Almacenamiento de energía térmica por calor latente en los edificios: bases para la optimización de aplicaciones pasivas, opacas y traslúcidas

La principal motivación para la elección del tema de la tesis es nuestra realidad energética y ambiental. Y más específicamente, la necesidad urgente de dar una respuesta a esta realidad desde el sector de la edificación. Por lo que, el trabajo parte de la búsqueda de soluciones pasivas que ayuden a la reducción del consumo energético y de las emisiones de C02 de los edificios, tanto nuevos como existentes. El objeto de estudio son aplicaciones innovadoras, basadas en el uso de materiales reactivos, con un efecto térmico de memoria bidireccional. La energía es un elemento imprescindible para el desarrollo. Sin embargo, el modelo energético predominante, basado principalmente en la utilización de combustibles de origen fósil, es uno de los importantes responsables del deterioro ambiental que sufre el planeta. Además, sus reservas son limitadas y están concentradas en unas pocas regiones del mundo, lo que genera problemas de dependencia, competitividad y de seguridad de suministro. Dado el gran potencial de ahorro energético del sector de la edificación, la Unión Europea en sus directivas enfatiza la necesidad de mejorar la eficiencia energética de los edificios. Añadiendo, además, la obligatoriedad de desarrollar edificios “energía casi nula”, cuyo prerrequisito es tener un muy alto rendimiento energético. En España, los edificios son responsables del 31% del consumo de energía primaria. La mayor parte de este consumo se relaciona a la utilización de sistemas activos de acondicionamiento. Una medida efectiva para reducir la demanda es mejorar la envolvente. Sin embargo, hay que buscar estrategias adicionales para aumentar aún más la eficiencia de los edificios nuevos y existentes. Para los climas de España, el uso de la inercia térmica ha probado ser una estrategia válida. Sin embargo, su funcionamiento está vinculado al peso y al volumen de los materiales utilizados. Esto limita sus posibilidades en la rehabilitación energética y en los nuevos edificios basados en la construcción ligera. Una alternativa es el uso de aplicaciones de almacenamiento térmico por calor latente, utilizando materiales de cambio de fase (PCM). Los PCM son sustancias con un muy alto calor de fusión, capaces de almacenar una gran cantidad de energía térmica sin requerir aumentos significativos de peso o volumen. Estas características los hacen idóneos para reducir el consumo relacionado con el acondicionamiento térmico, en edificios nuevos y existentes. En la parte preliminar de la investigación, se encontró que para lograr un aprovechamiento óptimo de las aplicaciones con PCM es necesario tener un conocimiento profundo de su funcionamiento y de las variables del sistema. De ahí que el objetivo principal de la presente tesis sea: establecer las bases para la optimizatión integral de las aplicaciones con almacenamiento de energía térmica por calor latente, identificando y validando sus variables más relevantes. La investigación consta de tres partes. La primera, documental, sistematizando y jerarquizando la información científica publicada; la segunda, numérica, basada en un análisis paramétrico de una aplicación con PCM, utilizando simulaciones térmicas; y la tercera, experimental, monitorizando el funcionamiento térmico y energético de diferentes aplicaciones con PCM en módulos a escala real. Los resultados brindan un más profundo entendimiento del funcionamiento de las aplicaciones evaluadas. Han permitido identificar sus variables relevantes, cuantificar su influencia, y determinar condiciones óptimas para su utilización así como situaciones en las que sería muy difícil justificar su uso. En el proceso, se realizó la caracterización térmica y energética de aplicaciones con PCM, tanto opacas como traslúcidas. Además, se ha encontrado que las aplicaciones con PCM son capaces de aumentar la eficiencia energética inclusive en recintos con diseños optimizados, demostrando ser una de las estrategias adecuadas para lograr el muy alto desempeño energético requerido en los edificios energía nula. ABSTRACT The main motivation for choosing the theme of the thesis is our energy and environmental reality. And more specifically, the urgent need to respond to this reality from the building sector. This is why, the work start with the search of passive solutions that help reduce energy consumption and C02 emissions of buildings, in both new and existing ones. The object of study is innovative applications based on the use of responsive materials, with bidirectional thermal memory. Energy is an essential element for development. However, the predominant energy model, based primarily on the use of fossil fuels, is one of the major responsible for the environmental deterioration of the planet, the cause of most of the CO2 emissions. Furthermore, reserves of fossil fuels are limited and are concentrated in a few regions of the world, which creates issues related to dependency, competitiveness, and security of supply. Given the large potential for energy savings in the building sector, the European Union in its directives emphasizes the need to improve energy efficiency in buildings. Also, adding the obligation to develop "nearly zero energy" buildings, whose first prerequisite is to achieve a very high energy efficiency. In Spain, buildings are responsible for 31% of primary energy consumption and most of this consumption is related to the used of HVAC systems. One of the most effective measures to reduce demand is to improve the envelope. However, it is necessary to look for additional strategies to further increase the efficiency of new and existing buildings. For the predominant climates in Spain, use of the thermal inertia may be a valid strategy. Nevertheless, its operation is linked to weight and volume of the materials used. This limits their possibilities in the existing buildings energy retrofitting and in the new buildings based on lightweight construction. An alternative is the use of latent heat thermal energy storage applications (LHTES), using phase change materials (PCM). PCM are substances with a high heat of fusion, capable of storing a large amount of thermal energy without requiring significant increases in weight or volume. These features make them ideal for reducing energy consumption associated with thermal conditioning in both new and existing buildings. In the preliminary part of the investigation, it was found that to get optimum utilization of the PCM applications is needed to have a deep understanding of its operation and, in particular, how the system variables affect its performance. Hence, the main objective of this thesis is: to establish the basis for the integral optimization of applications with latent heat thermal energy storage, identifying and validating the most relevant variables. The research comprises of three parts. The first, documentary, systematizing and prioritizing published scientific information. The second, numeric, based on a parametric analysis of an application PCM using thermal simulations. The third, experimental, monitoring the thermal and energy performance of different applications with PCM on real scale test cells. The results provide a complete understanding of the functioning of the evaluated LHTES application. They have allowed to identify their relevant variables, quantify their influence and determine optimum conditions for use as well as situations where it would be very difficult to justify its use. In the process, it was carried out the power and thermal characterization of various opaque and translucent PCM applications. Furthermore, it has been found that applications with PCM can increase the energy efficiency, even in buildings with optimized designs; proving to be one of the appropriate measures to achieve the high energy performance required in zero energy buildings.

An efficient, parametric fixpoint algorithm for analysis of java bytecode

Abstract interpretation has been widely used for the analysis of object-oriented languages and, in particular, Java source and bytecode. However, while most existing work deals with the problem of flnding expressive abstract domains that track accurately the characteristics of a particular concrete property, the underlying flxpoint algorithms have received comparatively less attention. In fact, many existing (abstract interpretation based—) flxpoint algorithms rely on relatively inefHcient techniques for solving inter-procedural caligraphs or are speciflc and tied to particular analyses. We also argüe that the design of an efficient fixpoint algorithm is pivotal to supporting the analysis of large programs. In this paper we introduce a novel algorithm for analysis of Java bytecode which includes a number of optimizations in order to reduce the number of iterations. The algorithm is parametric -in the sense that it is independent of the abstract domain used and it can be applied to different domains as "plug-ins"-, multivariant, and flow-sensitive. Also, is based on a program transformation, prior to the analysis, that results in a highly uniform representation of all the features in the language and therefore simplifies analysis. Detailed descriptions of decompilation solutions are given and discussed with an example. We also provide some performance data from a preliminary implementation of the analysis.

An efficient, parametric fixpoint algorithm for incremental analysis of java bytecode

Abstract interpretation has been widely used for the analysis of object-oriented languages and, more precisely, Java source and bytecode. However, while most of the existing work deals with the problem of finding expressive abstract domains that track accurately the characteristics of a particular concrete property, the underlying fixpoint algorithms have received comparatively less attention. In fact, many existing (abstract interpretation based) fixpoint algorithms rely on relatively inefficient techniques to solve inter-procedural call graphs or are specific and tied to particular analyses. We argue that the design of an efficient fixpoint algorithm is pivotal to support the analysis of large programs. In this paper we introduce a novel algorithm for analysis of Java bytecode which includes a number of optimizations in order to reduce the number of iterations. Also, the algorithm is parametric in the sense that it is independent of the abstract domain used and it can be applied to different domains as "plug-ins". It is also incremental in the sense that, if desired, analysis data can be saved so that only a reduced amount of reanalysis is needed after a small program change, which can be instrumental for large programs. The algorithm is also multivariant and flowsensitive. Finally, another interesting characteristic of the algorithm is that it is based on a program transformation, prior to the analysis, that results in a highly uniform representation of all the features in the language and therefore simplifies analysis. Detailed descriptions of decompilation solutions are provided and discussed with an example.

A parametric, experimental analysis of conical vortices on curved roofs of low-rise buildings

Different methods to reduce the high suction caused by conical vortices have been reported in the literature: vertical parapets, either solid or porous, placed at the roof edges being the most analysed configuration. Another method for alleviating the high suction peaks due to conical vortices is the use of some non-standard parapet configuration like cantilever parapets. In this paper the influence of roof curvature on the conical vortex pattern appearing on a curved roof (Fig. 1) when subject to oblique winds is experimentally analysed by testing the mean pressure distribution on the curved roofs of low-rise building models in a wind tunnel. Also, the efficiency of cantilever parapets to reduce mean suction loads on curved roofs is experimentally checked. Very high suction loads have been measured on curved roofs, the magnitude of these high suction loads being significantly decreased when cantilever parapets are used. Thus, the suitability of these parapets to reduce wind pressure loads on curved roofs is demonstrated.

Comparative analysis of meta-analysis methods: when to use which?

Background: Several meta-analysis methods can be used to quantitatively combine the results of a group of experiments, including the weighted mean difference, statistical vote counting, the parametric response ratio and the non-parametric response ratio. The software engineering community has focused on the weighted mean difference method. However, other meta-analysis methods have distinct strengths, such as being able to be used when variances are not reported. There are as yet no guidelines to indicate which method is best for use in each case. Aim: Compile a set of rules that SE researchers can use to ascertain which aggregation method is best for use in the synthesis phase of a systematic review. Method: Monte Carlo simulation varying the number of experiments in the meta analyses, the number of subjects that they include, their variance and effect size. We empirically calculated the reliability and statistical power in each case Results: WMD is generally reliable if the variance is low, whereas its power depends on the effect size and number of subjects per meta-analysis; the reliability of RR is generally unaffected by changes in variance, but it does require more subjects than WMD to be powerful; NPRR is the most reliable method, but it is not very powerful; SVC behaves well when the effect size is moderate, but is less reliable with other effect sizes. Detailed tables of results are annexed. Conclusions: Before undertaking statistical aggregation in software engineering, it is worthwhile checking whether there is any appreciable difference in the reliability and power of the methods. If there is, software engineers should select the method that optimizes both parameters.

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.

A generic framework for the analysis and specialization of logic programs

The relationship between abstract interpretation and partial deduction has received considerable attention and (partial) integrations have been proposed starting from both the partial deduction and abstract interpretation perspectives. In this work we present what we argüe is the first fully described generic algorithm for efñcient and precise integration of abstract interpretation and partial deduction. Taking as starting point state-of-the-art algorithms for context-sensitive, polyvariant abstract interpretation and (abstract) partial deduction, we present an algorithm which combines the best of both worlds. Key ingredients include the accurate success propagation inherent to abstract interpretation and the powerful program transformations achievable by partial deduction. In our algorithm, the calis which appear in the analysis graph are not analyzed w.r.t. the original definition of the procedure but w.r.t. specialized definitions of these procedures. Such specialized definitions are obtained by applying both unfolding and abstract executability. Our framework is parametric w.r.t. different control strategies and abstract domains. Different combinations of such parameters correspond to existing algorithms for program analysis and specialization. Simultaneously, our approach opens the door to the efñcient computation of strictly more precise results than those achievable by each of the individual techniques. The algorithm is now one of the key components of the CiaoPP analysis and specialization system.

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Simplified computational method for non-linear seismic analysis of bridges

After the experience gained during the past years it seems clear that nonlinear analysis of bridges are very important to compute ductility demands and to localize potential hinges. This is specially true for irregular bridges in which it is not clear weather or not it is possible to use a linear computation followed by a correction using a behaviour factor. To simplify the numerical effort several approximate methods have been proposed. Among them, the so-called Dynamic Plastic Hinge Method in which an evolutionary shape function is used to reduce the structure to a single degree of freedom system seems to mantein a good balance between accuracy and simplicity. This paper presents results obtained in a parametric study conducted under the auspicies of PREC-8 european research program.

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

O Botafumeiro: parametric pumping in the Middle Age

The pendular motion of a giant censer (O Botafumeiro) that hangs in the transept of the cathedral of Santiago de Compostela, and is cyclically pumped by men who pull at the supporting rope, is analyzed. Maximum angular amplitude attainable, and number of cycles and time needed to attain it, are calculated; the results agree with observed values (~ 82°, ~ 17 cycles, ~ 80 seconds) to the few percent accuracy of both the analysis and the observations and parameter measurements. The energy gain in a pumping cycle is obtained for an arbitrary pumping procedure to two orders in the small fractional change of pendular length; the relevance of the ratio (characteristic radial acceleration during pumping)/g to the gain is discussed- Effects due to rope mass, air drag on both Censer and rope, and the fact that the Censer is not a point mass, are considered. If the pumping cycle is inverted once the maximum amplitude has been attained, the Censer could be swiftly brought to rest, avoiding the usual violent stop. Historically recorded accidents, rope shape, and the influence of relevant parameters on the motion are discussed.

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data

Background DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity. Results Pixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains. Conclusions A user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/ webcite.

Extracting energy from Vortex-Induced Vibrations: A parametric study

Here, Vortex-Induced Vibrations (VIVs) of a circular cylinder are analyzed as a potential source for energy harvesting. To this end, VIV is described by a one-degree-of-freedom model where fluid forces are introduced from experimental data from forced vibration tests. The influence of some influencing parameters, like the mass ratio m∗ or the mechanical damping ζ in the energy conversion factor is investigated. The analysis reveals that: (i) the maximum efficiency ηM is principally influenced by the mass-damping parameter m∗ζ and there is an optimum value of m∗ζ where ηM presents a maximum; (ii) the range of reduced velocities with significant efficiency is mainly governed by m∗, and (iii) it seems that encouraging high efficiency values can be achieved for high Reynolds numbers.

Numerical analysis of deflagration initation by a hot jet

Numerical simulations of axisymmetric reactive jets with one-step Arrhenius kinetics are used to investigate the problem of deflagration initiation in a premixed fuel–air mixture by the sudden discharge of a hot jet of its adiabatic reaction products. For the moderately large values of the jet Reynolds number considered in the computations, chemical reaction is seen to occur initially in the thin mixing layer that separates the hot products from the cold reactants. This mixing layer is wrapped around by the starting vortex, thereby enhancing mixing at the jet head, which is followed by an annular mixing layer that trails behind, connecting the leading vortex with the orifice rim. A successful deflagration is seen to develop for values of the orifice radius larger than a critical value a c in the order of the flame thickness of the planar deflagration δL. Introduction of appropriate scales provides the dimensionless formulation of the problem, with flame initiation characterised in terms of a critical Damköhler number Δc=(a d/δL)2, whose parametric dependence is investigated. The numerical computations reveal that, while the jet Reynolds number exerts a limited influence on the criticality conditions, the effect of the reactant diffusivity on ignition is much more pronounced, with the value of Δc increasing significantly with increasing Lewis numbers. The reactant diffusivity affects also the way ignition takes place, so that for reactants with the flame develops as a result of ignition in the annular mixing layer surrounding the developing jet stem, whereas for highly diffusive reactants with Lewis numbers sufficiently smaller than unity combustion is initiated in the mixed core formed around the starting vortex. The analysis provides increased understanding of deflagration initiation processes, including the effects of differential diffusion, and points to the need for further investigations corporating detailed chemistry models for specific fuel–air mixtures.

Instability Analysis of Incompressible Open Cavity Flows

El problema del flujo sobre una cavidad abierta ha sido estudiado en profundidad en la literatura, tanto por el interés académico del problema como por sus aplicaciones prácticas en gran variedad de problemas ingenieriles, como puede ser el alojamiento del tren de aterrizaje de aeronaves, o el depósito de agua de aviones contraincendios. Desde hace muchos a˜nos se estudian los distintos tipos de inestabilidades asociadas a este problema: los modos bidimensionales en la capa de cortadura, y los modos tridimensionales en el torbellino de recirculación principal dentro de la cavidad. En esta tesis se presenta un estudio paramétrico completo del límite incompresible del problema, empleando la herramienta de estabilidad lineal conocida como BiGlobal. Esta aproximación permite contemplar la estabilidad global del flujo, y obtener tanto la forma como las características de los modos propios del problema físico, sean estables o inestables. El estudio realizado permite caracterizar con gran detalle todos los modos relevantes, así como la envolvente de estabilidad en el espacio paramétrico del problema incompresible (Mach nulo, variación de Reynolds, espesor de capa límite incidente, relación altura/profundidad de la cavidad, y longitud característica de la perturbación en la dirección transversal). A la luz de los resultados obtenidos se proponen una serie de relaciones entre los parámetros y características de los modos principales, como por ejemplo entre el Reynolds crítico de un modo, y la longitud característica del mismo. Los resultados numéricos se contrastan con una campaña experimental, siendo la principal conclusión de dicha comparación que los modos lineales están presentes en el flujo real saturado, pero que existen diferencias notables en frecuencia entre las predicciones teóricas y los experimentos. Para intentar determinar la naturaleza de dichas diferencias se realiza una simulación numérica directa tridimensional, y se utiliza un algoritmo de DMD (descomposición dinámica de modos) para describir el proceso de saturación. ABSTRACT The problem of the flow over an open cavity has been studied in depth in the literature, both for being an interesting academical problem and due to the multitude of industrial applications, like the landing gear of aircraft, or the water deposit of firefighter airplanes. The different types of instabilities appearing in this flow studied in the literature are two: the two-dimensional shear layer modes, and the three-dimensional modes that appear in the main recirculating vortex inside the cavity. In this thesis a parametric study in the incompressible limit of the problem is presented, using the linear stability analysis known as BiGlobal. This approximation allows to obtain the global stability behaviour of the flow, and to capture both the morphological features and the characteristics of the eigenmodes of the physical problem, whether they are stable or unstable. The study presented here characterizes with great detail all the relevant eigenmodes, as well as the hypersurface of instability on the parameter space of the incompressible problem (Mach equal to zero, and variation of the Reynolds number, the incoming boundary layer thickness, the length to depth aspect ratio of the cavity and the spanwise length of the perturbation). The results allow to construct parametric relations between the characteristics of the leading eigenmodes and the parameters of the problem, like for example the one existing between the critical Reynolds number and its characteristic length. The numerical results presented here are compared with those of an experimental campaign, with the main conclusion of said comparison being that the linear eigenmode are present in the real saturated flow, albeit with some significant differences in the frequencies of the experiments and those predicted by the theory. To try to determine the nature of those differences a three-dimensional direct numerical simulation, analyzed with Dynamic Mode Decomposition algorithm, was used to describe the process of saturation.