Remediation efficiency of vapour extraction of sandy soils contaminated with cyclohexane: influence of air flow rate, water and natural organic matter content

Abstract This work reports the analysis of the efficiency and time of soil remediation using vapour extraction as well as provides comparison of results using both, prepared and real soils. The main objectives were: (i) to analyse the efficiency and time of remediation according to the water and natural organic matter content of the soil; and (ii) to assess if a previous study, performed using prepared soils, could help to preview the process viability in real conditions. For sandy soils with negligible clay content, artificially contaminated with cyclohexane before vapour extraction, it was concluded that (i) the increase of soil water content and mainly of natural organic matter content influenced negatively the remediation process, making it less efficient, more time consuming, and consequently more expensive; and (ii) a previous study using prepared soils of similar characteristics has proven helpful for previewing the process viability in real conditions.

Remediation of soils combining soil vapor extraction and bioremediation: benzene

This work reports the study of the combination of soil vapor extraction (SVE) with bioremediation (BR) to remediate soils contaminated with benzene. Soils contaminated with benzene with different water and natural organic matter contents were studied. The main goals were: (i) evaluate the performance of SVE regarding the remediation time and the process efficiency; (ii) study the combination of both technologies in order to identify the best option capable to achieve the legal clean up goals; and (iii) evaluate the influence of soil water content (SWC) and natural organic matter (NOM) on SVE and BR. The remediation experiments performed in soils contaminated with benzene allowed concluding that: (i) SVE presented (a) efficiencies above 92% for sandy soils and above 78% for humic soils; (b) and remediation times from 2 to 45 h, depending on the soil; (ii) BR showed to be an efficient technology to complement SVE; (iii) (a) SWC showed minimum impact on SVE when high airflow rates were used and led to higher remediation times for lower flow rates; (b) NOM as source of microorganisms and nutrients enhanced BR but hindered the SVE due the limitation on the mass transfer of benzene from the soil to the gas phase.

Electrodialytic remediation of two types of air pollution control residues and their applicability in construction materials

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente Perfil de Engenharia de Sistemas Ambientais

Predictions of the arrival time of coronal mass ejections at 1AU: an analysis of the causes of errors

Three existing models of Interplanetary Coronal Mass Ejection (ICME) transit between the Sun and the Earth are compared to coronagraph and in situ observations: all three models are found to perform with a similar level of accuracy (i.e. an average error between observed and predicted 1AU transit times of approximately 11 h). To improve long-term space weather prediction, factors influencing CME transit are investigated. Both the removal of the plane of sky projection (as suffered by coronagraph derived speeds of Earth directed CMEs) and the use of observed values of solar wind speed, fail to significantly improve transit time prediction. However, a correlation is found to exist between the late/early arrival of an ICME and the width of the preceding sheath region, suggesting that the error is a geometrical effect that can only be removed by a more accurate determination of a CME trajectory and expansion. The correlation between magnetic field intensity and speed of ejecta at 1AU is also investigated. It is found to be weak in the body of the ICME, but strong in the sheath, if the upstream solar wind conditions are taken into account.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Improving Analytical Travel Time Estimation for Transportation Planning Models

This dissertation aimed to improve travel time estimation for the purpose of transportation planning by developing a travel time estimation method that incorporates the effects of signal timing plans, which were difficult to consider in planning models. For this purpose, an analytical model has been developed. The model parameters were calibrated based on data from CORSIM microscopic simulation, with signal timing plans optimized using the TRANSYT-7F software. Independent variables in the model are link length, free-flow speed, and traffic volumes from the competing turning movements. The developed model has three advantages compared to traditional link-based or node-based models. First, the model considers the influence of signal timing plans for a variety of traffic volume combinations without requiring signal timing information as input. Second, the model describes the non-uniform spatial distribution of delay along a link, this being able to estimate the impacts of queues at different upstream locations of an intersection and attribute delays to a subject link and upstream link. Third, the model shows promise of improving the accuracy of travel time prediction. The mean absolute percentage error (MAPE) of the model is 13% for a set of field data from Minnesota Department of Transportation (MDOT); this is close to the MAPE of uniform delay in the HCM 2000 method (11%). The HCM is the industrial accepted analytical model in the existing literature, but it requires signal timing information as input for calculating delays. The developed model also outperforms the HCM 2000 method for a set of Miami-Dade County data that represent congested traffic conditions, with a MAPE of 29%, compared to 31% of the HCM 2000 method. The advantages of the proposed model make it feasible for application to a large network without the burden of signal timing input, while improving the accuracy of travel time estimation. An assignment model with the developed travel time estimation method has been implemented in a South Florida planning model, which improved assignment results.

Biorremediação de solos contaminados com produtos petrolíferos

Mestrado em Engenharia Química. Ramo Tecnologias de Protecção Ambiental.

Biocomplementation of SVE to achieve clean-up goals in soils contaminated with toluene and xylene

Soil vapor extraction (SVE) and bioremediation (BR) are two of the most common soil remediation technologies. Their application is widespread; however, both present limitations, namely related to the efficiencies of SVE on organic soils and to the remediation times of some BR processes. This work aimed to study the combination of these two technologies in order to verify the achievement of the legal clean-up goals in soil remediation projects involving seven different simulated soils separately contaminated with toluene and xylene. The remediations consisted of the application of SVE followed by biostimulation. The results show that the combination of these two technologies is effective and manages to achieve the clean-up goals imposed by the Spanish Legislation. Under the experimental conditions used in this work, SVE is sufficient for the remediation of soils, contaminated separately with toluene and xylene, with organic matter contents (OMC) below 4 %. In soils with higher OMC, the use of BR, as a complementary technology, and when the concentration of contaminant in the gas phase of the soil reaches values near 1 mg/L, allows the achievement of the clean-up goals. The OMC was a key parameter because it hindered SVE due to adsorption phenomena but enhanced the BR process because it acted as a microorganism and nutrient source.

Sistema de apoio ao utilizador de transportes públicos

Nos dias de hoje usar o transporte público para nos deslocarmos de uma determinada origem para um determinado destino é uma realidade na vida da maioria das pessoas. Muitas destas deslocações fazem parte da rotina diária do cidadão, que depende destes transportes para as suas atividades do dia-a-dia. Nos últimos anos, o número de cidadãos que usa os transportes públicos como meio de deslocação tem vindo a aumentar consideravelmente. Contudo, a maioria dos operadores de transportes públicos pecam pela falta de pontualidade dos seus serviços, e pela falta de informação disponível ao cidadão acerca dos horários dos mesmos em tempo real. Tendo este problema em conta, foi desenvolvida uma solução capaz de realizar uma previsão do tempo de chegada de um transporte público, ao longo de todo o seu serviço. Previsão essa que é atualizada ao longo do percurso de forma a reduzir a margem de erro da informação apresentada. Com esta informação o cidadão pode planear melhor o seu dia e decidir qual é a melhor altura para se deslocar para a paragem, evitando ao máximo a perda de tempo à espera do seu transporte público. A solução final foi desenvolvida com a ajuda da empresa BEWARE e teve como objetivo a criação de uma aplicação web capaz de apresentar os tempos de espera dos autocarros em diferentes tipos de vista, bem como o acompanhamento do mesmo ao longo do percurso. Toda a informação utilizada na aplicação web foi criada por dois serviços de apoio que efetuam o controlo do autocarro ao longo do percurso, bem como os cálculos da previsão dos tempos de espera. O projeto foi dividido em quatro constituintes que foram repetidas durante o desenvolvimento da solução. A primeira constou na análise do problema, no levantamento e definição dos requisitos. A segunda incluiu o desenvolvimento de um algoritmo capaz de validar a posição do autocarro ao longo do seu percurso, detetando a paragem onde este se encontra e a hora de chegada à mesma. A terceira abrangeu o desenvolvimento de um algoritmo capaz de prever o tempo de chegada de um autocarro às paragens definidas na sua rota, recorrendo ao histórico de viagens realizadas anteriormente. A quarta consistiu no desenvolvimento da aplicação web, implementando todas as funcionalidades necessárias para que a aplicação consiga realizar o acompanhamento do autocarro no percurso, a consulta dos tempos de chegada e da previsão dos tempos às paragens seguintes recorrendo a três tipos de vistas diferentes, e a possibilidade de agendar notificações de forma a receber no email as previsões dos tempos de chegada nos dias e horas mais significativos para o utilizador.

Framework for Traffic Pattern Identification: Required Step for Short-term Forecasting

In the world of transport management, the term ‘anticipation’ is gradually replacing ‘reaction’. Indeed, the ability to forecast traffic evolution in a network should ideally form the basis for many traffic management strategies and multiple ITS applications. Real-time prediction capabilities are therefore becoming a concrete need for the management of networks, both for urban and interurban environments, and today’s road operator has increasingly complex and exacting requirements. Recognising temporal patterns in traffic or the manner in which sequential traffic events evolve over time have been important considerations in short-term traffic forecasting. However, little work has been conducted in the area of identifying or associating traffic pattern occurrence with prevailing traffic conditions. This paper presents a framework for detection pattern identification based on finite mixture models using the EM algorithm for parameter estimation. The computation results have been conducted taking into account the traffic data available in an urban network.

Avaliação do processo oxidativo avançado por permanganato de potássio no tratamento de solos contaminados com idrocarbonetos poliaromáticos

The soil contamination with petroleum is one of the major concern of industries operating in the field and also of environmental agencies. The petroleum consists mainly of alkanes and aromatic hydrocarbons. The most common examples of hydrocarbons polyaromatic are: naphthalene, anthracene, phenanthrene, benzopyrene and their various isomers. These substances cause adverse effects on human and the environment. Thus, the main objective of this work is to study the advanced oxidation process using the oxidant potassium permanganate (KMnO4) for remediation of soils contaminated with two polyaromatic hydrocarbons (PAHs): anthracene and phenanthrene. This study was conducted at bench scale, where the first stage was at batch experiment, using the variables: the time and oxidant dosage in the soil. The second stage was the remediation conducted in continous by a fix column, to this stage, the only variable was remediation time. The concentration of oxidant in this stage was based on the best result obtained in the tests at batch, 2,464 mg / L. The results of degradation these contaminants were satisfactory, at the following dosages and time: (a) 5g of oxidant per kg soil for 48 hours, it was obtained residual contaminants 28 mg phenanthrene and 1.25 mg anthracene per kg of soil and (b) for 7g of oxidant per kg soil in 48 hours remaining 24 mg phenanthrene and anthracene 0.77 mg per kg soil, and therefore below the intervention limit residential and industrial proposed by the State Company of Environmental Sao Paulo (CETESB)

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

A constraint-based approach to quality assurance in service choreographies.

Knowledge about the quality characteristics (QoS) of service com- positions is crucial for determining their usability and economic value. Ser- vice quality is usually regulated using Service Level Agreements (SLA). While end-to-end SLAs are well suited for request-reply interactions, more complex, decentralized, multiparticipant compositions (service choreographies) typ- ically involve multiple message exchanges between stateful parties and the corresponding SLAs thus encompass several cooperating parties with interde- pendent QoS. The usual approaches to determining QoS ranges structurally (which are by construction easily composable) are not applicable in this sce- nario. Additionally, the intervening SLAs may depend on the exchanged data. We present an approach to data-aware QoS assurance in choreographies through the automatic derivation of composable QoS models from partici- pant descriptions. Such models are based on a message typing system with size constraints and are derived using abstract interpretation. The models ob- tained have multiple uses including run-time prediction, adaptive participant selection, or design-time compliance checking. We also present an experimen- tal evaluation and discuss the benefits of the proposed approach.

Propuesta de algoritmos de predicción de glucosa en pacientes diabéticos

La diabetes mellitus es el conjunto de alteraciones provocadas por un defecto en la cantidad de insulina secretada o por un aprovechamiento deficiente de la misma. Es causa directa de complicaciones a corto, medio y largo plazo que disminuyen la calidad y las expectativas de vida de las personas con diabetes. La diabetes mellitus es en la actualidad uno de los problemas más importantes de salud. Ha triplicado su prevalencia en los últimos 20 anos y para el año 2025 se espera que existan casi 300 millones de personas con diabetes. Este aumento de la prevalencia junto con la morbi-mortalidad asociada a sus complicaciones micro y macro-vasculares convierten la diabetes en una carga para los sistemas sanitarios, sus recursos económicos y sus profesionales, haciendo de la enfermedad un problema individual y de salud pública de enormes proporciones. De momento no existe cura a esta enfermedad, de modo que el objetivo terapéutico del tratamiento de la diabetes se centra en la normalización de la glucemia intentando minimizar los eventos de hiper e hipoglucemia y evitando la aparición o al menos retrasando la evolución de las complicaciones vasculares, que constituyen la principal causa de morbi-mortalidad de las personas con diabetes. Un adecuado control diabetológico implica un tratamiento individualizado que considere multitud de factores para cada paciente (edad, actividad física, hábitos alimentarios, presencia de complicaciones asociadas o no a la diabetes, factores culturales, etc.). Sin embargo, a corto plazo, las dos variables más influyentes que el paciente ha de manejar para intervenir sobre su nivel glucémico son la insulina administrada y la dieta. Ambas presentan un retardo entre el momento de su aplicación y el comienzo de su acción, asociado a la absorción de los mismos. Por este motivo la capacidad de predecir la evolución del perfil glucémico en un futuro cercano, ayudara al paciente a tomar las decisiones adecuadas para mantener un buen control de su enfermedad y evitar situaciones de riesgo. Este es el objetivo de la predicción en diabetes: adelantar la evolución del perfil glucémico en un futuro cercano para ayudar al paciente a adaptar su estilo de vida y sus acciones correctoras, con el propósito de que sus niveles de glucemia se aproximen a los de una persona sana, evitando así los síntomas y complicaciones de un mal control. La aparición reciente de los sistemas de monitorización continua de glucosa ha proporcionado nuevas alternativas. La disponibilidad de un registro exhaustivo de las variaciones del perfil glucémico, con un periodo de muestreo de entre uno y cinco minutos, ha favorecido el planteamiento de nuevos modelos que tratan de predecir la glucemia utilizando tan solo las medidas anteriores de glucemia o al menos reduciendo significativamente la información de entrada a los algoritmos. El hecho de requerir menor intervención por parte del paciente, abre nuevas posibilidades de aplicación de los predictores de glucemia, haciéndose viable su uso en tiempo real, como sistemas de ayuda a la decisión, como detectores de situaciones de riesgo o integrados en algoritmos automáticos de control. En esta tesis doctoral se proponen diferentes algoritmos de predicción de glucemia para pacientes con diabetes, basados en la información registrada por un sistema de monitorización continua de glucosa así como incorporando la información de la insulina administrada y la ingesta de carbohidratos. Los algoritmos propuestos han sido evaluados en simulación y utilizando datos de pacientes registrados en diferentes estudios clínicos. Para ello se ha desarrollado una amplia metodología, que trata de caracterizar las prestaciones de los modelos de predicción desde todos los puntos de vista: precisión, retardo, ruido y capacidad de detección de situaciones de riesgo. Se han desarrollado las herramientas de simulación necesarias y se han analizado y preparado las bases de datos de pacientes. También se ha probado uno de los algoritmos propuestos para comprobar la validez de la predicción en tiempo real en un escenario clínico. Se han desarrollado las herramientas que han permitido llevar a cabo el protocolo experimental definido, en el que el paciente consulta la predicción bajo demanda y tiene el control sobre las variables metabólicas. Este experimento ha permitido valorar el impacto sobre el control glucémico del uso de la predicción de glucosa. ABSTRACT Diabetes mellitus is the set of alterations caused by a defect in the amount of secreted insulin or a suboptimal use of insulin. It causes complications in the short, medium and long term that affect the quality of life and reduce the life expectancy of people with diabetes. Diabetes mellitus is currently one of the most important health problems. Prevalence has tripled in the past 20 years and estimations point out that it will affect almost 300 million people by 2025. Due to this increased prevalence, as well as to morbidity and mortality associated with micro- and macrovascular complications, diabetes has become a burden on health systems, their financial resources and their professionals, thus making the disease a major individual and a public health problem. There is currently no cure for this disease, so that the therapeutic goal of diabetes treatment focuses on normalizing blood glucose events. The aim is to minimize hyper- and hypoglycemia and to avoid, or at least to delay, the appearance and development of vascular complications, which are the main cause of morbidity and mortality among people with diabetes. A suitable, individualized and controlled treatment for diabetes involves many factors that need to be considered for each patient: age, physical activity, eating habits, presence of complications related or unrelated to diabetes, cultural factors, etc. However, in the short term, the two most influential variables that the patient has available in order to manage his/her glycemic levels are administered insulin doses and diet. Both suffer from a delay between their time of application and the onset of the action associated with their absorption. Therefore, the ability to predict the evolution of the glycemic profile in the near future could help the patient to make appropriate decisions on how to maintain good control of his/her disease and to avoid risky situations. Hence, the main goal of glucose prediction in diabetes consists of advancing the evolution of glycemic profiles in the near future. This would assist the patient in adapting his/her lifestyle and in taking corrective actions in a way that blood glucose levels approach those of a healthy person, consequently avoiding the symptoms and complications of a poor glucose control. The recent emergence of continuous glucose monitoring systems has provided new alternatives in this field. The availability of continuous records of changes in glycemic profiles (with a sampling period of one or five minutes) has enabled the design of new models which seek to predict blood glucose by using automatically read glucose measurements only (or at least, reducing significantly the data input manually to the algorithms). By requiring less intervention by the patient, new possibilities are open for the application of glucose predictors, making its use feasible in real-time applications, such as: decision support systems, hypo- and hyperglycemia detectors, integration into automated control algorithms, etc. In this thesis, different glucose prediction algorithms are proposed for patients with diabetes. These are based on information recorded by a continuous glucose monitoring system and incorporate information of the administered insulin and carbohydrate intakes. The proposed algorithms have been evaluated in-silico and using patients’ data recorded in different clinical trials. A complete methodology has been developed to characterize the performance of predictive models from all points of view: accuracy, delay, noise and ability to detect hypo- and hyperglycemia. In addition, simulation tools and patient databases have been deployed. One of the proposed algorithms has additionally been evaluated in terms of real-time prediction performance in a clinical scenario in which the patient checked his/her glucose predictions on demand and he/she had control on his/her metabolic variables. This has allowed assessing the impact of using glucose prediction on glycemic control. The tools to carry out the defined experimental protocols were also developed in this thesis.

Predicción de la velocidad crítica de flameo mediante el uso de modelos aeroelásticos extrapolados en tiempo real con datos de vuelo

La Aeroelasticidad fue definida por Arthur Collar en 1947 como "el estudio de la interacción mutua entre fuerzas inerciales, elásticas y aerodinámicas actuando sobre elementos estructurales expuestos a una corriente de aire". Actualmente, esta definición se ha extendido hasta abarcar la influencia del control („Aeroservoelasticidad‟) e, incluso, de la temperatura („Aerotermoelasticidad‟). En el ámbito de la Ingeniería Aeronáutica, los fenómenos aeroelásticos, tanto estáticos (divergencia, inversión de mando) como dinámicos (flameo, bataneo) son bien conocidos desde los inicios de la Aviación. Las lecciones aprendidas a lo largo de la Historia Aeronáutica han permitido establecer criterios de diseño destinados a mitigar la probabilidad de sufrir fenómenos aeroelásticos adversos durante la vida operativa de una aeronave. Adicionalmente, el gran avance experimentado durante esta última década en el campo de la Aerodinámica Computacional y en la modelización aeroelástica ha permitido mejorar la fiabilidad en el cálculo de las condiciones de flameo de una aeronave en su fase de diseño. Sin embargo, aún hoy, los ensayos en vuelo siguen siendo necesarios para validar modelos aeroelásticos, verificar que la aeronave está libre de inestabilidades aeroelásticas y certificar sus distintas envolventes. En particular, durante el proceso de expansión de la envolvente de una aeronave en altitud/velocidad, se requiere predecir en tiempo real las condiciones de flameo y, en consecuencia, evitarlas. A tal efecto, en el ámbito de los ensayos en vuelo, se han desarrollado diversas metodologías que predicen, en tiempo real, las condiciones de flameo en función de condiciones de vuelo ya verificadas como libres de inestabilidades aeroelásticas. De entre todas ellas, aquella que relaciona el amortiguamiento y la velocidad con un parámetro específico definido como „Margen de Flameo‟ (Flutter Margin), permanece como la técnica más común para proceder con la expansión de Envolventes en altitud/velocidad. No obstante, a pesar de su popularidad y facilidad de aplicación, dicha técnica no es adecuada cuando en la aeronave a ensayar se hallan presentes no-linealidades mecánicas como, por ejemplo, holguras. En particular, en vuelos de ensayo dedicados específicamente a expandir la envolvente en altitud/velocidad, las condiciones de „Oscilaciones de Ciclo Límite‟ (Limit Cycle Oscillations, LCOs) no pueden ser diferenciadas de manera precisa de las condiciones de flameo, llevando a una determinación excesivamente conservativa de la misma. La presente Tesis desarrolla una metodología novedosa, basada en el concepto de „Margen de Flameo‟, que permite predecir en tiempo real las condiciones de „Ciclo Límite‟, siempre que existan, distinguiéndolas de las de flameo. En una primera parte, se realiza una revisión bibliográfica de la literatura acerca de los diversos métodos de ensayo existentes para efectuar la expansión de la envolvente de una aeronave en altitud/velocidad, el efecto de las no-linealidades mecánicas en el comportamiento aeroelástico de dicha aeronave, así como una revisión de las Normas de Certificación civiles y militares respecto a este tema. En una segunda parte, se propone una metodología de expansión de envolvente en tiempo real, basada en el concepto de „Margen de Flameo‟, que tiene en cuenta la presencia de no-linealidades del tipo holgura en el sistema aeroelástico objeto de estudio. Adicionalmente, la metodología propuesta se valida contra un modelo aeroelástico bidimensional paramétrico e interactivo programado en Matlab. Para ello, se plantean las ecuaciones aeroelásticas no-estacionarias de un perfil bidimensional en la formulación espacio-estado y se incorpora la metodología anterior a través de un módulo de análisis de señal y otro módulo de predicción. En una tercera parte, se comparan las conclusiones obtenidas con las expuestas en la literatura actual y se aplica la metodología propuesta a resultados experimentales de ensayos en vuelo reales. En resumen, los principales resultados de esta Tesis son: 1. Resumen del estado del arte en los métodos de ensayo aplicados a la expansión de envolvente en altitud/velocidad y la influencia de no-linealidades mecánicas en la determinación de la misma. 2. Revisión de la normas de Certificación Civiles y las normas Militares en relación a la verificación aeroelástica de aeronaves y los límites permitidos en presencia de no-linealidades. 3. Desarrollo de una metodología de expansión de envolvente basada en el Margen de Flameo. 4. Validación de la metodología anterior contra un modelo aeroelástico bidimensional paramétrico e interactivo programado en Matlab/Simulink. 5. Análisis de los resultados obtenidos y comparación con resultados experimentales. ABSTRACT Aeroelasticity was defined by Arthur Collar in 1947 as “the study of the mutual interaction among inertia, elastic and aerodynamic forces when acting on structural elements surrounded by airflow”. Today, this definition has been updated to take into account the Controls („Aeroservoelasticity‟) and even the temperature („Aerothermoelasticity‟). Within the Aeronautical Engineering, aeroelastic phenomena, either static (divergence, aileron reversal) or dynamic (flutter, buzz), are well known since the early beginning of the Aviation. Lessons learned along the History of the Aeronautics have provided several design criteria in order to mitigate the probability of encountering adverse aeroelastic phenomena along the operational life of an aircraft. Additionally, last decade improvements experienced by the Computational Aerodynamics and aeroelastic modelization have refined the flutter onset speed calculations during the design phase of an aircraft. However, still today, flight test remains as a key tool to validate aeroelastic models, to verify flutter-free conditions and to certify the different envelopes of an aircraft. Specifically, during the envelope expansion in altitude/speed, real time prediction of flutter conditions is required in order to avoid them in flight. In that sense, within the flight test community, several methodologies have been developed to predict in real time flutter conditions based on free-flutter flight conditions. Among them, the damping versus velocity technique combined with a Flutter Margin implementation remains as the most common technique used to proceed with the envelope expansion in altitude/airspeed. However, although its popularity and „easy to implement‟ characteristics, several shortcomings can adversely affect to the identification of unstable conditions when mechanical non-linearties, as freeplay, are present. Specially, during test flights devoted to envelope expansion in altitude/airspeed, Limits Cycle Oscillations (LCOs) conditions can not be accurately distinguished from those of flutter and, in consequence, it leads to an excessively conservative envelope determination. The present Thesis develops a new methodology, based on the Flutter Margin concept, that enables in real time the prediction of the „Limit Cycle‟ conditions, whenever they exist, without degrading the capability of predicting the flutter onset speed. The first part of this Thesis presents a review of the state of the art regarding the test methods available to proceed with the envelope expansion of an aircraft in altitude/airspeed and the effect of mechanical non-linearities on the aeroelastic behavior. Also, both civil and military regulations are reviewed with respect aeroelastic investigation of air vehicles. The second part of this Thesis proposes a new methodology to perform envelope expansion in real time based on the Flutter Margin concept when non-linearities, as freeplay, are present. Additionally, this methodology is validated against a Matlab/Slimulink bidimensional aeroelastic model. This model, parametric and interactive, is formulated within the state-space field and it implements the proposed methodology through two main real time modules: A signal processing module and a prediction module. The third part of this Thesis compares the final conclusions derived from the proposed methodology with those stated by the flight test community and experimental results. In summary, the main results provided by this Thesis are: 1. State of the Art review of the test methods applied to envelope expansion in altitude/airspeed and the influence of mechanical non-linearities in its identification. 2. Review of the main civil and military regulations regarding the aeroelastic verification of air vehicles and the limits set when non-linearities are present. 3. Development of a methodology for envelope expansion based on the Flutter Margin concept. 4. A Matlab/Simulink 2D-[aeroelastic model], parametric and interactive, used as a tool to validate the proposed methodology. 5. Conclusions driven from the present Thesis and comparison with experimental results.