Curing, defects and mechanical performance of fiber-reinforced composites

Tradicionalmente, la fabricación de materiales compuestos de altas prestaciones se lleva a cabo en autoclave mediante la consolidación de preimpregnados a través de la aplicación simultánea de altas presiones y temperatura. Las elevadas presiones empleadas en autoclave reducen la porosidad de los componentes garantizando unas buenas propiedades mecánicas. Sin embargo, este sistema de fabricación conlleva tiempos de producción largos y grandes inversiones en equipamiento lo que restringe su aplicación a otros sectores alejados del sector aeronáutico. Este hecho ha generado una creciente demanda de sistemas de fabricación alternativos al autoclave. Aunque estos sistemas son capaces de reducir los tiempos de producción y el gasto energético, por lo general, dan lugar a materiales con menores prestaciones mecánicas debido a que se reduce la compactación del material al aplicar presiones mas bajas y, por tanto, la fracción volumétrica de fibras, y disminuye el control de la porosidad durante el proceso. Los modelos numéricos existentes permiten conocer los fundamentos de los mecanismos de crecimiento de poros durante la fabricación de materiales compuestos de matriz polimérica mediante autoclave. Dichos modelos analizan el comportamiento de pequeños poros esféricos embebidos en una resina viscosa. Su validez no ha sido probada, sin embargo, para la morfología típica observada en materiales compuestos fabricados fuera de autoclave, consistente en poros cilíndricos y alargados embebidos en resina y rodeados de fibras continuas. Por otro lado, aunque existe una clara evidencia experimental del efecto pernicioso de la porosidad en las prestaciones mecánicas de los materiales compuestos, no existe información detallada sobre la influencia de las condiciones de procesado en la forma, fracción volumétrica y distribución espacial de los poros en los materiales compuestos. Las técnicas de análisis convencionales para la caracterización microestructural de los materiales compuestos proporcionan información en dos dimensiones (2D) (microscopía óptica y electrónica, radiografía de rayos X, ultrasonidos, emisión acústica) y sólo algunas son adecuadas para el análisis de la porosidad. En esta tesis, se ha analizado el efecto de ciclo de curado en el desarrollo de los poros durante la consolidación de preimpregnados Hexply AS4/8552 a bajas presiones mediante moldeo por compresión, en paneles unidireccionales y multiaxiales utilizando tres ciclos de curado diferentes. Dichos ciclos fueron cuidadosamente diseñados de acuerdo a la caracterización térmica y reológica de los preimpregnados. La fracción volumétrica de poros, su forma y distribución espacial se analizaron en detalle mediante tomografía de rayos X. Esta técnica no destructiva ha demostrado su capacidad para analizar la microestructura de materiales compuestos. Se observó, que la porosidad depende en gran medida de la evolución de la viscosidad dinámica a lo largo del ciclo y que la mayoría de la porosidad inicial procedía del aire atrapado durante el apilamiento de las láminas de preimpregnado. En el caso de los laminados multiaxiales, la porosidad también se vio afectada por la secuencia de apilamiento. En general, los poros tenían forma cilíndrica y se estaban orientados en la dirección de las fibras. Además, la proyección de la población de poros a lo largo de la dirección de la fibra reveló la existencia de una estructura celular de un diámetro aproximado de 1 mm. Las paredes de las celdas correspondían con regiones con mayor densidad de fibra mientras que los poros se concentraban en el interior de las celdas. Esta distribución de la porosidad es el resultado de una consolidación no homogenea. Toda esta información es crítica a la hora de optimizar las condiciones de procesado y proporcionar datos de partida para desarrollar herramientas de simulación de los procesos de fabricación de materiales compuestos fuera de autoclave. Adicionalmente, se determinaron ciertas propiedades mecánicas dependientes de la matriz termoestable con objeto de establecer la relación entre condiciones de procesado y las prestaciones mecánicas. En el caso de los laminados unidireccionales, la resistencia interlaminar depende de la porosidad para fracciones volumétricas de poros superiores 1%. Las mismas tendencias se observaron en el caso de GIIc mientras GIc no se vio afectada por la porosidad. En el caso de los laminados multiaxiales se evaluó la influencia de la porosidad en la resistencia a compresión, la resistencia a impacto a baja velocidad y la resistencia a copresión después de impacto. La resistencia a compresión se redujo con el contenido en poros, pero éste no influyó significativamente en la resistencia a compresión despues de impacto ya que quedó enmascarada por otros factores como la secuencia de apilamiento o la magnitud del daño generado tras el impacto. Finalmente, el efecto de las condiciones de fabricación en el proceso de compactación mediante moldeo por compresión en laminados unidireccionales fue simulado mediante el método de los elementos finitos en una primera aproximación para simular la fabricación de materiales compuestos fuera de autoclave. Los parámetros del modelo se obtuvieron mediante experimentos térmicos y reológicos del preimpregnado Hexply AS4/8552. Los resultados obtenidos en la predicción de la reducción de espesor durante el proceso de consolidación concordaron razonablemente con los resultados experimentales. Manufacturing of high performance polymer-matrix composites is normally carried out by means of autoclave using prepreg tapes stacked and consolidated under the simultaneous application of pressure and temperature. High autoclave pressures reduce the porosity in the laminate and ensure excellent mechanical properties. However, this manufacturing route is expensive in terms of capital investment and processing time, hindering its application in many industrial sectors. This fact has driven the demand of alternative out-of-autoclave processing routes. These techniques claim to produce composite parts faster and at lower cost but the mechanical performance is also reduced due to the lower fiber content and to the higher porosity. Corrient numerical models are able to simulate the mechanisms of void growth in polymer-matrix composites processed in autoclave. However these models are restricted to small spherical voids surrounded by a viscous resin. Their validity is not proved for long cylindrical voids in a viscous matrix surrounded by aligned fibers, the standard morphology observed in out-of-autoclave composites. In addition, there is an experimental evidence of the detrimental effect of voids on the mechanical performance of composites but, there is detailed information regarding the influence of curing conditions on the actual volume fraction, shape and spatial distribution of voids within the laminate. The standard techniques of microstructural characterization of composites (optical or electron microscopy, X-ray radiography, ultrasonics) provide information in two dimensions and are not always suitable to determine the porosity or void population. Moreover, they can not provide 3D information. The effect of curing cycle on the development of voids during consolidation of AS4/8552 prepregs at low pressure by compression molding was studied in unidirectional and multiaxial panels. They were manufactured using three different curing cycles carefully designed following the rheological and thermal analysis of the raw prepregs. The void volume fraction, shape and spatial distribution were analyzed in detail by means of X-ray computed microtomography, which has demonstrated its potential for analyzing the microstructural features of composites. It was demonstrated that the final void volume fraction depended on the evolution of the dynamic viscosity throughout the cycle. Most of the initial voids were the result of air entrapment and wrinkles created during lay-up. Differences in the final void volume fraction depended on the processing conditions for unidirectional and multiaxial panels. Voids were rod-like shaped and were oriented parallel to the fibers and concentrated in channels along the fiber orientation. X-ray computer tomography analysis of voids along the fiber direction showed a cellular structure with an approximate cell diameter of 1 mm. The cell walls were fiber-rich regions and porosity was localized at the center of the cells. This porosity distribution within the laminate was the result of inhomogeneous consolidation. This information is critical to optimize processing parameters and to provide inputs for virtual testing and virtual processing tools. In addition, the matrix-controlled mechanical properties of the panels were measured in order to establish the relationship between processing conditions and mechanical performance. The interlaminar shear strength (ILSS) and the interlaminar toughness (GIc and GIIc) were selected to evaluate the effect of porosity on the mechanical performance of unidirectional panels. The ILSS was strongly affected by the porosity when the void contents was higher than 1%. The same trends were observed in the case of GIIc while GIc was insensitive to the void volume fraction. Additionally, the mechanical performance of multiaxial panels in compression, low velocity impact and compression after impact (CAI) was measured to address the effect of processing conditions. The compressive strength decreased with porosity and ply-clustering. However, the porosity did not influence the impact resistance and the coompression after impact strength because the effect of porosity was masked by other factors as the damage due to impact or the laminate lay-up. Finally, the effect of the processing conditions on the compaction behavior of unidirectional AS4/8552 panels manufactured by compression moulding was simulated using the finite element method, as a first approximation to more complex and accurate models for out-of autoclave curing and consolidation of composite laminates. The model parameters were obtained from rheological and thermo-mechanical experiments carried out in raw prepreg samples. The predictions of the thickness change during consolidation were in reasonable agreement with the experimental results.

Interactions between roughness and topography in hydraulic models

Analysis of river flow using hydraulic modelling and its implications in derived environ-mental applications are inextricably connected with the way in which the river boundary shape is represented. This relationship is scale-dependent upon the modelling resolution which in turn determines the importance of a subscale performance of the model and the way subscale (surface and flow) processes are parameterised. Commonly, the subscale behaviour of the model relies upon a roughness parameterisation whose meaning depends on the dimensionality of the hydraulic model and the resolution of the topographic represen¬tation scale. This latter is, in turn, dependent on the resolution of the computational mesh as well as on the detail of measured topographic data. Flow results are affected by this interactions between scale and subscale parameterisation according to the dimensionality approach. The aim of this dissertation is the evaluation of these interactions upon hy¬draulic modelling results. Current high resolution topographic source availability induce this research which is tackled using a suitable roughness approach according to each di¬mensionality with the purpose of the interaction assessment. A 1D HEC-RAS model, a 2D raster-based diffusion-wave model with a scale-dependent distributed roughness parame-terisation and a 3D finite volume scheme with a porosity algorithm approach to incorporate complex topography have been used. Different topographic sources are assessed using a 1D scheme. LiDAR data are used to isolate the mesh resolution from the topographic content of the DEM effects upon 2D and 3D flow results. A distributed roughness parameterisation, using a roughness height approach dependent upon both mesh resolution and topographic content is developed and evaluated for the 2D scheme. Grain-size data and fractal methods are used for the reconstruction of topography with microscale information, required for some applications but not easily available. Sensitivity of hydraulic parameters to this topographic parameterisation is evaluated in a 3D scheme at different mesh resolu¬tions. Finally, the structural variability of simulated flow is analysed and related to scale interactions. Model simulations demonstrate (i) the importance of the topographic source in a 1D models; (ii) the mesh resolution approach is dominant in 2D and 3D simulations whereas in a 1D model the topographic source and even the roughness parameterisation impacts are more critical; (iii) the increment of the sensitivity to roughness parameterisa-tion in 1D and 2D schemes with detailed topographic sources and finer mesh resolutions; and (iv) the topographic content and microtopography impact throughout the vertical profile of computed 3D velocity in a depth-dependent way, whereas 2D results are not affected by topographic content variations. Finally, the spatial analysis shows that the mesh resolution controls high resolution model scale results, roughness parameterisation control 2D simulation results for a constant mesh resolution; and topographic content and micro-topography variations impacts upon the organisation of flow results depth-dependently in a 3D scheme. Resumen La topografía juega un papel fundamental en la distribución del agua y la energía en los paisajes naturales (Beven and Kirkby 1979; Wood et al. 1997). La simulación hidráulica combinada con métodos de medición del terreno por teledetección constituyen una poderosa herramienta de investigación en la comprensión del comportamiento de los flujos de agua debido a la variabilidad de la superficie sobre la que fluye. La representación e incorporación de la topografía en el esquema hidráulico tiene una importancia crucial en los resultados y determinan el desarrollo de sus aplicaciones al campo medioambiental. Cualquier simulación es una simplificación de un proceso del mundo real, y por tanto el grado de simplificación determinará el significado de los resultados simulados. Este razonamiento es particularmente difícil de trasladar a la simulación hidráulica donde aspectos de la escala tan diferentes como la escala de los procesos de flujo y de representación del contorno son considerados conjuntamente incluso en fases de parametrización (e.g. parametrización de la rugosidad). Por una parte, esto es debido a que las decisiones de escala vienen condicionadas entre ellas (e.g. la dimensionalidad del modelo condiciona la escala de representación del contorno) y por tanto interaccionan en sus resultados estrechamente. Y por otra parte, debido a los altos requerimientos numéricos y computacionales de una representación explícita de alta resolución de los procesos de flujo y discretización de la malla. Además, previo a la modelización hidráulica, la superficie del terreno sobre la que el agua fluye debe ser modelizada y por tanto presenta su propia escala de representación, que a su vez dependerá de la escala de los datos topográficos medidos con que se elabora el modelo. En última instancia, esta topografía es la que determina el comportamiento espacial del flujo. Por tanto, la escala de la topografía en sus fases de medición y modelización (resolución de los datos y representación topográfica) previas a su incorporación en el modelo hidráulico producirá a su vez un impacto que se acumulará al impacto global resultante debido a la escala computacional del modelo hidráulico y su dimensión. La comprensión de las interacciones entre las complejas geometrías del contorno y la estructura del flujo utilizando la modelización hidráulica depende de las escalas consideradas en la simplificación de los procesos hidráulicos y del terreno (dimensión del modelo, tamaño de escala computacional y escala de los datos topográficos). La naturaleza de la aplicación del modelo hidráulico (e.g. habitat físico, análisis de riesgo de inundaciones, transporte de sedimentos) determina en primer lugar la escala del estudio y por tanto el detalle de los procesos a simular en el modelo (i.e. la dimensionalidad) y, en consecuencia, la escala computacional a la que se realizarán los cálculos (i.e. resolución computacional). Esta última a su vez determina, el detalle geográfico con que deberá representarse el contorno acorde con la resolución de la malla computacional. La parametrización persigue incorporar en el modelo hidráulico la cuantificación de los procesos y condiciones físicas del sistema natural y por tanto debe incluir no solo aquellos procesos que tienen lugar a la escala de modelización, sino también aquellos que tienen lugar a un nivel subescalar y que deben ser definidos mediante relaciones de escalado con las variables modeladas explícitamente. Dicha parametrización se implementa en la práctica mediante la provisión de datos al modelo, por tanto la escala de los datos geográficos utilizados para parametrizar el modelo no sólo influirá en los resultados, sino también determinará la importancia del comportamiento subescalar del modelo y el modo en que estos procesos deban ser parametrizados (e.g. la variabilidad natural del terreno dentro de la celda de discretización o el flujo en las direcciones laterales y verticales en un modelo unidimensional). En esta tesis, se han utilizado el modelo unidimensional HEC-RAS, (HEC 1998b), un modelo ráster bidimensional de propagación de onda, (Yu 2005) y un esquema tridimensional de volúmenes finitos con un algoritmo de porosidad para incorporar la topografía, (Lane et al. 2004; Hardy et al. 2005). La geometría del contorno viene definida por la escala de representación topográfica (resolución de malla y contenido topográfico), la cual a su vez depende de la escala de la fuente cartográfica. Todos estos factores de escala interaccionan en la respuesta del modelo hidráulico a la topografía. En los últimos años, métodos como el análisis fractal y las técnicas geoestadísticas utilizadas para representar y analizar elementos geográficos (e.g. en la caracterización de superficies (Herzfeld and Overbeck 1999; Butler et al. 2001)), están promoviendo nuevos enfoques en la cuantificación de los efectos de escala (Lam et al. 2004; Atkinson and Tate 2000; Lam et al. 2006) por medio del análisis de la estructura espacial de la variable (e.g. Bishop et al. 2006; Ju et al. 2005; Myint et al. 2004; Weng 2002; Bian and Xie 2004; Southworth et al. 2006; Pozd-nyakova et al. 2005; Kyriakidis and Goodchild 2006). Estos métodos cuantifican tanto el rango de valores de la variable presentes a diferentes escalas como la homogeneidad o heterogeneidad de la variable espacialmente distribuida (Lam et al. 2004). En esta tesis, estas técnicas se han utilizado para analizar el impacto de la topografía sobre la estructura de los resultados hidráulicos simulados. Los datos de teledetección de alta resolución y técnicas GIS también están siendo utilizados para la mejor compresión de los efectos de escala en modelos medioambientales (Marceau 1999; Skidmore 2002; Goodchild 2003) y se utilizan en esta tesis. Esta tesis como corpus de investigación aborda las interacciones de esas escalas en la modelización hidráulica desde un punto de vista global e interrelacionado. Sin embargo, la estructura y el foco principal de los experimentos están relacionados con las nociones espaciales de la escala de representación en relación con una visión global de las interacciones entre escalas. En teoría, la representación topográfica debe caracterizar la superficie sobre la que corre el agua a una adecuada (conforme a la finalidad y dimensión del modelo) escala de discretización, de modo que refleje los procesos de interés. La parametrización de la rugosidad debe de reflejar los efectos de la variabilidad de la superficie a escalas de más detalle que aquellas representadas explícitamente en la malla topográfica (i.e. escala de discretización). Claramente, ambos conceptos están físicamente relacionados por un

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

Cup Anemometer's loss of performance due to ageing process, and its effect on Annual energy Production (AEP) estimates

The deviation of calibration coefficients from five cup anemometer models over time was analyzed. The analysis was based on a series of laboratory calibrations between January 2001 and August 2010. The analysis was performed on two different groups of anemometers: (1) anemometers not used for any industrial purpose (that is, just stored); and (2) anemometers used in different industrial applications (mainly in the field—or outside—applications like wind farms). Results indicate a loss of performance of the studied anemometers over time. In the case of the unused anemometers the degradation shows a clear pattern. In the case of the anemometers used in the field, the data analyzed also suggest a loss of performance, yet the degradation does not show a clear trend. A recalibration schedule is proposed based on the observed performances variations

Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.

A CAD framework for the characterization and use of Memristor models

In the recent years the missing fourth component, the memristor, was successfully synthesized. However, the mathematical complexity and variety of the models behind this component, in addition to the existence of convergence problems in the simulations, make the design of memristor-based applications long and difficult. In this work we present a memristor model characterization framework which supports the automated generation of subcircuit files. The proposed environment allows the designer to choose and parameterize the memristor model that best suits for a given application. The framework carries out characterizing simulations in order to study the possible non-convergence problems, solving the dependence on the simulation conditions and guaranteeing the functionality and performance of the design. Additionally, the occurrence of undesirable effects related to PVT variations is also taken into account. By performing a Monte Carlo or a corner analysis, the designer is aware of the safety margins which assure the correct device operation.

Analysis and validation of CFD wind farm models in complex terrain. Effects induced by topography and wind turbines

Wind farms have been extensively simulated through engineering models for the estimation of wind speed and power deficits inside wind farms. These models were designed initially for a few wind turbines located in flat terrain. Other models based on the parabolic approximation of Navier Stokes equations were developed, making more realistic and feasible the operational resolution of big wind farms in flat terrain and offshore sites. These models have demonstrated to be accurate enough when solving wake effects for this type of environments. Nevertheless, few analyses exist on how complex terrain can affect the behaviour of wind farm wake flow. Recent numerical studies have demonstrated that topographical wakes induce a significant effect on wind turbines wakes, compared to that on flat terrain. This circumstance has recommended the development of elliptic CFD models which allow global simulation of wind turbine wakes in complex terrain. An accurate simplification for the analysis of wind turbine wakes is the actuator disk technique. Coupling this technique with CFD wind models enables the estimation of wind farm wakes preserving the extraction of axial momentum present inside wind farms. This paper describes the analysis and validation of the elliptical wake model CFDWake 1.0 against experimental data from an operating wind farm located in complex terrain. The analysis also reports whether it is possible or not to superimpose linearly the effect of terrain and wind turbine wakes. It also represents one of the first attempts to observe the performance of engineering models compares in large complex terrain wind farms.

Maximum likelihood estimation of new state space models for operational modal analysis

The modal analysis of a structural system consists on computing its vibrational modes. The experimental way to estimate these modes requires to excite the system with a measured or known input and then to measure the system output at different points using sensors. Finally, system inputs and outputs are used to compute the modes of vibration. When the system refers to large structures like buildings or bridges, the tests have to be performed in situ, so it is not possible to measure system inputs such as wind, traffic, . . .Even if a known input is applied, the procedure is usually difficult and expensive, and there are still uncontrolled disturbances acting at the time of the test. These facts led to the idea of computing the modes of vibration using only the measured vibrations and regardless of the inputs that originated them, whether they are ambient vibrations (wind, earthquakes, . . . ) or operational loads (traffic, human loading, . . . ). This procedure is usually called Operational Modal Analysis (OMA), and in general consists on to fit a mathematical model to the measured data assuming the unobserved excitations are realizations of a stationary stochastic process (usually white noise processes). Then, the modes of vibration are computed from the estimated model. The first issue investigated in this thesis is the performance of the Expectation- Maximization (EM) algorithm for the maximum likelihood estimation of the state space model in the field of OMA. The algorithm is described in detail and it is analysed how to apply it to vibration data. After that, it is compared to another well known method, the Stochastic Subspace Identification algorithm. The maximum likelihood estimate enjoys some optimal properties from a statistical point of view what makes it very attractive in practice, but the most remarkable property of the EM algorithm is that it can be used to address a wide range of situations in OMA. In this work, three additional state space models are proposed and estimated using the EM algorithm: • The first model is proposed to estimate the modes of vibration when several tests are performed in the same structural system. Instead of analyse record by record and then compute averages, the EM algorithm is extended for the joint estimation of the proposed state space model using all the available data. • The second state space model is used to estimate the modes of vibration when the number of available sensors is lower than the number of points to be tested. In these cases it is usual to perform several tests changing the position of the sensors from one test to the following (multiple setups of sensors). Here, the proposed state space model and the EM algorithm are used to estimate the modal parameters taking into account the data of all setups. • And last, a state space model is proposed to estimate the modes of vibration in the presence of unmeasured inputs that cannot be modelled as white noise processes. In these cases, the frequency components of the inputs cannot be separated from the eigenfrequencies of the system, and spurious modes are obtained in the identification process. The idea is to measure the response of the structure corresponding to different inputs; then, it is assumed that the parameters common to all the data correspond to the structure (modes of vibration), and the parameters found in a specific test correspond to the input in that test. The problem is solved using the proposed state space model and the EM algorithm. Resumen El análisis modal de un sistema estructural consiste en calcular sus modos de vibración. Para estimar estos modos experimentalmente es preciso excitar el sistema con entradas conocidas y registrar las salidas del sistema en diferentes puntos por medio de sensores. Finalmente, los modos de vibración se calculan utilizando las entradas y salidas registradas. Cuando el sistema es una gran estructura como un puente o un edificio, los experimentos tienen que realizarse in situ, por lo que no es posible registrar entradas al sistema tales como viento, tráfico, . . . Incluso si se aplica una entrada conocida, el procedimiento suele ser complicado y caro, y todavía están presentes perturbaciones no controladas que excitan el sistema durante el test. Estos hechos han llevado a la idea de calcular los modos de vibración utilizando sólo las vibraciones registradas en la estructura y sin tener en cuenta las cargas que las originan, ya sean cargas ambientales (viento, terremotos, . . . ) o cargas de explotación (tráfico, cargas humanas, . . . ). Este procedimiento se conoce en la literatura especializada como Análisis Modal Operacional, y en general consiste en ajustar un modelo matemático a los datos registrados adoptando la hipótesis de que las excitaciones no conocidas son realizaciones de un proceso estocástico estacionario (generalmente ruido blanco). Posteriormente, los modos de vibración se calculan a partir del modelo estimado. El primer problema que se ha investigado en esta tesis es la utilización de máxima verosimilitud y el algoritmo EM (Expectation-Maximization) para la estimación del modelo espacio de los estados en el ámbito del Análisis Modal Operacional. El algoritmo se describe en detalle y también se analiza como aplicarlo cuando se dispone de datos de vibraciones de una estructura. A continuación se compara con otro método muy conocido, el método de los Subespacios. Los estimadores máximo verosímiles presentan una serie de propiedades que los hacen óptimos desde un punto de vista estadístico, pero la propiedad más destacable del algoritmo EM es que puede utilizarse para resolver un amplio abanico de situaciones que se presentan en el Análisis Modal Operacional. En este trabajo se proponen y estiman tres modelos en el espacio de los estados: • El primer modelo se utiliza para estimar los modos de vibración cuando se dispone de datos correspondientes a varios experimentos realizados en la misma estructura. En lugar de analizar registro a registro y calcular promedios, se utiliza algoritmo EM para la estimación conjunta del modelo propuesto utilizando todos los datos disponibles. • El segundo modelo en el espacio de los estados propuesto se utiliza para estimar los modos de vibración cuando el número de sensores disponibles es menor que vi Resumen el número de puntos que se quieren analizar en la estructura. En estos casos es usual realizar varios ensayos cambiando la posición de los sensores de un ensayo a otro (múltiples configuraciones de sensores). En este trabajo se utiliza el algoritmo EM para estimar los parámetros modales teniendo en cuenta los datos de todas las configuraciones. • Por último, se propone otro modelo en el espacio de los estados para estimar los modos de vibración en la presencia de entradas al sistema que no pueden modelarse como procesos estocásticos de ruido blanco. En estos casos, las frecuencias de las entradas no se pueden separar de las frecuencias del sistema y se obtienen modos espurios en la fase de identificación. La idea es registrar la respuesta de la estructura correspondiente a diferentes entradas; entonces se adopta la hipótesis de que los parámetros comunes a todos los registros corresponden a la estructura (modos de vibración), y los parámetros encontrados en un registro específico corresponden a la entrada en dicho ensayo. El problema se resuelve utilizando el modelo propuesto y el algoritmo EM.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks.

VentanaTM power train features and performance

Most CPV systems are based on Fresnel lenses. Among these, LPI-patented Fresnel-Köhler (FK) concentrator outstands owing to performance and practical reasons. The VentanaTM power train is the first off-the-shelf commercial product based on the FK and comprises both the primary (POE) lenses (a 36-units 1×1 m2 acrylic panel manufactured by EVONIK and 10×) and glass (or Savosil) secondary optics (SOE). This high concentration optical train (Cg=1,024×, ~250mm optical depth) fits with 5×5 mm2 (at least) solar cells. The optical train is the fruit of a 1-year development that has included design, modeling, prototyping and characterization, and through the process LPI had the opportunity to find out how well the actual performance correlates with models, but also learned practical aspects of a CPV system of this kind, some of which have very positive impact on system performance and reliability.

A formalism and method for representing and reasoning with process models authored by subject matter experts

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic in order to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82% were well-formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25% and 30% with respect to the base case, respectively.