Simulation by discrete mass modeling of offshore wind turbine system with DC link

This paper presents an integrated model for an offshore wind turbine taking into consideration a contribution for the marine wave and wind speed with perturbations influences on the power quality of current injected into the electric grid. The paper deals with the simulation of one floating offshore wind turbine equipped with a permanent magnet synchronous generator, and a two-level converter connected to an onshore electric grid. The use of discrete mass modeling is accessed in order to reveal by computing the total harmonic distortion on how the perturbations of the captured energy are attenuated at the electric grid injection point. Two torque actions are considered for the three-mass modeling, the aerodynamic on the flexible part and on the rigid part of the blades. Also, a torque due to the influence of marine waves in deep water is considered. Proportional integral fractional-order control supports the control strategy. A comparison between the drive train models is presented.

Offshore Wind Energy System with DC Transmission Discrete Mass: Modeling and Simulation

This paper presents an integrated model for an offshore wind energy system taking into consideration a contribution for the marine wave and wind speed with perturbations influences on the power quality of current injected into the electric grid. The paper deals with the simulation of one floating offshore wind turbine equipped with a PMSG and a two-level converter connected to an onshore electric grid. The use of discrete mass modeling is accessed in order to reveal by computing the THD on how the perturbations of the captured energy are attenuated at the electric grid injection point. Two torque actions are considered for the three-mass modeling, the aerodynamic on the flexible part and on the rigid part of the blades. Also, a torque due to the influence of marine waves in deep water is considered. PI fractional-order control supports the control strategy. A comparison between the drive train models is presented.

Treeline and climate change : analyzing and modeling patterns and shifts in the Swiss Alps

Summary: Global warming has led to an average earth surface temperature increase of about 0.7 °C in the 20th century, according to the 2007 IPCC report. In Switzerland, the temperature increase in the same period was even higher: 1.3 °C in the Northern Alps anal 1.7 °C in the Southern Alps. The impacts of this warming on ecosystems aspecially on climatically sensitive systems like the treeline ecotone -are already visible today. Alpine treeline species show increased growth rates, more establishment of young trees in forest gaps is observed in many locations and treelines are migrating upwards. With the forecasted warming, this globally visible phenomenon is expected to continue. This PhD thesis aimed to develop a set of methods and models to investigate current and future climatic treeline positions and treeline shifts in the Swiss Alps in a spatial context. The focus was therefore on: 1) the quantification of current treeline dynamics and its potential causes, 2) the evaluation and improvement of temperaturebased treeline indicators and 3) the spatial analysis and projection of past, current and future climatic treeline positions and their respective elevational shifts. The methods used involved a combination of field temperature measurements, statistical modeling and spatial modeling in a geographical information system. To determine treeline shifts and assign the respective drivers, neighborhood relationships between forest patches were analyzed using moving window algorithms. Time series regression modeling was used in the development of an air-to-soil temperature transfer model to calculate thermal treeline indicators. The indicators were then applied spatially to delineate the climatic treeline, based on interpolated temperature data. Observation of recent forest dynamics in the Swiss treeline ecotone showed that changes were mainly due to forest in-growth, but also partly to upward attitudinal shifts. The recent reduction in agricultural land-use was found to be the dominant driver of these changes. Climate-driven changes were identified only at the uppermost limits of the treeline ecotone. Seasonal mean temperature indicators were found to be the best for predicting climatic treelines. Applying dynamic seasonal delimitations and the air-to-soil temperature transfer model improved the indicators' applicability for spatial modeling. Reproducing the climatic treelines of the past 45 years revealed regionally different attitudinal shifts, the largest being located near the highest mountain mass. Modeling climatic treelines based on two IPCC climate warming scenarios predicted major shifts in treeline altitude. However, the currently-observed treeline is not expected to reach this limit easily, due to lagged reaction, possible climate feedback effects and other limiting factors. Résumé: Selon le rapport 2007 de l'IPCC, le réchauffement global a induit une augmentation de la température terrestre de 0.7 °C en moyenne au cours du 20e siècle. En Suisse, l'augmentation durant la même période a été plus importante: 1.3 °C dans les Alpes du nord et 1.7 °C dans les Alpes du sud. Les impacts de ce réchauffement sur les écosystèmes - en particuliers les systèmes sensibles comme l'écotone de la limite des arbres - sont déjà visibles aujourd'hui. Les espèces de la limite alpine des forêts ont des taux de croissance plus forts, on observe en de nombreux endroits un accroissement du nombre de jeunes arbres s'établissant dans les trouées et la limite des arbres migre vers le haut. Compte tenu du réchauffement prévu, on s'attend à ce que ce phénomène, visible globalement, persiste. Cette thèse de doctorat visait à développer un jeu de méthodes et de modèles pour étudier dans un contexte spatial la position présente et future de la limite climatique des arbres, ainsi que ses déplacements, au sein des Alpes suisses. L'étude s'est donc focalisée sur: 1) la quantification de la dynamique actuelle de la limite des arbres et ses causes potentielles, 2) l'évaluation et l'amélioration des indicateurs, basés sur la température, pour la limite des arbres et 3) l'analyse spatiale et la projection de la position climatique passée, présente et future de la limite des arbres et des déplacements altitudinaux de cette position. Les méthodes utilisées sont une combinaison de mesures de température sur le terrain, de modélisation statistique et de la modélisation spatiale à l'aide d'un système d'information géographique. Les relations de voisinage entre parcelles de forêt ont été analysées à l'aide d'algorithmes utilisant des fenêtres mobiles, afin de mesurer les déplacements de la limite des arbres et déterminer leurs causes. Un modèle de transfert de température air-sol, basé sur les modèles de régression sur séries temporelles, a été développé pour calculer des indicateurs thermiques de la limite des arbres. Les indicateurs ont ensuite été appliqués spatialement pour délimiter la limite climatique des arbres, sur la base de données de températures interpolées. L'observation de la dynamique forestière récente dans l'écotone de la limite des arbres en Suisse a montré que les changements étaient principalement dus à la fermeture des trouées, mais aussi en partie à des déplacements vers des altitudes plus élevées. Il a été montré que la récente déprise agricole était la cause principale de ces changements. Des changements dus au climat n'ont été identifiés qu'aux limites supérieures de l'écotone de la limite des arbres. Les indicateurs de température moyenne saisonnière se sont avérés le mieux convenir pour prédire la limite climatique des arbres. L'application de limites dynamiques saisonnières et du modèle de transfert de température air-sol a amélioré l'applicabilité des indicateurs pour la modélisation spatiale. La reproduction des limites climatiques des arbres durant ces 45 dernières années a mis en évidence des changements d'altitude différents selon les régions, les plus importants étant situés près du plus haut massif montagneux. La modélisation des limites climatiques des arbres d'après deux scénarios de réchauffement climatique de l'IPCC a prédit des changements majeurs de l'altitude de la limite des arbres. Toutefois, l'on ne s'attend pas à ce que la limite des arbres actuellement observée atteigne cette limite facilement, en raison du délai de réaction, d'effets rétroactifs du climat et d'autres facteurs limitants.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Consistent Dirichlet Boundary Conditions for Numerical Solution of Moving Boundary Problems

We consider the imposition of Dirichlet boundary conditions in the finite element modelling of moving boundary problems in one and two dimensions for which the total mass is prescribed. A modification of the standard linear finite element test space allows the boundary conditions to be imposed strongly whilst simultaneously conserving a discrete mass. The validity of the technique is assessed for a specific moving mesh finite element method, although the approach is more general. Numerical comparisons are carried out for mass-conserving solutions of the porous medium equation with Dirichlet boundary conditions and for a moving boundary problem with a source term and time-varying mass.

Body Mass Index, Cigarette Smoking, and Alcohol Consumption and Cancers of the Oral Cavity, Pharynx, and Larynx: Modeling Odds Ratios in Pooled Case-Control Data

study-specific results, their findings should be interpreted with caution

Body mass index, cigarette smoking, and alcohol consumption and cancers of the oral cavity, pharynx, and larynx: modeling odds ratios in pooled case-control data.

Odds ratios for head and neck cancer increase with greater cigarette and alcohol use and lower body mass index (BMI; weight (kg)/height(2) (m(2))). Using data from the International Head and Neck Cancer Epidemiology Consortium, the authors conducted a formal analysis of BMI as a modifier of smoking- and alcohol-related effects. Analysis of never and current smokers included 6,333 cases, while analysis of never drinkers and consumers of < or =10 drinks/day included 8,452 cases. There were 8,000 or more controls, depending on the analysis. Odds ratios for all sites increased with lower BMI, greater smoking, and greater drinking. In polytomous regression, odds ratios for BMI (P = 0.65), smoking (P = 0.52), and drinking (P = 0.73) were homogeneous for oral cavity and pharyngeal cancers. Odds ratios for BMI and drinking were greater for oral cavity/pharyngeal cancer (P < 0.01), while smoking odds ratios were greater for laryngeal cancer (P < 0.01). Lower BMI enhanced smoking- and drinking-related odds ratios for oral cavity/pharyngeal cancer (P < 0.01), while BMI did not modify smoking and drinking odds ratios for laryngeal cancer. The increased odds ratios for all sites with low BMI may suggest related carcinogenic mechanisms; however, BMI modification of smoking and drinking odds ratios for cancer of the oral cavity/pharynx but not larynx cancer suggests additional factors specific to oral cavity/pharynx cancer.

Regularity and mass conservation for discrete coagulation-fragmentation equations with diffusion

We present a new a-priori estimate for discrete coagulation fragmentation systems with size-dependent diffusion within a bounded, regular domain confined by homogeneous Neumann boundary conditions. Following from a duality argument, this a-priori estimate provides a global L2 bound on the mass density and was previously used, for instance, in the context of reaction-diffusion equations. In this paper we demonstrate two lines of applications for such an estimate: On the one hand, it enables to simplify parts of the known existence theory and allows to show existence of solutions for generalised models involving collision-induced, quadratic fragmentation terms for which the previous existence theory seems difficult to apply. On the other hand and most prominently, it proves mass conservation (and thus the absence of gelation) for almost all the coagulation coefficients for which mass conservation is known to hold true in the space homogeneous case.

On the use of spatially discrete data to compute energy and mass balance

In many practical applications the state of field soils is monitored by recording the evolution of temperature and soil moisture at discrete depths. We theoretically investigate the systematic errors that arise when mass and energy balances are computed directly from these measurements. We show that, even with no measurement or model errors, large residuals might result when finite difference approximations are used to compute fluxes and storage term. To calculate the limits set by the use of spatially discrete measurements on the accuracy of balance closure, we derive an analytical solution to estimate the residual on the basis of the two key parameters: the penetration depth and the distance between the measurements. When the thickness of the control layer for which the balance is computed is comparable to the penetration depth of the forcing (which depends on the thermal diffusivity and on the forcing period) large residuals arise. The residual is also very sensitive to the distance between the measurements, which requires accurately controlling the position of the sensors in field experiments. We also demonstrate that, for the same experimental setup, mass residuals are sensitively larger than the energy residuals due to the nonlinearity of the moisture transport equation. Our analysis suggests that a careful assessment of the systematic mass error introduced by the use of spatially discrete data is required before using fluxes and residuals computed directly from field measurements.

Modeling of non-isothermalvapor membrane separation with thermodynamic models and generalized mass transfer equations

A rigorous unit operation model is developed for vapor membrane separation. The new model is able to describe temperature, pressure, and concentration dependent permeation as wellreal fluid effects in vapor and gas separation with hydrocarbon selective rubbery polymeric membranes. The permeation through the membrane is described by a separate treatment of sorption and diffusion within the membrane. The chemical engineering thermodynamics is used to describe the equilibrium sorption of vapors and gases in rubbery membranes with equation of state models for polymeric systems. Also a new modification of the UNIFAC model is proposed for this purpose. Various thermodynamic models are extensively compared in order to verify the models' ability to predict and correlate experimental vapor-liquid equilibrium data. The penetrant transport through the selective layer of the membrane is described with the generalized Maxwell-Stefan equations, which are able to account for thebulk flux contribution as well as the diffusive coupling effect. A method is described to compute and correlate binary penetrant¿membrane diffusion coefficients from the experimental permeability coefficients at different temperatures and pressures. A fluid flow model for spiral-wound modules is derived from the conservation equation of mass, momentum, and energy. The conservation equations are presented in a discretized form by using the control volume approach. A combination of the permeation model and the fluid flow model yields the desired rigorous model for vapor membrane separation. The model is implemented into an inhouse process simulator and so vapor membrane separation may be evaluated as an integralpart of a process flowsheet.

Discrete distributions when modeling the disability severity score of motor victims

Many European states apply score systems to evaluate the disability severity of non-fatal motor victims under the law of third-party liability. The score is a non-negative integer with an upper bound at 100 that increases with severity. It may be automatically converted into financial terms and thus also reflects the compensation cost for disability. In this paper, discrete regression models are applied to analyze the factors that influence the disability severity score of victims. Standard and zero-altered regression models are compared from two perspectives: an interpretation of the data generating process and the level of statistical fit. The results have implications for traffic safety policy decisions aimed at reducing accident severity. An application using data from Spain is provided.