661 research outputs found

    Complex systems methods characterizing nonlinear processes in the near-Earth electromagnetic environment: recent advances and open challenges

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    Learning from successful applications of methods originating in statistical mechanics, complex systems science, or information theory in one scientific field (e.g., atmospheric physics or climatology) can provide important insights or conceptual ideas for other areas (e.g., space sciences) or even stimulate new research questions and approaches. For instance, quantification and attribution of dynamical complexity in output time series of nonlinear dynamical systems is a key challenge across scientific disciplines. Especially in the field of space physics, an early and accurate detection of characteristic dissimilarity between normal and abnormal states (e.g., pre-storm activity vs. magnetic storms) has the potential to vastly improve space weather diagnosis and, consequently, the mitigation of space weather hazards. This review provides a systematic overview on existing nonlinear dynamical systems-based methodologies along with key results of their previous applications in a space physics context, which particularly illustrates how complementary modern complex systems approaches have recently shaped our understanding of nonlinear magnetospheric variability. The rising number of corresponding studies demonstrates that the multiplicity of nonlinear time series analysis methods developed during the last decades offers great potentials for uncovering relevant yet complex processes interlinking different geospace subsystems, variables and spatiotemporal scales

    Hierarchical Coordinated Fast Frequency Control using Inverter-Based Resources for Next-Generation Power Grids

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    The proportion of inverter-connected renewable energy resources (RES) in the grid is expanding, primarily displacing conventional synchronous generators. This shift significantly impacts the objective of maintaining grid stability and reliable operations. The increased penetration of RESs contributes to the variability of active power supply and a decrease in the rotational inertia of the grid, resulting in faster system dynamics and larger, more frequent frequency events. These emerging challenges could make traditional centralized frequency control strategies ineffective, necessitating the adoption of modern, high-bandwidth control schemes. In this thesis, we propose a novel hierarchical and coordinated real-time frequency control scheme. It leverages advancements in grid monitoring and communication infrastructure to employ local, flexible inverter-based resources for promptly correcting power imbalances in the system. We solve two research problems that, when combined, yield a practical, real-time, next-generation frequency control scheme. This scheme blends localized control with high-bandwidth wide-area coordination. For the first problem, we propose a layered architecture where control, estimation, and optimization tasks are efficiently aggregated and decentralized across the system. This layered control structure, comprising decentralized, distributed, and centralized assets, enables fast, localized control responses to local power imbalances, integrated with wide- area coordination. For the second problem, we propose a data-driven extension to the framework to enhance model flexibility. Achieving high accuracy in system models used for control design is a considerable challenge due to the increasing scale, complexity, and evolving dynamics of the power system. In our proposed approach, we leverage collected data to provide direct data-driven controller designs for fast frequency regulation. The devised scheme ensures swift and effective frequency control for the bulk grid by accurately re-dispatching inverter-based resources (IBRs) to compensate for unmeasured net-load changes. These changes are computed in real-time using frequency and area tie power flow measurements, alongside collected historical data, thus eliminating reliance on proprietary power system models. Validated through detailed simulations under various scenarios such as load increase, generation trips, and three-phase faults, the scheme is practical, provides rapid, localized frequency control, safeguards data privacy, and eliminates the need for system models of the increasingly complex power system

    Contributions to improve the technologies supporting unmanned aircraft operations

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    Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

    Structural optimization in steel structures, algorithms and applications

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Flow Dynamics and Aeroacoustics of Flow-induced Vibration of Bluff Bodies

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    Flow-induced vibration (FIV), a common phenomenon of fluid-structure interaction (FSI), is found everywhere and at all scales in the applications of marine, civil, aeronautical, and power engineering. The study of FIV phenomenology, ranging from fatigue and concomitant damage of structures to its exploitation for energy extraction, has been an active area of fundamental research. The research on the mechanism supporting the amplifying, stabilizing, and suppressing of FIV has practical implications for the structural design for optimal engineering fatigue control, energy utilization, etc. Moreover, the noise propagation generated from FIV is also accompanying environmental pollution that should not be ignored. However, past research on the FIV supported by nonlinear spring and the corresponding detailed FSI characteristics are limited. The present study will conduct a numerical FIV study of bluff bodies mounted by linear and nonlinear springs, and analyze the impact of stiffness nonlinearity on the FIV responses, including the amplitude variation, phase change, frequency variation, and wake pattern. The technical method used in this part is direct Computational Fluid Dynamics/Computational Structural Dynamics (CFD/CSD) simulation with the full-order model (FOM), via the coupled Navier-Stokes and body-structure equations. Additionally, the present study investigates the geometrical influences on FIV response and the mechanism underpinning the transfer from lock-in range to desynchronization or galloping range. Different body shapes, varied Reynolds numbers, and reduced velocity will involve many cases, as a result, expensive time will be consumed if the corresponding grids are generated and FOM calculations are carried out for each case. This part of the research will be mainly based on the data-driven stability analysis using the reduced-order model (ROM), and FOM based on CFD/CSD method will be used as supplementary for comparison. ROM could also provide the modal analysis and physical perspective that are not available for FOM. Combining ROM and FOM methods, this thesis explores the mode transformation and interaction in the lock-in behavior of laminar flow past a circular cylinder. For the galloping analysis, it is observed very small changes in the windward interior angle of an isosceles-trapezoidal body can provoke or suppress galloping---indeed, a small decrease or increase (low to 1°) of the windward interior angle from a right angle (90°) can result in a significant enhancement and complete suppression, respectively, of the galloping oscillations. This supports our hypothesis that the contraction and/or expansion of the cross-section in the streamline direction is significantly responsible for the galloping response. Furthermore, one novel methodology of data-driven stability analysis via the superposition of 2-D reduced-order modes (SROM) for the purpose of performing modal analysis and stability predictions of 3-D flow-induced vibration with spanwise shear inflow is presented. Lastly, noise propagation from energy harvesters based on the FIV mechanism also deserves attention. Owing that there is limited past research on noise propagation from oscillating cylinders, an investigation on aeroacoustics study of different oscillation patterns of single cylinder and tandem cylinders is carried out

    Edoardo Benvenuto Prize. Collection of papers

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    The promotion of studies and research on the science and art of building in their historical development constitutes the objective that the Edoardo Benvenuto Association has set itself, since its establishment, in order to honor the memory of Edoardo Benvenuto (1940-1998). The Association in recent years has achieved interesting results by developing various activities such as: organization of national and international meetings, conferences, study days; collaborations with national and foreign research institutions; promotion of the editorial series “Between Mechanics and Architecture"; activation of the portal Bibliotheca Mechanica Architectonica, first “open source” digitized library dedicated to historical research on mechanical and architectural texts. But perhaps the most qualifying initiative was the institution of the Edoardo Benvenuto Prize, arrived in 2019 in its twelfth edition, reserved for young researchers in the field of historical studies on science and the art of building. The awarding of the Prize takes place after an in-depth examination of the texts received by the Association by an international commission of experts. The purpose of this book is to collect and present the most recent studies and publications produced by the winners of the various editions of the Edoardo Benvenuto Prize

    Effect of weather forecast uncertainty on offshore wind farm availability assessment

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    With the growing demand for offshore wind energy and the continued drive for reduced levelised cost of energy, it is necessary to make operation and maintenance activities more effective and reduce related costs. A key factor in achieving this aim is to more representatively model operation and maintenance activities, and to do this, simulation models should include more accurate weather forecasting algorithms. In this paper, three weather forecast modelling methods are used to generate projections of wind and wave values which are then used as inputs in an operation and maintenance simulation model. These methods include Markov Chains, gradient boosting and a novel hybrid regression/statistical approach which has been developed and is presented herein. The change in key performance indicators after the wind farm lifespan is simulated using the forecasting methods and then compared to one another. It is shown that the Markov Chain and hybrid models numerically perform similarly, although the hybrid method has some additional desirable features. Finally, it is shown that the effect of this type of modelling uncertainty leads to significantly differing performance estimates through the operation and maintenance model
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