reynolds Number . . . . . . . . . . . . . . . . . . . . . . . .51 3.4 Influence of Heterogeneous Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Influence of Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61 3.6 Influence of Operating Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66 3.7 Influence of Particle Diameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70 3.8 The influence of Particle Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.9 Impact of Stefan Flow on the Boundary Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.9.1 Impact of Stefan Flow on the Drag Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83 3.9.2 Impact of Stefan Flow on the Nusselt and Sherwood Number . . . . . . . . . . . . . . . . . . . .85 3.10 Single-Film Sub-Model vs. CFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4 CFD-based Numerical Modeling of Partial Oxidation of a Porous Carbon Particle . . . . . . . . . .99 4.1 Chemical Reaction System for Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.1.1 Heterogeneous Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100 4.1.2 Homogeneous Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.2 Two-Dimensional Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.1 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.2 Influence of Reynolds Number and Ambient Temperature . . . . . . . . . . . . . . . . . . . . . .109 4.2.3 Influence of Porosity and Internal Surface . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 120 4.3 Comparative Three-Dimensional Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 4.3.1 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126 4.3.2 Results of the 3-D Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 4.4 Extended Sub-Model for Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .141 5.1 Summary of This Work . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .141 5.2 Recommendations for Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145 6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.1 Appendix I: Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.2 Appendix II: Two-Film Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 6.3 Appendix III: Sub-Model for the Combustion of Solid Particles . . . . . . . . . . . . . . . . . . . . 160 6.4 Appendix IV: Sub-Model for the Gasification of Porous Particles . . . . . . . . . . . . . . . . . . . 161

Das vorliegende Skript ist aus dem Lehrveranstaltungszyklus Theorie und Methodik der Statistik hervorgegangen, den ich an der Fakultät Wirtschaftswissenschaften der TU Dresden im Hauptstudium der Diplomstudiengänge und in den Masterstudiengängen gehalten habe. Es gibt 16 ältere Auflagen, die während mehr als 15 Jahren kontinuierlich überarbeitet, erweitert und korrigiert wurden. Für Hinweise auf Fehler und für Erweiterungs- und Verbesserungsvorschläge danke ich mehreren Studenten und allen Mitarbeitern, die in dieser Zeit am Lehrstuhl für Quantitative Verfahren, insbesondere Statistik beschäftigt waren. Die 30 Kapitel verteilen sich auf die fünf Teile Grundlagen, Schätzen und Testen, Korrelation und Regression, Stochastische Prozesse und Multivariate Verfahren, die jeweils Grundlage einer Lehrveranstaltung waren. Viele Kapitel haben einen abschließenden Abschnitt "Weiterführendes" mit zusätzlichem und ergänzendem Material, das in der Vorlesung nicht behandelt wurde, sowie zusätzlichen Eräauterungen, Beispielen und Anmerkungen. Zur verwendeten Notation und zu mathematischen Grundlagen siehe Anhang A.

Diese Arbeit enthält drei unabhängige Kapitel, jedes davon im Bereich der Dynamischen Vertragstheorie. Kapitel I zeigt, dass deterministische dynamische Prinzipal-Agenten-Verträge immer mindestens so ertragreich sind wie stochastische, falls die sogenannte Methode erster Ordnung des dynamischen Mechanismus-Designs erfüllt ist. Meine Ergebnisse legen dar, dass die in der Literatur übliche Einschränkung auf deterministische Verträge zulässig ist, so lange die Methode erster Ordnung gültig ist. Kapitel II basiert auf einer gemeinsamen Arbeit mit Ilia Krasikov und Rohit Lamba. Ein Großanbieter (Prinzipal) handelt mit einer kleinen Firma (Agent) einen wiederkehrenden Geschäftsvertrag aus, wobei sich der Agent als ungeduldiger erweist. Der optimale Vertrag wird durch zwei Schlüsseleigenschaften beschrieben: Neustart und Abbruch, die vielerlei Eigenschaften der angebotenen Verträge darlegen. Kapitel III basiert schließlich auf einer gemeinsamen Arbeit mit Rohit Lamba. Darin besitzt der Agent dynamische private Information, die einem Markovprozess folgt. Ein monopolistischer Prinzipal verkauft dem Agenten ein nicht-dauerhaftes Gut und er verpflichtet sich in jeder Periode an den ursprünglich ausgehandelten Vertrag. Die entstehenden Informationskosten verhindern erst-beste Verträge bei auftretender Persistenz im Typ des Agenten. Diese Arbeit stellt einen Weg bereit, wie man den optimalen deterministischen Vertrag in dynamischen Prinzipal-Agenten-Modellen erhält. Der gewöhnliche Weg mit lediglich lokal nach unten bindenden Anreizverträglichkeitsbedingungen misslingt bei hoher Persistenz der Typrealisierungen und bei stark differenzierender Diskontierung. Zudem zeigt die Arbeit wann mit Gewissheit stochastische Verträge ausgeschlossen werden können. / This dissertation consists of three independent chapters, each in the field of dynamic contracting. Chapter I shows that deterministic dynamic contracts between a principal and an agent are always at least as profitable to the principal as stochastic ones, if the so-called first-order approach in dynamic mechanism design is satisfied. My results demonstrate that the usual restriction in the literature to deterministic contracts is admissible, as long as the first-order approach is valid. Chapter II is based on joint work with Ilia Krasikov and Rohit Lamba. We consider a large supplier (principal) who contracts with a small firm (agent) to repeatedly provide working capital in return for payments. The agent is less patient than the principal. The optimal contract is characterized by two key properties: restart and shutdown, which capture various aspects of contracts offered in the marketplace. Finally, Chapter III is based on joint work with Rohit Lamba. We consider the problem of optimal contracting where the agent has dynamic private information, which follows a Markov process. In each period, a monopolistic principal sells a nondurable good to the agent and she commits to the contract she made in the initial period. The emerging information costs prevent first-best contracts whenever there is persistency in the agent’s type. This thesis provides a strategy on how to obtain the optimal deterministic contract in dynamic principal-agent models with Markovian type realizations. We see that the usual approach with only local downward binding incentive compatibility constraints does not work for highly persistent type realizations and for large differences in discounting. Furthermore, I show in which situations we with certainty can exclude stochastic contracts.