The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

Comparison of natural and forced amplification regimes in plasma-based soft-x-ray lasers seeded by high-order harmonic

The amplification of high-order harmonics (HOH) in a plasma-based amplifier is a multiscale, temporal phenomenon that couples plasma hydrodynamics, atomic processes, and HOH electromagnetic fields. We use a one-dimensional, time-dependent Maxwell-Bloch code to compare the natural amplification regime and another regime where plasma polarization is constantly forced by the HOH. In this regime, a 10-MW (i.e., 100 times higher than current seeded soft x-ray laser power), 1.5-μJ, 140-fs pulse free from the parasitic temporal structures appearing on the natural amplification regime can be obtained.

A three-dimensional dynamical model of Amundsenisen icefield, Svalbard.

Amundsenisen is an ice field, 80 km2 in area, located in Southern Spitsbergen, Svalbard. Radio-echo sounding measurements at 20 MHz show high intensity returns from a nearly flat basal reflector at four zones, all of them with ice thickness larger than 500m. These reflections suggest possible subglacial lakes. To determine whether basal liquid water is compatible with current pressure and temperature conditions, we aim at applying a thermo mechanical model with a free boundary at the bed defined as solution of a Stefan problem for the interface ice-subglaciallake. The complexity of the problem suggests the use of a bi-dimensional model, but this requires that well-defined flowlines across the zones with suspected subglacial lakes are available. We define these flow lines from the solution of a three-dimensional dynamical model, and this is the main goal of the present contribution. We apply a three-dimensional full-Stokes model of glacier dynamics to Amundsenisen icefield. We are mostly interested in the plateau zone of the icefield, so we introduce artificial vertical boundaries at the heads of the main outlet glaciers draining Amundsenisen. At these boundaries we set velocity boundary conditions. Velocities near the centres of the heads of the outlets are known from experimental measurements. The velocities at depth are calculated according to a SIA velocity-depth profile, and those at the rest of the transverse section are computed following Nye’s (1952) model. We select as southeastern boundary of the model domain an ice divide, where we set boundary conditions of zero horizontal velocities and zero vertical shear stresses. The upper boundary is a traction-free boundary. For the basal boundary conditions, on the zones of suspected subglacial lakes we set free-slip boundary conditions, while for the rest of the basal boundary we use a friction law linking the sliding velocity to the basal shear stress,in such a way that, contrary to the shallow ice approximation, the basal shear stress is not equal to the basal driving stress but rather part of the solution.

Effect of Aspect Ratio on the Three-Dimensional Global Instability Analysis of Incompressible Open Cavity Flows

The stability analysis of open cavity flows is a problem of great interest in the aeronautical industry. This type of flow can appear, for example, in landing gears or auxiliary power unit configurations. Open cavity flows is very sensitive to any change in the configuration, either physical (incoming boundary layer, Reynolds or Mach numbers) or geometrical (length to depth and length to width ratio). In this work, we have focused on the effect of geometry and of the Reynolds number on the stability properties of a threedimensional spanwise periodic cavity flow in the incompressible limit. To that end, BiGlobal analysis is used to investigate the instabilities in this configuration. The basic flow is obtained by the numerical integration of the Navier-Stokes equations with laminar boundary layers imposed upstream. The 3D perturbation, assumed to be periodic in the spanwise direction, is obtained as the solution of the global eigenvalue problem. A parametric study has been performed, analyzing the stability of the flow under variation of the Reynolds number, the L/D ratio of the cavity, and the spanwise wavenumber β. For consistency, multidomain high order numerical schemes have been used in all the computations, either basic flow or eigenvalue problems. The results allow to define the neutral curves in the range of L/D = 1 to L/D = 3. A scaling relating the frequency of the eigenmodes and the length to depth ratio is provided, based on the analysis results.

An optimization method for conceptual design in engineering systems based on high order singular value decomposition

Se presenta un nuevo método de diseño conceptual en Ingeniería Aeronáutica basado el uso de modelos reducidos, también llamados modelos sustitutos (‘surrogates’). Los ingredientes de la función objetivo se calculan para cada indiviudo mediante la utilización de modelos sustitutos asociados a las distintas disciplinas técnicas que se construyen mediante definiciones de descomposición en valores singulares de alto orden (HOSVD) e interpolaciones unidimensionales. Estos modelos sustitutos se obtienen a partir de un número limitado de cálculos CFD. Los modelos sustitutos pueden combinarse, bien con un método de optimización global de tipo algoritmo genético, o con un método local de tipo gradiente. El método resultate es flexible a la par que mucho más eficiente, computacionalmente hablando, que los modelos convencionales basados en el cálculo directo de la función objetivo, especialmente si aparecen un gran número de parámetros de diseño y/o de modelado. El método se ilustra considerando una versión simplificada del diseño conceptual de un avión. Abstract An optimization method for conceptual design in Aeronautics is presented that is based on the use of surrogate models. The various ingredients in the target function are calculated for each individual using surrogates of the associated technical disciplines that are constructed via high order singular value decomposition and one dimensional interpolation. These surrogates result from a limited number of CFD calculated snapshots. The surrogates are combined with an optimization method, which can be either a global optimization method such as a genetic algorithm or a local optimization method, such as a gradient-like method. The resulting method is both flexible and much more computationally efficient than the conventional method based on direct calculation of the target function, especially if a large number of free design parameters and/or tunablemodeling parameters are present. The method is illustrated considering a simplified version of the conceptual design of an aircraft empennage.

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.

The Dynamics of High-Speed Railway Bridges: A Review of Design Issues and New Research for Lateral Dynamics

The dynamic effects of high-speed trains on viaducts are important issues for the design of the structures, as well as for the consideration of safe running conditions for the trains. In this work we start by reviewing the relevance of some basic design aspects. The significance of impact factor envelopes for moving loads is considered first. Resonance which may be achieved for high-speed trains requires dynamic analysis, for which some key aspects are discussed. The relevance of performing a longitudinal distribution of axle loads, the number of modes taken in analysis, and the consideration of vehicle-structure interaction are discussed with representative examples. The lateral dynamic effects of running trains on bridges is of importance for laterally compliant viaducts, such as some very tall structures erected in new high-speed lines. The relevance of this study is mainly for the safety of the traffic, considering both internal actions such as the hunting motion as well as external actions such as wind or earthquakes [1]. These studies require three-dimensional dynamic coupled vehicle-bridge models, and consideration of wheel to rail contact, a phenomenon which is complex and costly to model in detail. We describe here a fully nonlinear coupled model, described in absolute coordinates and incorporated into a commercial finite element framework [2]. The wheel-rail contact has been considered using a FastSim algorithm which provides a compromise between accuracy and computational cost, and captures the main nonlinear response of the contact interface. Two applications are presented, firstly to a vehicle subject to a strong wind gust traversing a bridge, showing the relevance of the nonlinear wheel-rail contact model as well as the dynamic interaction between bridge and vehicle. The second application is to a real HS viaduct with a long continuous deck and tall piers and high lateral compliance [3]. The results show the safety of the traffic as well as the importance of considering features such as track alignment irregularities.

A three-dimensional self-adaptive hp finite element method for the characterization of waveguide discontinuities

We propose the use of a highly-accurate three-dimensional (3D) fully automatic hp-adaptive finite element method (FEM) for the characterization of rectangular waveguide discontinuities. These discontinuities are either the unavoidable result of mechanical/electrical transitions or deliberately introduced in order to perform certain electrical functions in modern communication systems. The proposed numerical method combines the geometrical flexibility of finite elements with an accuracy that is often superior to that provided by semi-analytical methods. It supports anisotropic refinements on irregular meshes with hanging nodes, and isoparametric elements. It makes use of hexahedral elements compatible with high-order H(curl)H(curl) discretizations. The 3D hp-adaptive FEM is applied for the first time to solve a wide range of 3D waveguide discontinuity problems of microwave communication systems in which exponential convergence of the error is observed.

Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Be?zier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.

Analytic free-form lens design for imaging applications with high aspect ratio

A new three-dimensional analytic optics design method is presented that enables the coupling of three ray sets with only two free-form lens surfaces. Closely related to the Simultaneous Multiple Surface method in three dimensions (SMS3D), it is derived directly from Fermat?s principle, leading to multiple sets of functional differential equations. The general solution of these equations makes it possible to calculate more than 80 coefficients for each implicit surface function. Ray tracing simulations of these free-form lenses demonstrate superior imaging performance for applications with high aspect ratio, compared to conventional rotational symmetric systems.

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

High reynolds numbers turbulent boundary layers

Esta tesis estudia las similitudes y diferencias entre los flujos turbulentos de pared de tipo externo e interno, en régimen incompresible, y a números de Reynolds moderada¬mente altos. Para ello consideramos tanto simulaciones numéricas como experimentos de capas límites con gradiente de presiones nulo y de flujos de canal, ambos a números de Reynolds en el rango δ+ ~ 500 - 2000. Estos flujos de cortadura son objeto de numerosas investigaciones debido a la gran importancia que tienen tanto a nivel tecnológico como a nivel de física fundamental. No obstante, todavía existen muchos interrogantes sobre aspectos básicos tales como la universalidad de los perfiles medios y de fluctuación de las velocidades o de la presión, tanto en la zona cercana a la pared como en la zona logarítmica, el escalado y el efecto del número de Reynolds, o las diferencias entre los flujos internos y externos en la zona exterior. En éste estudio hemos utilizado simulaciones numéricas ya existentes de canales y capas límites a números de Reynolds δ+ ~ 2000 y δ+ ~ 700, respectivamente. Para poder comparar ambos flujos a igual número de Reynolds hemos realizado una nueva simulación directa de capa límite en el rango δ+ ~ 1000-2000. Los resultados de la misma son presentados y analizados en detalle. Los datos sin postprocesar y las estadísticas ya postprocesadas están públicamente disponibles en nuestro sitio web.162 El análisis de las estadísticas usando un único punto confirma la existencia de perfiles logarítmicos para las fluctuaciones de la velocidad transversal w'2+ y de la presión p'2+ en ambos tipos de flujos, pero no para la velocidad normal v'2+ o la velocidad longitudinal u'2+. Para aceptar o rechazar la existencia de un rango logarítmico en u'2+ se requieren números de Reynolds más altos que los considerados en éste trabajo. Una de las conse¬cuencias más importantes de poseer tales perfiles es que el valor máximo de la intensidad, que se alcanza cerca de la pared, depende explícitamente del número de Reynolds. Esto ha sido confirmado tras analizar un gran número de datos experimentales y numéricos, cor¬roborando que el máximo de u'2+, p/2+, y w'2+ aumenta proporcionalmente con el log(δ+). Por otro lado, éste máximo es más intenso en los flujos externos que en los internos. La máxima diferencia ocurre en torno a y/δ ~ 0.3-0.5, siendo esta altura prácticamente independiente del número de Reynolds considerado. Estas diferencias se originan como consecuencia del carácter intermitente de las capas límites, que es inexistente en los flujos internos. La estructura de las fluctuaciones de velocidad y de presión, junto con la de los esfuer¬zos de Reynolds, se han investigado por medio de correlaciones espaciales tridimensionales considerando dos puntos de medida. Hemos obtenido que el tamaño de las mismas es gen¬eralmente mayor en canales que en capas límites, especialmente en el caso de la correlación longitudinal Cuu en la dirección del flujo. Para esta correlación se demuestra que las es¬tructuras débilmente correladas presentan longitudes de hasta 0(75), en el caso de capas límites, y de hasta 0(185) en el caso de canales. Estas longitudes se obtienen respecti-vamente en la zona logarítmica y en la zona exterior. Las longitudes correspondientes en la dirección transversal son significativamente menores en ambos flujos, 0(5 — 25). La organización espacial de las correlaciones es compatible con la de una pareja de rollos casi paralelos con dimensiones que escalan en unidades exteriores. Esta organización se mantiene al menos hasta y ~ 0.65, altura a la cual las capas límites comienzan a organi¬zarse en rollos transversales. Este comportamiento es sin embargo más débil en canales, pudiéndose observar parcialmente a partir de y ~ 0.85. Para estudiar si estas estructuras están onduladas a lo largo de la dirección transver¬sal, hemos calculado las correlaciones condicionadas a eventos intensos de la velocidad transversal w'. Estas correlaciones revelan que la ondulación de la velocidad longitudinal aumenta conforme nos alejamos de la pared, sugiriendo que las estructuras están más alineadas en la zona cercana a la pared que en la zona lejana a ella. El por qué de esta ondulación se encuentra posiblemente en la configuración a lo largo de diagonales que presenta w'. Estas estructuras no sólo están onduladas, sino que también están inclinadas respecto a la pared con ángulos que dependen de la variable considerada, de la altura, y de el contorno de correlación seleccionado. Por encima de la zona tampón e independien¬temente del número de Reynolds y tipo de flujo, Cuu presenta una inclinación máxima de unos 10°, las correlaciones Cvv y Cm son esencialmente verticales, y Cww está inclinada a unos 35°. Summary This thesis studies the similitudes and differences between external and internal in¬compressible wall-bounded turbulent flows at moderately-high Reynolds numbers. We consider numerical and experimental zero-pressure-gradient boundary layers and chan¬nels in the range of δ+ ~ 500 — 2000. These shear flows are subjects of intensive research because of their technological importance and fundamental physical interest. However, there are still open questions regarding basic aspects such as the universality of the mean and fluctuating velocity and pressure profiles at the near-wall and logarithmic regions, their scaling and the effect of the Reynolds numbers, or the differences between internal and external flows at the outer layer, to name but a few. For this study, we made use of available direct numerical simulations of channel and boundary layers reaching δ+ ~ 2000 and δ+ ~ 700, respectively. To fill the gap in the Reynolds number, a new boundary layer simulation in the range δ+ ~ 1000-2000 is presented and discussed. The original raw data and the post-processed statistics are publicly available on our website.162 The analysis of the one-point statistic confirms the existence of logarithmic profiles for the spanwise w'2+ and pressure p'2+ fluctuations for both type of flows, but not for the wall-normal v'2+ or the streamwise u'2+ velocities. To accept or reject the existence of a logarithmic range in u'2+ requires higher Reynolds numbers than the ones considered in this work. An important consequence of having such profiles is that the maximum value of the intensities, reached near the wall, depends on the Reynolds number. This was confirmed after surveying a wide number of experimental and numerical datasets, corrob¬orating that the maximum of ul2+, p'2+, and w'2+ increases proportionally to log(δ+). On the other hand, that maximum is more intense in external flows than in internal ones, differing the most around y/δ ~ 0.3-0.5, and essentially independent of the Reynolds number. We discuss that those differences are originated as a consequence of the inter¬mittent character of boundary layers that is absent in internal flows. The structure of the velocity and pressure fluctuations, together with those of the Reynolds shear stress, were investigated using three-dimensional two-point spatial correlations. We find that the correlations extend over longer distances in channels than in boundary layers, especially in the case of the streamwise correlation Cuu in the flow direc-tion. For weakly correlated structures, the maximum streamwise length of Cuu is O(78) for boundary layers and O(188) for channels, attained at the logarithmic and outer regions respectively. The corresponding lengths for the transverse velocities and for the pressure are shorter, 0(8 — 28), and of the same order for both flows. The spatial organization of the velocity correlations is shown to be consistent with a pair of quasi-streamwise rollers that scales in outer units. That organization is observed until y ~ 0.68, from which boundary layers start to organize into spanwise rollers. This effect is weaker in channels, and it appears at y ~ 0.88. We present correlations conditioned to intense events of the transversal velocity, w', to study if these structures meander along the spanwise direction. The results indicate that the streamwise velocity streaks increase their meandering proportionally to the distance to the wall, suggesting that the structures are more aligned close to the wall than far from it. The reason behind this meandering is probably due to the characteristic organization along diagonals of w'. These structures not only meander along the spanwise direction, but they are also inclined to the wall at angles that depend on the distance from the wall, on the variable being considered, and on the correlation level used to define them. Above the buffer layer and independent of the Reynolds numbers and type of flow, the maximum inclination of Cuu is about 10°, Cvv and Cpp are roughly vertical, and Cww is inclined by 35°.

A wind tunnel two-dimensional parametric investigation of biplane configurations

This paper presents an experimental and systematic investigation about how geometric parameters on a biplane configuration have an influence on aerodynamic parameters. This experimental investigation has been developed in a two-dimensional approach. Theoretical studies about biplanes configurations have been developed in the past, but there is not enough information about experimental wind tunnel data at low Reynolds number. This two-dimensional study is a first step to further tridimensional investigations about the box wing configuration. The main objective of the study is to find the relationships between the geometrical parameters which present the best aerodynamic behavior: the highest lift, the lowest drag and the lowest slope of the pitching moment. A tridimensional wing-box model will be designed following the pattern of the two dimensional study conclusions. It will respond to the geometrical relationships that have been considered to show the better aerodynamic behavior. This box-wing model will be studied in the aim of comparing the advantages and disadvantages between this biplane configuration and the plane configuration, looking for implementing the box-wing in the UAV?s field. Although the box wing configuration has been used in a small number of existing UAV, prestigious researchers have found it as a field of high aerodynamic and structural potential.

Three-dimensional mapping of real environments for cognitive robots navigation

Abstract The development of cognitive robots needs a strong “sensorial” support which should allow it to perceive the real world for interacting with it properly. Therefore the development of efficient visual-processing software to be equipped in effective artificial agents is a must. In this project we study and develop a visual-processing software that will work as the “eyes” of a cognitive robot. This software performs a three-dimensional mapping of the robot’s environment, providing it with the essential information required to make proper decisions during its navigation. Due to the complexity of this objective we have adopted the Scrum methodology in order to achieve an agile development process, which has allowed us to correct and improve in a fast way the successive versions of the product. The present project is structured in Sprints, which cover the different stages of the software development based on the requirements imposed by the robot and its real necessities. We have initially explored different commercial devices oriented to the acquisition of the required visual information, adopting the Kinect Sensor camera (Microsoft) as the most suitable option. Later on, we have studied the available software to manage the obtained visual information as well as its integration with the robot’s software, choosing the high-level platform Matlab as the common nexus to join the management of the camera, the management of the robot and the implementation of the behavioral algorithms. During the last stages the software has been developed to include the fundamental functionalities required to process the real environment, such as depth representation, segmentation, and clustering. Finally the software has been optimized to exhibit real-time processing and a suitable performance to fulfill the robot’s requirements during its operation in real situations.