Darmstadt, Techn. Univ., Diss., 2000.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Climate change may cause epidemic threats as species spreading human diseases invades previously unpopulated areas. A species can establish in new areas if they fulfills an invasion criterion. Two common invasion criteria are the long-term exponential growth-rate, r, and the basic reproduction number, R0, that measures the population's exponential growth in time and growth between generations respectively. Previous work have determined the long-term exponential growth-rate of the mosquito Aedes aegypti, a vector spreading dengue, zika and yellow fever, in Europe. However, in epidemiology r is rarely used as invasion criterion, which makes their results difficult to communicate and interpret. A more commonly used invasion criterion is the basic reproduction number R0. From this number, public health receives information about high-risk areas where they can vaccinate the population and prevent the mosquitoes from establishing by reducing breeding habitats. Here we extend the previous work by developing a method to calculate R0 for Aedes aegypti and verify it by the results from previous studies. Using R0 as invasion criterion we then predict the global distribution of Aedes aegypti during different climate change scenarios in the 21st century. One related to high emissions of greenhouse gases, RCP8.5, and one to low emissions, RCP2.6. We predict that the distribution of Aedes aegypti will expand towards higher latitudes at great speed during the 21st century assuming the high emission scenario RCP8.5. Assuming the low emission scenario RCP2.6, the distribution will not reach higher latitudes at the end of the 21st century. In Europe, the distribution covered 1.8 % in the beginning of the 20th century and at the end of the 21st century the distribution will cover 10 % assuming RCP8.5 and 2.0 % assuming RCP2.6. This work underscores the importance of reducing global warming and to take other preventive actions to avoid major epidemic outbreaks. Since we also provide instructions and software to calculate both r and R0 for stage-structured models in periodic environments, we anticipate that this work will support more studies of this kind to better understand the epidemic threats from climate change.

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This study uses CFD methodology to simulate an advanced Diesel engine operated at higher than conventional peak cylinder pressures. The existing mathematical models for Diesel combustion, pollutant formation and wall heat transfer are improved and validated for this operating range. The fluid flow is described via the gas-phase Favre-averaged transport equations, governing the conservation of mass, chemical species, momentum and energy, based on the Eulerian continuum framework. These equations are closed by means of the k — e turbulence model. The liquid phase uses the Lagrangian approach, in which parcels, representing a class of droplets, are described by differential equations for the conservation of mass, momentum and energy. The numerical solution of the gas phase is obtained by the finite volume method applied to unstructured meshes with moving boundaries. Diesel ignition is modeled via a reduced kinetics mechanism, coupled with a characteristic timescale combustion model. Additionally, NOx and soot emissions are simulated. For the elevated cylinder temperatures and pressures, the behaviour of the thermophysical properties of the gases and liquids involved is critically examined. A near-wall treatment is applied accounting for the large gradients of thermophysical properties in the vicinity of the wall. Furthermore an alternative combined combustion and emissions modelling approach, RIF, based on the laminar flamelet concept is tested. The methodology is validated by reference to experimental data from a research engine, a constant volume pressure chamber and a high-pressure DI Diesel engine at various operating conditions. The modified near-wall treatment gives better agreement with the heat transfer measurements. The methodology predicts Diesel combustion evolution reasonably well for the elevated pressures. Best agreement was achieved using the LATCT combustion model combined with a NOx and soot model. The predictions of emissions show encouraging trends especially regarding the soot/NOx tradeoff, but require tuning of model coefficients.

<p> This thesis presents a space-time extended finite element method (space-time XFEM) based on the Heaviside enrichment for transient problems with moving interfaces, and its applications to the fluid-structure interaction (FSI) analysis. The Heaviside-enriched XFEM is a promising method to discretize partial differential equations with discontinuities in space. However, significant approximation errors are introduced by time stepping schemes when the interface geometry changes in time. The proposed space-time XFEM applies the finite element discretization and the Heaviside enrichment in both space and time with elements forming a space-time slab. A simple space-time scheme is introduced to integrate the weak form of the governing equations. This scheme considers spatial intersection configuration at multiple temporal integration points. Standard spatial integration techniques can be applied for each spatial configuration. Nitsche's method and the face-oriented ghost-penalty method are extended to the proposed space-time XFEM formulation. The stability, accuracy and flexibility of the space-time XFEM for various interface conditions including moving interfaces are demonstrated with structural and fluid problems. Moreover, the space-time XFEM enables analyzing complex FSI problems using moving interfaces, such as FSI with contact. Two FSI methods using moving interfaces (full-Eulerian FSI and Lagrangian-immersed FSI) are studied. The Lagrangian-immersed FSI method is a mixed formulation of Lagrangian and Eulerian descriptions. As solid and fluid meshes are independently defined, the FSI is computed between non-matching interfaces based on Nitsche's method and projection techniques adopted from computational contact mechanics. The stabilized Lagrange multiplier method is used for contact. Numerical examples of FSI and FSI-contact problems provide insight into the characteristics of the combination of the space-time XFEM and the Lagrangian-immersed FSI method. The proposed combination is a promising method which has the versatility for various multi-physics simulations and the applicability such as optimization.</p><p>

<p> The solute carrier family (&ldquo;SLC&rdquo;) is a diverse group of membrane transporter proteins expressed ubiquitously throughout the human body. SLC members have been heavily implicated in Mendelian disease, and play an active role in the pathogenesis of many cancers. Further, several members of the SLC family have ligands and/or precise functions that have yet to be elucidated. As such, examining the structure and function relationships of this family can have significant implication in the study and drug design of serious disease. We explored these structure and function relationships in three specific SLC members, with the goal of applying a homology modeling approach to both tool compound discovery and the examination of disease mechanisms. This study highlights the importance of homology modeling both in the exploration of the role SLC members play in human disease, and in human health overall. </p><p>

This thesis focuses on investigating the usage of statistical models for estimating fuel consumption of heavy duty vehicles. Several statistical models are assessed, along with machine learning using artificial neural networks. Data recorded by sensors on board trucks in the EU describe the operational usage of the vehicle. The usage of this data for estimating the fuel consumption is assessed, and several variables originating from the operational data is modelled and tested as possible input parameters. The estimation model for real world fuel consumption uses 8 parameters describing the operational usage of the vehicles, and 8 parameters describing the vehicles themselves. The operational parameters describe the average speed, topography, variation of speed, idling, and more. This model has an average relative error of 5.75%, with a prediction error less than 11.14% for 95% of all tested vehicles. When only vehicle parameters are considered, it is possible to make predictions with an average relative error of 9.30%, with a prediction error less than 19.50% for 95% of all tested vehicles. Furthermore, a computer software called Vehicle Energy Consumption Calculation tool(VECTO) must be used to simulate the fuel consumption for all heavy duty vehicles, according to legislation by the EU. Running VECTO is a slow process, and this thesis also investigates how well statistical models can be used to quickly estimate the VECTO fuel consumption. The model estimates VECTO fuel consumption with an average relative error of 0.32%and with a prediction error less than 0.65% for 95% of all tested vehicles. / Denna rapport fokuserar på att undersöka användningen av statistiska mod-eller för att uppskatta bränsleförbrukningen hos tunga fordon. Flera statistiska modeller utvärderas, tillsammans med maskinlärning med artificiella neurala nätverk. Data som registreras av sensorer ombord på Scania-lastbilar i EU beskriver fordonets drift. Användningen av dessa data för att uppskatta bränsleförbrukningen undersöks och flera variabler som kommer från operativa data modelleras och testas som möjliga in-parametrar. Uppskattningsmodellen för den verkliga bränsleförbrukningen använder 8 parametrar som beskriver användningen av fordonet och 8 parametrar som beskriver själva fordonet. Bland annat beskrivs medelhastighet, topografi, hastighetsvariation, andel tomgång. Denna modell har ett genomsnittligt relativfel på 5.75 %, med ett skattningsfel mindre än 11.14% för 95% av de de fordon som testats. Om endast fordonsparametrar beaktas som in-parametrar är det möjligt att göra skattningar med ett genomsnittligt relativfel på 9.30 %, med ett skattningsfel mindre än 19.50% för 95% av de de fordon som testats. Ett datorprogram kallat VECTO måste användas för att simulera bränsleförbrukningen för alla tunga fordon enligt EU-lagstiftning. Att köra VECTO är en tidskrävande process, och denna rapport undersöker också hur väl statistiska modeller kan användas för att snabbt uppskatta VECTO-bränsleförbrukningen. Modellen uppskattar VECTO-bränsleförbrukningen med ett genomsnittligt relativfel på 0.32% och med ett skattningsfel mindre än 0.65% för 95% av de de fordon som testats.

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