Coupled model of initiation and propagation of corrosion in reinforced concrete

Corrosion of reinforcing steel in concrete due to chloride ingress is one of the main causes of the deterioration of reinforced concrete structures. Structures most affected by such a corrosion are marine zone buildings and structures exposed to de-icing salts like highways and bridges. Such process is accompanied by an increase in volume of the corrosión products on the rebarsconcrete interface. Depending on the level of oxidation, iron can expand as much as six times its original volume. This increase in volume exerts tensile stresses in the surrounding concrete which result in cracking and spalling of the concrete cover if the concrete tensile strength is exceeded. The mechanism by which steel embedded in concrete corrodes in presence of chloride is the local breakdown of the passive layer formed in the highly alkaline condition of the concrete. It is assumed that corrosion initiates when a critical chloride content reaches the rebar surface. The mathematical formulation idealized the corrosion sequence as a two-stage process: an initiation stage, during which chloride ions penetrate to the reinforcing steel surface and depassivate it, and a propagation stage, in which active corrosion takes place until cracking of the concrete cover has occurred. The aim of this research is to develop computer tools to evaluate the duration of the service life of reinforced concrete structures, considering both the initiation and propagation periods. Such tools must offer a friendly interface to facilitate its use by the researchers even though their background is not in numerical simulation. For the evaluation of the initiation period different tools have been developed: Program TavProbabilidade: provides means to carry out a probability analysis of a chloride ingress model. Such a tool is necessary due to the lack of data and general uncertainties associated with the phenomenon of the chloride diffusion. It differs from the deterministic approach because it computes not just a chloride profile at a certain age, but a range of chloride profiles for each probability or occurrence. Program TavProbabilidade_Fiabilidade: carries out reliability analyses of the initiation period. It takes into account the critical value of the chloride concentration on the steel that causes breakdown of the passive layer and the beginning of the propagation stage. It differs from the deterministic analysis in that it does not predict if the corrosion is going to begin or not, but to quantifies the probability of corrosion initiation. Program TavDif_1D: was created to do a one dimension deterministic analysis of the chloride diffusion process by the finite element method (FEM) which numerically solves Fick’second Law. Despite of the different FEM solver already developed in one dimension, the decision to create a new code (TavDif_1D) was taken because of the need to have a solver with friendly interface for pre- and post-process according to the need of IETCC. An innovative tool was also developed with a systematic method devised to compare the ability of the different 1D models to predict the actual evolution of chloride ingress based on experimental measurements, and also to quantify the degree of agreement of the models with each others. For the evaluation of the entire service life of the structure: a computer program has been developed using finite elements method to do the coupling of both service life periods: initiation and propagation. The program for 2D (TavDif_2D) allows the complementary use of two external programs in a unique friendly interface: • GMSH - an finite element mesh generator and post-processing viewer • OOFEM – a finite element solver. This program (TavDif_2D) is responsible to decide in each time step when and where to start applying the boundary conditions of fracture mechanics module in function of the amount of chloride concentration and corrosion parameters (Icorr, etc). This program is also responsible to verify the presence and the degree of fracture in each element to send the Information of diffusion coefficient variation with the crack width. • GMSH - an finite element mesh generator and post-processing viewer • OOFEM – a finite element solver. The advantages of the FEM with the interface provided by the tool are: • the flexibility to input the data such as material property and boundary conditions as time dependent function. • the flexibility to predict the chloride concentration profile for different geometries. • the possibility to couple chloride diffusion (initiation stage) with chemical and mechanical behavior (propagation stage). The OOFEM code had to be modified to accept temperature, humidity and the time dependent values for the material properties, which is necessary to adequately describe the environmental variations. A 3-D simulation has been performed to simulate the behavior of the beam on both, action of the external load and the internal load caused by the corrosion products, using elements of imbedded fracture in order to plot the curve of the deflection of the central region of the beam versus the external load to compare with the experimental data.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Propagation Mechanism modeling in the Near-Region of Arbitrary Cross-Sectional Tunnels.

Along with the increase of the use of working frequencies in advanced radio communication systems, the near-region inside tunnels lengthens considerably and even occupies the whole propagation cell or the entire length of some short tunnels. This paper analytically models the propagation mechanisms and their dividing point in the near-region of arbitrary cross-sectional tunnels for the first time. To begin with, the propagation losses owing to the free space mechanism and the multimode waveguide mechanism are modeled, respectively. Then, by conjunctively employing the propagation theory and the three-dimensional solid geometry, the paper presents a general model for the dividing point between two propagation mechanisms. It is worthy to mention that this model can be applied in arbitrary cross-sectional tunnels. Furthermore, the general dividing point model is specified in rectangular, circular, and arched tunnels, respectively. Five groups of measurements are used to justify the model in different tunnels at different frequencies. Finally, in order to facilitate the use of the model, simplified analytical solutions for the dividing point in five specific application situations are derived. The results in this paper could help deepen the insight into the propagation mechanisms in tunnels.

Use of prediction methods for patent and trademark applications in Spain

Patent and trademark offices which run according to principles of new management have an inherent need for dependable forecasting data in planning capacity and service levels. The ability of the Spanish Office of Patents and Trademarks to carry out efficient planning of its resource needs requires the use of methods which allow it to predict the changes in the number of patent and trademark applications at different time horizons. The approach for the prediction of time series of Spanish patents and trademarks applications (1979e2009) was based on the use of different techniques of time series prediction in a short-term horizon. The methods used can be grouped into two specifics areas: regression models of trends and time series models. The results of this study show that it is possible to model the series of patents and trademarks applications with different models, especially ARIMA, with satisfactory model adjustment and relatively low error.

Prediction of near field overpressure from quarry blasting

This paper investigates the propagation of airblast or pressure waves in air produced by bench blasting (i.e. detonation of the explosive in a row of blastholes, breaking the burden of rock towards the free vertical face of the block). Peak overpressure is calculated as a function of blasting parameters (explosive mass per delay and velocity at which the detonation sequence proceeds along the bench) and the polar coordinates of the position of interest (distance to the source and azimuth with respect to the free face). The model has been fitted to empirical data using linear least squares. The data set is composed of 122 airblast records monitored at distances less than 400 m in 41 production blasts carried out in two quarries. The model is statistically significant and has a determination coefficient of 0.87. The formula is validated from 12 airblast measurements gathered in five additional blasts.

A Novel Method for Radio Propagation Simulation Based on Automatic 3D Environment Reconstruction

In this paper, a novel method to simulate radio propagation is presented. The method consists of two steps: automatic 3D scenario reconstruction and propagation modeling. For 3D reconstruction, a machine learning algorithm is adopted and improved to automatically recognize objects in pictures taken from target regions, and 3D models are generated based on the recognized objects. The propagation model employs a ray tracing algorithm to compute signal strength for each point on the constructed 3D map. Our proposition reduces, or even eliminates, infrastructure cost and human efforts during the construction of realistic 3D scenes used in radio propagation modeling. In addition, the results obtained from our propagation model proves to be both accurate and efficient

HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

A novel method for radio propagation simulation based on automatic 3D environment reconstruction

In this paper, a novel method to simulate radio propagation is presented. The method consists of two steps: automatic 3D scenario reconstruction and propagation modeling. For 3D reconstruction, a machine learning algorithm is adopted and improved to automatically recognize objects in pictures taken from target region, and 3D models are generated based on the recognized objects. The propagation model employs a ray tracing algorithm to compute signal strength for each point on the constructed 3D map. By comparing with other methods, the work presented in this paper makes contributions on reducing human efforts and cost in constructing 3D scene; moreover, the developed propagation model proves its potential in both accuracy and efficiency.

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.

An online failure prediction system for private IaaS platforms

The size and complexity of cloud environments make them prone to failures. The traditional approach to achieve a high dependability for these systems relies on constant monitoring. However, this method is purely reactive. A more proactive approach is provided by online failure prediction (OFP) techniques. In this paper, we describe a OFP system for private IaaS platforms, currently under development, that combines di_erent types of data input, including monitoring information, event logs, and failure data. In addition, this system operates at both the physical and virtual planes of the cloud, taking into account the relationships between nodes and failure propagation mechanisms that are unique to cloud environments.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Structure of intermediate shocks in collisionsless anisotropic Hall-magnetohydrodynamics plasma models

The existence of discontinuities within the double-adiabatic Hall-magnetohydrodynamics (MHD) model is discussed. These solutions are transitional layers where some of the plasma properties change from one equilibrium state to another. Under the assumption of traveling wave solutions with velocity C and propagation angle θ with respect to the ambient magnetic field, the Hall-MHD model reduces to a dynamical system and the waves are heteroclinic orbits joining two different fixed points. The analysis of the fixed points rules out the existence of rotational discontinuities. Simple considerations about the Hamiltonian nature of the system show that, unlike dissipative models, the intermediate shock waves are organized in branches in parameter space, i.e., they occur if a given relationship between θ and C is satisfied. Electron-polarized (ion-polarized) shock waves exhibit, in addition to a reversal of the magnetic field component tangential to the shock front, a maximum (minimum) of the magnetic field amplitude. The jumps of the magnetic field and the relative specific volume between the downstream and the upstream states as a function of the plasma properties are presented. The organization in parameter space of localized structures including in the model the influence of finite Larmor radius is discussed

1RM prediction: a data mining case study

In the last few years there has been a heightened interest in data treatment and analysis with the aim of discovering hidden knowledge and eliciting relationships and patterns within this data. Data mining techniques (also known as Knowledge Discovery in Databases) have been applied over a wide range of fields such as marketing, investment, fraud detection, manufacturing, telecommunications and health. In this study, well-known data mining techniques such as artificial neural networks (ANN), genetic programming (GP), forward selection linear regression (LR) and k-means clustering techniques, are proposed to the health and sports community in order to aid with resistance training prescription. Appropriate resistance training prescription is effective for developing fitness, health and for enhancing general quality of life. Resistance exercise intensity is commonly prescribed as a percent of the one repetition maximum. 1RM, dynamic muscular strength, one repetition maximum or one execution maximum, is operationally defined as the heaviest load that can be moved over a specific range of motion, one time and with correct performance. The safety of the 1RM assessment has been questioned as such an enormous effort may lead to muscular injury. Prediction equations could help to tackle the problem of predicting the 1RM from submaximal loads, in order to avoid or at least, reduce the associated risks. We built different models from data on 30 men who performed up to 5 sets to exhaustion at different percentages of the 1RM in the bench press action, until reaching their actual 1RM. Also, a comparison of different existing prediction equations is carried out. The LR model seems to outperform the ANN and GP models for the 1RM prediction in the range between 1 and 10 repetitions. At 75% of the 1RM some subjects (n = 5) could perform 13 repetitions with proper technique in the bench press action, whilst other subjects (n = 20) performed statistically significant (p < 0:05) more repetitions at 70% than at 75% of their actual 1RM in the bench press action. Rate of perceived exertion (RPE) seems not to be a good predictor for 1RM when all the sets are performed until exhaustion, as no significant differences (p < 0:05) were found in the RPE at 75%, 80% and 90% of the 1RM. Also, years of experience and weekly hours of strength training are better correlated to 1RM (p < 0:05) than body weight. O'Connor et al. 1RM prediction equation seems to arise from the data gathered and seems to be the most accurate 1RM prediction equation from those proposed in literature and used in this study. Epley's 1RM prediction equation is reproduced by means of data simulation from 1RM literature equations. Finally, future lines of research are proposed related to the problem of the 1RM prediction by means of genetic algorithms, neural networks and clustering techniques. RESUMEN En los últimos años ha habido un creciente interés en el tratamiento y análisis de datos con el propósito de descubrir relaciones, patrones y conocimiento oculto en los mismos. Las técnicas de data mining (también llamadas de \Descubrimiento de conocimiento en bases de datos\) se han aplicado consistentemente a lo gran de un gran espectro de áreas como el marketing, inversiones, detección de fraude, producción industrial, telecomunicaciones y salud. En este estudio, técnicas bien conocidas de data mining como las redes neuronales artificiales (ANN), programación genética (GP), regresión lineal con selección hacia adelante (LR) y la técnica de clustering k-means, se proponen a la comunidad del deporte y la salud con el objetivo de ayudar con la prescripción del entrenamiento de fuerza. Una apropiada prescripción de entrenamiento de fuerza es efectiva no solo para mejorar el estado de forma general, sino para mejorar la salud e incrementar la calidad de vida. La intensidad en un ejercicio de fuerza se prescribe generalmente como un porcentaje de la repetición máxima. 1RM, fuerza muscular dinámica, una repetición máxima o una ejecución máxima, se define operacionalmente como la carga máxima que puede ser movida en un rango de movimiento específico, una vez y con una técnica correcta. La seguridad de las pruebas de 1RM ha sido cuestionada debido a que el gran esfuerzo requerido para llevarlas a cabo puede derivar en serias lesiones musculares. Las ecuaciones predictivas pueden ayudar a atajar el problema de la predicción de la 1RM con cargas sub-máximas y son empleadas con el propósito de eliminar o al menos, reducir los riesgos asociados. En este estudio, se construyeron distintos modelos a partir de los datos recogidos de 30 hombres que realizaron hasta 5 series al fallo en el ejercicio press de banca a distintos porcentajes de la 1RM, hasta llegar a su 1RM real. También se muestra una comparación de algunas de las distintas ecuaciones de predicción propuestas con anterioridad. El modelo LR parece superar a los modelos ANN y GP para la predicción de la 1RM entre 1 y 10 repeticiones. Al 75% de la 1RM algunos sujetos (n = 5) pudieron realizar 13 repeticiones con una técnica apropiada en el ejercicio press de banca, mientras que otros (n = 20) realizaron significativamente (p < 0:05) más repeticiones al 70% que al 75% de su 1RM en el press de banca. El ínndice de esfuerzo percibido (RPE) parece no ser un buen predictor del 1RM cuando todas las series se realizan al fallo, puesto que no existen diferencias signifiativas (p < 0:05) en el RPE al 75%, 80% y el 90% de la 1RM. Además, los años de experiencia y las horas semanales dedicadas al entrenamiento de fuerza están más correlacionadas con la 1RM (p < 0:05) que el peso corporal. La ecuación de O'Connor et al. parece surgir de los datos recogidos y parece ser la ecuación de predicción de 1RM más precisa de aquellas propuestas en la literatura y empleadas en este estudio. La ecuación de predicción de la 1RM de Epley es reproducida mediante simulación de datos a partir de algunas ecuaciones de predicción de la 1RM propuestas con anterioridad. Finalmente, se proponen futuras líneas de investigación relacionadas con el problema de la predicción de la 1RM mediante algoritmos genéticos, redes neuronales y técnicas de clustering.

Continuum models of the mechanical behavior of rolled and die-cast magnesium alloys

En los últimos años ha habido una fuerte tendencia a disminuir las emisiones de CO2 y su negativo impacto medioambiental. En la industria del transporte, reducir el peso de los vehículos aparece como la mejor opción para alcanzar este objetivo. Las aleaciones de Mg constituyen un material con gran potencial para el ahorro de peso. Durante la última década se han realizado muchos esfuerzos encaminados a entender los mecanismos de deformación que gobiernan la plasticidad de estos materiales y así, las aleaciones de Mg de colada inyectadas a alta presión y forjadas son todavía objeto de intensas campañas de investigación. Es ahora necesario desarrollar modelos que contemplen la complejidad inherente de los procesos de deformación de éstos. Esta tesis doctoral constituye un intento de entender mejor la relación entre la microestructura y el comportamiento mecánico de aleaciones de Mg, y dará como resultado modelos de policristales capaces de predecir propiedades macro- y microscópicas. La deformación plástica de las aleaciones de Mg está gobernada por una combinación de mecanismos de deformación característicos de la estructura cristalina hexagonal, que incluye el deslizamiento cristalográfico en planos basales, prismáticos y piramidales, así como el maclado. Las aleaciones de Mg de forja presentan texturas fuertes y por tanto los mecanismos de deformación activos dependen de la orientación de la carga aplicada. En este trabajo se ha desarrollado un modelo de plasticidad cristalina por elementos finitos con el objetivo de entender el comportamiento macro- y micromecánico de la aleación de Mg laminada AZ31 (Mg-3wt.%Al-1wt.%Zn). Este modelo, que incorpora el maclado y tiene en cuenta el endurecimiento por deformación debido a las interacciones dislocación-dislocación, dislocación-macla y macla-macla, predice exitosamente las actividades de los distintos mecanismos de deformación y la evolución de la textura con la deformación. Además, se ha llevado a cabo un estudio que combina difracción de electrones retrodispersados en tres dimensiones y modelización para investigar el efecto de los límites de grano en la propagación del maclado en el mismo material. Ambos, experimentos y simulaciones, confirman que el ángulo de desorientación tiene una influencia decisiva en la propagación del maclado. Se ha observado que los efectos no-Schmid, esto es, eventos de deformación plástica que no cumplen la ley de Schmid con respecto a la carga aplicada, no tienen lugar en la vecindad de los límites de baja desorientación y se hacen más frecuentes a medida que la desorientación aumenta. Esta investigación también prueba que la morfología de las maclas está altamente influenciada por su factor de Schmid. Es conocido que los procesos de colada suelen dar lugar a la formación de microestructuras con una microporosidad elevada, lo cuál afecta negativamente a sus propiedades mecánicas. La aplicación de presión hidrostática después de la colada puede reducir la porosidad y mejorar las propiedades aunque es poco conocido su efecto en el tamaño y morfología de los poros. En este trabajo se ha utilizado un enfoque mixto experimentalcomputacional, basado en tomografía de rayos X, análisis de imagen y análisis por elementos finitos, para la determinación de la distribución tridimensional (3D) de la porosidad y de la evolución de ésta con la presión hidrostática en la aleación de Mg AZ91 (Mg- 9wt.%Al-1wt.%Zn) colada por inyección a alta presión. La distribución real de los poros en 3D obtenida por tomografía se utilizó como input para las simulaciones por elementos finitos. Los resultados revelan que la aplicación de presión tiene una influencia significativa tanto en el cambio de volumen como en el cambio de forma de los poros que han sido cuantificados con precisión. Se ha observado que la reducción del tamaño de éstos está íntimamente ligada con su volumen inicial. En conclusión, el modelo de plasticidad cristalina propuesto en este trabajo describe con éxito los mecanismos intrínsecos de la deformación de las aleaciones de Mg a escalas meso- y microscópica. Más especificamente, es capaz de capturar las activadades del deslizamiento cristalográfico y maclado, sus interacciones, así como los efectos en la porosidad derivados de los procesos de colada. ---ABSTRACT--- The last few years have seen a growing effort to reduce CO2 emissions and their negative environmental impact. In the transport industry more specifically, vehicle weight reduction appears as the most straightforward option to achieve this objective. To this end, Mg alloys constitute a significant weight saving material alternative. Many efforts have been devoted over the last decade to understand the main mechanisms governing the plasticity of these materials and, despite being already widely used, high pressure die-casting and wrought Mg alloys are still the subject of intense research campaigns. Developing models that can contemplate the complexity inherent to the deformation of Mg alloys is now timely. This PhD thesis constitutes an attempt to better understand the relationship between the microstructure and the mechanical behavior of Mg alloys, as it will result in the design of polycrystalline models that successfully predict macro- and microscopic properties. Plastic deformation of Mg alloys is driven by a combination of deformation mechanisms specific to their hexagonal crystal structure, namely, basal, prismatic and pyramidal dislocation slip as well as twinning. Wrought Mg alloys present strong textures and thus specific deformation mechanisms are preferentially activated depending on the orientation of the applied load. In this work a crystal plasticity finite element model has been developed in order to understand the macro- and micromechanical behavior of a rolled Mg AZ31 alloy (Mg-3wt.%Al-1wt.%Zn). The model includes twinning and accounts for slip-slip, slip-twin and twin-twin hardening interactions. Upon calibration and validation against experiments, the model successfully predicts the activity of the various deformation mechanisms and the evolution of the texture at different deformation stages. Furthermore, a combined three-dimensional electron backscatter diffraction and modeling approach has been adopted to investigate the effect of grain boundaries on twin propagation in the same material. Both experiments and simulations confirm that the misorientation angle has a critical influence on twin propagation. Non-Schmid effects, i.e. plastic deformation events that do not comply with the Schmid law with respect to the applied stress, are absent in the vicinity of low misorientation boundaries and become more abundant as misorientation angle increases. This research also proves that twin morphology is highly influenced by the Schmid factor. Finally, casting processes usually lead to the formation of significant amounts of gas and shrinkage microporosity, which adversely affect the mechanical properties. The application of hydrostatic pressure after casting can reduce the porosity and improve the properties but little is known about the effects on the casting’s pores size and morphology. In this work, an experimental-computational approach based on X-ray computed tomography, image analysis and finite element analysis is utilized for the determination of the 3D porosity distribution and its evolution with hydrostatic pressure in a high pressure diecast Mg AZ91 alloy (Mg-9wt.%Al-1wt.%Zn). The real 3D pore distribution obtained by tomography is used as input for the finite element simulations using an isotropic hardening law. The model is calibrated and validated against experimental stress-strain curves. The results reveal that the pressure treatment has a significant influence both on the volume and shape changes of individuals pores, which have been precisely quantified, and which are found to be related to the initial pore volume. In conclusion, the crystal plasticity model proposed in this work successfully describes the intrinsic deformation mechanisms of Mg alloys both at the mesoscale and the microscale. More specifically, it can capture slip and twin activities, their interactions, as well as the potential porosity effects arising from casting processes.

Analysis of pixel-mapping rounding on geometric distortion as a prediction for view synthesis distortion

We analyze the performance of the geometric distortion, incurred when coding depth maps in 3D Video, as an estimator of the distortion of synthesized views. Our analysis is motivated by the need of reducing the computational complexity required for the computation of synthesis distortion in 3D video encoders. We propose several geometric distortion models that capture (i) the geometric distortion caused by the depth coding error, and (ii) the pixel-mapping precision in view synthesis. Our analysis starts with the evaluation of the correlation of geometric distortion values obtained with these models and the actual distortion on synthesized views. Then, the different geometric distortion models are employed in the rate-distortion optimization cycle of depth map coding, in order to assess the results obtained by the correlation analysis. Results show that one of the geometric distortion models is performing consistently better than the other models in all tests. Therefore, it can be used as a reasonable estimator of the synthesis distortion in low complexity depth encoders.