From transient to steady state deformation and grain size: A thermodynamic approach using elasto-visco-plastic numerical modeling

Numerical simulation experiments give insight into the evolving energy partitioning during high-strain torsion experiments of calcite. Our numerical experiments are designed to derive a generic macroscopic grain size sensitive flow law capable of describing the full evolution from the transient regime to steady state. The transient regime is crucial for understanding the importance of micro structural processes that may lead to strain localization phenomena in deforming materials. This is particularly important in geological and geodynamic applications where the phenomenon of strain localization happens outside the time frame that can be observed under controlled laboratory conditions. Ourmethod is based on an extension of the paleowattmeter approach to the transient regime. We add an empirical hardening law using the Ramberg-Osgood approximation and assess the experiments by an evolution test function of stored over dissipated energy (lambda factor). Parameter studies of, strain hardening, dislocation creep parameter, strain rates, temperature, and lambda factor as well asmesh sensitivity are presented to explore the sensitivity of the newly derived transient/steady state flow law. Our analysis can be seen as one of the first steps in a hybrid computational-laboratory-field modeling workflow. The analysis could be improved through independent verifications by thermographic analysis in physical laboratory experiments to independently assess lambda factor evolution under laboratory conditions.

Micro-blogging adoption in the enterprise: an empirical analysis

Given the increasing interest in using social software for company-internal communication and collaboration, this paper examines drivers and inhibitors of micro-blogging adoption at the workplace. While nearly one in two companies is currently planning to introduce social software, there is no empirically validated research on employees’ adoption. In this paper, we build on previous focus group results and test our research model in an empirical study using Structural Equation Modeling. Based on our findings, we derive recommendations on how to foster adoption. We suggest that micro-blogging should be presented to employees as an efficient means of communication, personal brand building, and knowledge management. In order to particularly promote content contribution, privacy concerns should be eased by setting clear rules on who has access to postings and for how long they will be archived.

Why participate in an online social network: an empirical analysis

Despite their enormous success the motivation behind user participation in Online Social Networks is still little understood. This study explores a variety of possible incentives and provides an empirical evaluation of their subjective relevance. The analysis is based on survey data from 129 test subjects. Using Structural Equation Modeling, we identified that the satisfaction of the needs for belongingness and the esteem needs through self-presentation together with peer pressure are the main drivers of participation. The analysis of a sub-sample of active users pointed out the satisfaction of the cognitive needs as an additional participation determinant. Based on these findings, recommendations for online social network providers are made.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Monte Carlo modeling of polarized light propagation: Stokes vs Jones - Part I

This bipartite comparative study aims at inspecting the similarities and differences between the Jones and Stokes–Mueller formalisms when modeling polarized light propagation with numerical simulations of the Monte Carlo type. In this first part, we review the theoretical concepts that concern light propagation and detection with both pure and partially/totally unpolarized states. The latter case involving fluctuations, or “depolarizing effects,” is of special interest here: Jones and Stokes–Mueller are equally apt to model such effects and are expected to yield identical results. In a second, ensuing paper, empirical evidence is provided by means of numerical experiments, using both formalisms.

Modeling corrective experiences as motivational mountain pass

Aim: The landscape metaphor allows viewing corrective experiences (CE) as pathway to a state with relatively lower 'tension' (local minimum). However, such local minima are not easily accessible but obstructed by states with relatively high tension (local maxima) according to the landscape metaphor (Caspar & Berger, 2012). For example, an individual with spider phobia has to transiently tolerate high levels of tension during an exposure therapy to access the local minimum of habituation. To allow for more specific therapeutic guidelines and empirically testable hypotheses, we advance the landscape metaphor to a scientific model which bases on motivational processes. Specifically, we conceptualize CEs as available but unusual trajectories (=pathways) through a motivational space. The dimensions of the motivational state are set up by basic motives such as need for agency or attachment. Methods: Dynamic system theory is used to model motivational states and trajectories using mathematical equations. Fortunately, these equations have easy-to-comprehend and intuitive visual representations similar to the landscape metaphor. Thus, trajectories that represent CEs are informative and action guiding for both therapists and patients without knowledge on dynamic systems. However, the mathematical underpinnings of the model allow researchers to deduct hypotheses for empirical testing. Results: First, the results of simulations of CEs during exposure therapy in anxiety disorders are presented and compared to empirical findings. Second, hypothetical CEs in an autonomy-attachment conflict are reported from a simulation study. Discussion: Preliminary clinical implications for the evocation of CEs are drawn after a critical discussion of the proposed model.

Modeling the formation and aging of secondary organic aerosols during CalNex 2010

Four different literature parameterizations for the formation and evolution of urban secondary organic aerosol (SOA) frequently used in 3-D models are evaluated using a 0-D box model representing the Los Angeles metropolitan region during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign. We constrain the model predictions with measurements from several platforms and compare predictions with particle- and gas-phase observations from the CalNex Pasadena ground site. That site provides a unique opportunity to study aerosol formation close to anthropogenic emission sources with limited recirculation. The model SOA that formed only from the oxidation of VOCs (V-SOA) is insufficient to explain the observed SOA concentrations, even when using SOA parameterizations with multi-generation oxidation that produce much higher yields than have been observed in chamber experiments, or when increasing yields to their upper limit estimates accounting for recently reported losses of vapors to chamber walls. The Community Multiscale Air Quality (WRF-CMAQ) model (version 5.0.1) provides excellent predictions of secondary inorganic particle species but underestimates the observed SOA mass by a factor of 25 when an older VOC-only parameterization is used, which is consistent with many previous model–measurement comparisons for pre-2007 anthropogenic SOA modules in urban areas. Including SOA from primary semi-volatile and intermediate-volatility organic compounds (P-S/IVOCs) following the parameterizations of Robinson et al. (2007), Grieshop et al. (2009), or Pye and Seinfeld (2010) improves model–measurement agreement for mass concentration. The results from the three parameterizations show large differences (e.g., a factor of 3 in SOA mass) and are not well constrained, underscoring the current uncertainties in this area. Our results strongly suggest that other precursors besides VOCs, such as P-S/IVOCs, are needed to explain the observed SOA concentrations in Pasadena. All the recent parameterizations overpredict urban SOA formation at long photochemical ages (3 days) compared to observations from multiple sites, which can lead to problems in regional and especially global modeling. However, reducing IVOC emissions by one-half in the model to better match recent IVOC measurements improves SOA predictions at these long photochemical ages. Among the explicitly modeled VOCs, the precursor compounds that contribute the greatest SOA mass are methylbenzenes. Measured polycyclic aromatic hydrocarbons (naphthalenes) contribute 0.7% of the modeled SOA mass. The amounts of SOA mass from diesel vehicles, gasoline vehicles, and cooking emissions are estimated to be 16–27, 35–61, and 19–35 %, respectively, depending on the parameterization used, which is consistent with the observed fossil fraction of urban SOA, 71(+-3) %. The relative contribution of each source is uncertain by almost a factor of 2 depending on the parameterization used. In-basin biogenic VOCs are predicted to contribute only a few percent to SOA. A regional SOA background of approximately 2.1 μgm-3 is also present due to the long-distance transport of highly aged OA, likely with a substantial contribution from regional biogenic SOA. The percentage of SOA from diesel vehicle emissions is the same, within the estimated uncertainty, as reported in previous work that analyzed the weekly cycles in OA concentrations (Bahreini et al., 2012; Hayes et al., 2013). However, the modeling work presented here suggests a strong anthropogenic source of modern carbon in SOA, due to cooking emissions, which was not accounted for in those previous studies and which is higher on weekends. Lastly, this work adapts a simple two-parameter model to predict SOA concentration and O/C from urban emissions. This model successfully predicts SOA concentration, and the optimal parameter combination is very similar to that found for Mexico City. This approach provides a computationally inexpensive method for predicting urban SOA in global and climate models. We estimate pollution SOA to account for 26 Tg yr-1 of SOA globally, or 17% of global SOA, one third of which is likely to be non-fossil.

The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink

The growth rate of atmospheric carbondioxide(CO2) concentrations since industrialization is characterized by large interannual variability, mostly resulting from variability in CO 2 uptake by terrestrial ecosystems (typically termed carbon sink). However, the contributions of regional ecosystems to that variability are not well known. Using an ensemble of ecosystem and land-surface models and an empirical observation-based product of global gross primary production, we show that the mean sink, trend, and interannual variability in CO2 uptake by terrestrial ecosystems are dominated by distinct biogeographic regions. Whereas the mean sink is dominated by highly productive lands (mainly tropical forests), the trend and interannual variability of the sink are dominated by semi-arid ecosystems whose carbon balance is strongly associated with circulation-driven variations in both precipitation and temperature.

Empirical Analysis of Credit Risk Regime Switching and Temporal Conditional Default Correlation in Credit Default Swap Valuation: The Market liquidity effect

In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

Modeling the Volatility of Real GDP Growth: The Case of Japan Revisited

Previous studies (e.g., Hamori, 2000; Ho and Tsui, 2003; Fountas et al., 2004) find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Bai and Perronâs (1998, 2003) multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility.

Modeling for the world crude oil and natural gas markets

Crude oil and natural gas have been essential energy sources and play a crucial role in the world economy. Changes in energy prices significantly impact economic growth. This study builds an econometric model to illustrate the substitute relation between crude oil and natural gas markets. Additionally, the determination of the oil and natural gas prices are endogenized, assuming imperfect competition to reflect a real market strategy. Our empirical results show that the overall performance of this system is acceptable, and the model can be applied to policy analysis for determining monetary or energy policy by introducing this model to the more comprehensive system.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Development of a wake model for wind farms based on an open source CFD solver. Strategies on parabolization and turbulence modeling

Wake effect represents one of the most important aspects to be analyzed at the engineering phase of every wind farm since it supposes an important power deficit and an increase of turbulence levels with the consequent decrease of the lifetime. It depends on the wind farm design, wind turbine type and the atmospheric conditions prevailing at the site. Traditionally industry has used analytical models, quick and robust, which allow carry out at the preliminary stages wind farm engineering in a flexible way. However, new models based on Computational Fluid Dynamics (CFD) are needed. These models must increase the accuracy of the output variables avoiding at the same time an increase in the computational time. Among them, the elliptic models based on the actuator disk technique have reached an extended use during the last years. These models present three important problems in case of being used by default for the solution of large wind farms: the estimation of the reference wind speed upstream of each rotor disk, turbulence modeling and computational time. In order to minimize the consequence of these problems, this PhD Thesis proposes solutions implemented under the open source CFD solver OpenFOAM and adapted for each type of site: a correction on the reference wind speed for the general elliptic models, the semi-parabollic model for large offshore wind farms and the hybrid model for wind farms in complex terrain. All the models are validated in terms of power ratios by means of experimental data derived from real operating wind farms.

Urban wind energy: empirical optimization of high-rise building roof shape for the wind energy exploitation

El programa Europeo HORIZON2020 en Futuras Ciudades Inteligentes establece como objetivo que el 20% de la energía eléctrica sea generada a partir de fuentes renovables. Este objetivo implica la necesidad de potenciar la generación de energía eólica en todos los ámbitos. La energía eólica reduce drásticamente las emisiones de gases de efecto invernadero y evita los riesgos geo-políticos asociados al suministro e infraestructuras energéticas, así como la dependencia energética de otras regiones. Además, la generación de energía distribuida (generación en el punto de consumo) presenta significativas ventajas en términos de elevada eficiencia energética y estimulación de la economía. El sector de la edificación representa el 40% del consumo energético total de la Unión Europea. La reducción del consumo energético en este área es, por tanto, una prioridad de acuerdo con los objetivos "20-20-20" en eficiencia energética. La Directiva 2010/31/EU del Parlamento Europeo y del Consejo de 19 de mayo de 2010 sobre el comportamiento energético de edificaciones contempla la instalación de sistemas de suministro energético a partir de fuentes renovables en las edificaciones de nuevo diseño. Actualmente existe una escasez de conocimiento científico y tecnológico acerca de la geometría óptima de las edificaciones para la explotación de la energía eólica en entornos urbanos. El campo tecnológico de estudio de la presente Tesis Doctoral es la generación de energía eólica en entornos urbanos. Específicamente, la optimization de la geometría de las cubiertas de edificaciones desde el punto de vista de la explotación del recurso energético eólico. Debido a que el flujo del viento alrededor de las edificaciones es exhaustivamente investigado en esta Tesis empleando herramientas de simulación numérica, la mecánica de fluidos computacional (CFD en inglés) y la aerodinámica de edificaciones son los campos científicos de estudio. El objetivo central de esta Tesis Doctoral es obtener una geometría de altas prestaciones (u óptima) para la explotación de la energía eólica en cubiertas de edificaciones de gran altura. Este objetivo es alcanzado mediante un análisis exhaustivo de la influencia de la forma de la cubierta del edificio en el flujo del viento desde el punto de vista de la explotación energética del recurso eólico empleando herramientas de simulación numérica (CFD). Adicionalmente, la geometría de la edificación convencional (edificio prismático) es estudiada, y el posicionamiento adecuado para los diferentes tipos de aerogeneradores es propuesto. La compatibilidad entre el aprovechamiento de las energías solar fotovoltaica y eólica también es analizado en este tipo de edificaciones. La investigación prosigue con la optimización de la geometría de la cubierta. La metodología con la que se obtiene la geometría óptima consta de las siguientes etapas: - Verificación de los resultados de las geometrías previamente estudiadas en la literatura. Las geometrías básicas que se someten a examen son: cubierta plana, a dos aguas, inclinada, abovedada y esférica. - Análisis de la influencia de la forma de las aristas de la cubierta sobre el flujo del viento. Esta tarea se lleva a cabo mediante la comparación de los resultados obtenidos para la arista convencional (esquina sencilla) con un parapeto, un voladizo y una esquina curva. - Análisis del acoplamiento entre la cubierta y los cerramientos verticales (paredes) mediante la comparación entre diferentes variaciones de una cubierta esférica en una edificación de gran altura: cubierta esférica estudiada en la literatura, cubierta esférica integrada geométricamente con las paredes (planta cuadrada en el suelo) y una cubierta esférica acoplada a una pared cilindrica. El comportamiento del flujo sobre la cubierta es estudiado también considerando la posibilidad de la variación en la dirección del viento incidente. - Análisis del efecto de las proporciones geométricas del edificio sobre el flujo en la cubierta. - Análisis del efecto de la presencia de edificaciones circundantes sobre el flujo del viento en la cubierta del edificio objetivo. Las contribuciones de la presente Tesis Doctoral pueden resumirse en: - Se demuestra que los modelos de turbulencia RANS obtienen mejores resultados para la simulación del viento alrededor de edificaciones empleando los coeficientes propuestos por Crespo y los propuestos por Bechmann y Sórensen que empleando los coeficientes estándar. - Se demuestra que la estimación de la energía cinética turbulenta del flujo empleando modelos de turbulencia RANS puede ser validada manteniendo el enfoque en la cubierta de la edificación. - Se presenta una nueva modificación del modelo de turbulencia Durbin k — e que reproduce mejor la distancia de recirculación del flujo de acuerdo con los resultados experimentales. - Se demuestra una relación lineal entre la distancia de recirculación en una cubierta plana y el factor constante involucrado en el cálculo de la escala de tiempo de la velocidad turbulenta. Este resultado puede ser empleado por la comunidad científica para la mejora del modelado de la turbulencia en diversas herramientas computacionales (OpenFOAM, Fluent, CFX, etc.). - La compatibilidad entre las energías solar fotovoltaica y eólica en cubiertas de edificaciones es analizada. Se demuestra que la presencia de los módulos solares provoca un descenso en la intensidad de turbulencia. - Se demuestran conflictos en el cambio de escala entre simulaciones de edificaciones a escala real y simulaciones de modelos a escala reducida (túnel de viento). Se demuestra que para respetar las limitaciones de similitud (número de Reynolds) son necesarias mediciones en edificaciones a escala real o experimentos en túneles de viento empleando agua como fluido, especialmente cuando se trata con geometrías complejas, como es el caso de los módulos solares. - Se determina el posicionamiento más adecuado para los diferentes tipos de aerogeneradores tomando en consideración la velocidad e intensidad de turbulencia del flujo. El posicionamiento de aerogeneradores es investigado en las geometrías de cubierta más habituales (plana, a dos aguas, inclinada, abovedada y esférica). - Las formas de aristas más habituales (esquina, parapeto, voladizo y curva) son analizadas, así como su efecto sobre el flujo del viento en la cubierta de un edificio de gran altura desde el punto de vista del aprovechamiento eólico. - Se propone una geometría óptima (o de altas prestaciones) para el aprovechamiento de la energía eólica urbana. Esta optimización incluye: verificación de las geometrías estudiadas en el estado del arte, análisis de la influencia de las aristas de la cubierta en el flujo del viento, estudio del acoplamiento entre la cubierta y las paredes, análisis de sensibilidad del grosor de la cubierta, exploración de la influencia de las proporciones geométricas de la cubierta y el edificio, e investigación del efecto de las edificaciones circundantes (considerando diferentes alturas de los alrededores) sobre el flujo del viento en la cubierta del edificio objetivo. Las investigaciones comprenden el análisis de la velocidad, la energía cinética turbulenta y la intensidad de turbulencia en todos los casos. ABSTRACT The HORIZON2020 European program in Future Smart Cities aims to have 20% of electricity produced by renewable sources. This goal implies the necessity to enhance the wind energy generation, both with large and small wind turbines. Wind energy drastically reduces carbon emissions and avoids geo-political risks associated with supply and infrastructure constraints, as well as energy dependence from other regions. Additionally, distributed energy generation (generation at the consumption site) offers significant benefits in terms of high energy efficiency and stimulation of the economy. The buildings sector represents 40% of the European Union total energy consumption. Reducing energy consumption in this area is therefore a priority under the "20-20-20" objectives on energy efficiency. The Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings aims to consider the installation of renewable energy supply systems in new designed buildings. Nowadays, there is a lack of knowledge about the optimum building shape for urban wind energy exploitation. The technological field of study of the present Thesis is the wind energy generation in urban environments. Specifically, the improvement of the building-roof shape with a focus on the wind energy resource exploitation. Since the wind flow around buildings is exhaustively investigated in this Thesis using numerical simulation tools, both computational fluid dynamics (CFD) and building aerodynamics are the scientific fields of study. The main objective of this Thesis is to obtain an improved (or optimum) shape of a high-rise building for the wind energy exploitation on the roof. To achieve this objective, an analysis of the influence of the building shape on the behaviour of the wind flow on the roof from the point of view of the wind energy exploitation is carried out using numerical simulation tools (CFD). Additionally, the conventional building shape (prismatic) is analysed, and the adequate positions for different kinds of wind turbines are proposed. The compatibility of both photovoltaic-solar and wind energies is also analysed for this kind of buildings. The investigation continues with the buildingroof optimization. The methodology for obtaining the optimum high-rise building roof shape involves the following stages: - Verification of the results of previous building-roof shapes studied in the literature. The basic shapes that are compared are: flat, pitched, shed, vaulted and spheric. - Analysis of the influence of the roof-edge shape on the wind flow. This task is carried out by comparing the results obtained for the conventional edge shape (simple corner) with a railing, a cantilever and a curved edge. - Analysis of the roof-wall coupling by testing different variations of a spherical roof on a high-rise building: spherical roof studied in the litera ture, spherical roof geometrically integrated with the walls (squared-plant) and spherical roof with a cylindrical wall. The flow behaviour on the roof according to the variation of the incident wind direction is commented. - Analysis of the effect of the building aspect ratio on the flow. - Analysis of the surrounding buildings effect on the wind flow on the target building roof. The contributions of the present Thesis can be summarized as follows: - It is demonstrated that RANS turbulence models obtain better results for the wind flow around buildings using the coefficients proposed by Crespo and those proposed by Bechmann and S0rensen than by using the standard ones. - It is demonstrated that RANS turbulence models can be validated for turbulent kinetic energy focusing on building roofs. - A new modification of the Durbin k — e turbulence model is proposed in order to obtain a better agreement of the recirculation distance between CFD simulations and experimental results. - A linear relationship between the recirculation distance on a flat roof and the constant factor involved in the calculation of the turbulence velocity time scale is demonstrated. This discovery can be used by the research community in order to improve the turbulence modeling in different solvers (OpenFOAM, Fluent, CFX, etc.). - The compatibility of both photovoltaic-solar and wind energies on building roofs is demonstrated. A decrease of turbulence intensity due to the presence of the solar panels is demonstrated. - Scaling issues are demonstrated between full-scale buildings and windtunnel reduced-scale models. The necessity of respecting the similitude constraints is demonstrated. Either full-scale measurements or wind-tunnel experiments using water as a medium are needed in order to accurately reproduce the wind flow around buildings, specially when dealing with complex shapes (as solar panels, etc.). - The most adequate position (most adequate roof region) for the different kinds of wind turbines is highlighted attending to both velocity and turbulence intensity. The wind turbine positioning was investigated for the most habitual kind of building-roof shapes (flat, pitched, shed, vaulted and spherical). - The most habitual roof-edge shapes (simple edge, railing, cantilever and curved) were investigated, and their effect on the wind flow on a highrise building roof were analysed from the point of view of the wind energy exploitation. - An optimum building-roof shape is proposed for the urban wind energy exploitation. Such optimization includes: state-of-the-art roof shapes test, analysis of the influence of the roof-edge shape on the wind flow, study of the roof-wall coupling, sensitivity analysis of the roof width, exploration of the aspect ratio of the building-roof shape and investigation of the effect of the neighbouring buildings (considering different surrounding heights) on the wind now on the target building roof. The investigations comprise analysis of velocity, turbulent kinetic energy and turbulence intensity for all the cases.