4,332 research outputs found

    Nondimensional Shape Optimization of Nonprismatic Beams with Sinusoidal Lateral Profile

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    The present paper deals with the optimal design of nonprismatic beams, i.e., beams with variable cross section. To set the optimization problem, Euler-Bernoulli unshearable beam theory is considered and the elastica equation expressing the transverse displacement as a function of the applied loads is reformulated into a system of four differential equations involving kinematic components and internal forces. The optimal solution (in terms of volume) must satisfy two constraints: the maximum Von Mises equivalent stress must not exceed an (ideal) strength, and the maximum vertical displacement is limited to a fraction of beam length. To evaluate the maximum equivalent stress in the beam, normal and shear stresses have been considered. The former was evaluated through the Navier formula, and the latter through a formula derived from Jourawsky and holding for straight and untwisted beams with bisymmetric variable cross sections. The optimal solutions as function of material unit weight, maximum strength, and applied load are presented and discussed. It is shown that the binding constraint is usually represented by the maximum stress in the beam, and that applied load and strength affect the solution more than material unit weight. To maintain the generality of the solution, the nondimensionalization according to Buckingham pi-theorem is implemented and a design abacus is proposed

    Time-optimal control of ship manoeuvring under wave loads

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    Ship manoeuvrability of Maritime Autonomous Surface Ships (MASS) revolutionise the maritime industry. However, this paradigm shift necessitates the advancement of manoeuvring control models to meet the complex demands of autonomous navigation. This paper addresses the need for an improved manoeuvring control model for MASS, particularly concerning path planning and tracking in the presence of wave loads. The paper establishes a comprehensive mathematical model for ship manoeuvring, considering forces acting on the ship's hull, propellers, rudders, and wave loads. A time optimisation model using a spatial reformulation approach is introduced. A nonlinear Model Predictive Control (MPC) model is presented for path planning and tracking, with a case study investigating the influence of wave load and comparing two control strategies. 10%–20% of time consumption increases if the wave load exists. This research bridges the gap in existing literature by incorporating wave loads into MPC-based control models for MASS. The findings shed light on the significance of wave loads in ship manoeuvring and provide valuable insights into effective control strategies for autonomous vessels operating in real-world sea conditions

    Optimization for Energy Management in the Community Microgrids

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    This thesis focuses on improving the energy management strategies for Community Microgrids (CMGs), which are expected to play a crucial role in the future smart grid. CMGs bring many benefits, including increased use of renewable energy, improved reliability, resiliency, and energy efficiency. An Energy Management System (EMS) is a key tool that helps in monitoring, controlling, and optimizing the operations of the CMG in a cost-effective manner. The EMS can include various functionalities like day-ahead generation scheduling, real-time scheduling, uncertainty management, and demand response programs. Generation scheduling in a microgrid is a challenging optimization problem, especially due to the intermittent nature of renewable energy. The power balance constraint, which is the balance between energy demand and generation, is difficult to satisfy due to prediction errors in energy demand and generation. Real-time scheduling, which is based on a shorter prediction horizon, reduces these errors, but the impact of uncertainties cannot be completely eliminated. In regards to demand response programs, it is challenging to design an effective model that motivates customers to voluntarily participate while benefiting the system operator. Mathematical optimization techniques have been widely used to solve power system problems, but their application is limited by the need for specific mathematical properties. Metaheuristic techniques, particularly Evolutionary Algorithms (EAs), have gained popularity for their ability to solve complex and non-linear problems. However, the traditional form of EAs may require significant computational effort for complex energy management problems in the CMG. This thesis aims to enhance the existing methods of EMS in CMGs. Improved techniques are developed for day-ahead generation scheduling, multi-stage real-time scheduling, and demand response implementation. For generation scheduling, the performance of conventional EAs is improved through an efficient heuristic. A new multi-stage scheduling framework is proposed to minimize the impact of uncertainties in real-time operations. In regards to demand response, a memetic algorithm is proposed to solve an incentive-based scheme from the perspective of an aggregator, and a price-based demand response driven by dynamic price optimization is proposed to enhance the electric vehicle hosting capacity. The proposed methods are validated through extensive numerical experiments and comparison with state-of-the-art approaches. The results confirm the effectiveness of the proposed methods in improving energy management in CMGs

    A mixed-mode dependent interface and phase-field damage model for solids with inhomogeneities

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    The developed computational approach is capable of initiating and propagating cracks inside materials and along material interfaces of general multi-domain structures under quasi-static conditions. Special attention is paid to particular situation of a solid with inhomogeneities. Description of the fracture processes are based on the theory of material damage. It introduces two independent damage parameters to distinguish between interface and internal cracks. The parameter responsible for interface cracks is defined in a thin adhesive layer of the interface and renders relation between stress and strain quantities in fashion of cohesive zone models.The second parameter is defined inside material domains and it is founded on the theory of phase-field fracture guaranteeing the material damage to occur in a thin material strip introducing a regularised model of internal cracks. Additional property of both interface and phase-field damage is their capability to distinguish between fracture modes which is useful if the structures is subjected to combined loading. The solution methodology is based on a variational approach which allows implementation of non-linear programming optimisation into standard methods of finite-element discretisation and time stepping method.Computational implementation is prepared in MATLAB whose numerical data validate developed formulation for analysis of problems of fracture in multi-domain elements of structures.Comment: 24 pages, 19 figures, to be published in Theoretical and Applied Fracture Mechanic

    An automatic method for generating multiple alignment alternatives for a railway bypass

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    This paper deals with the problem of designing a bypass on a railway line. Based on a geometrical model capable of determining automatically the need of major structures (bridges, tunnels, overpasses and underpasses), the optimal design of a railway bypass is formulated in the framework of Mixed Integer Non Linear Programming (MINLP), and it is solved with a numerical algorithm which provides different layout alternatives that are optimal solutions (local minima) from the economic point of view. The proposed method is tested on a case study with the aim of showing its practical usefulness as a support tool for engineers in order to accomplish the complex and time-consuming task to generate a set of initial alternatives for the design of a railway bypassThis research was funded by Ministerio de Ciencia e Innovación (Spain) grant number TED2021-129324B-I00, and by the collaboration agreement between Xunta de Galicia (Spain) and Universidade de Santiago de Compostela (Spain) which regulates the Specialization Campus “Campus Terra”. Additionally, the authors are grateful to Concello de Guitiriz (Spain) for financial support through the contract Optimal design of multiple alignment alternatives for a bypass on the railway line A Coruña-Palencia passing through Parga-Guitiriz (Lugo), ref. 2021-CP138 . Finally, third and fourth authors thank the support given by Xunta de Galicia (Spain) under research projects ref. ED341D R2016/023 and GI-1563ED431C2021/15, respectivelyS

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p

    Numerical simulation of surfactant flooding with relative permeability estimation using inversion method

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    Surfactant flooding attracts significant interest in the hydrocarbon industry, with a definite promise to improve oil recovery from depleting oil reserves. In this thesis, surfactant flooding is the primary area of focus as it has significant potential for integration with other chemical enhanced oil recovery techniques, including polymer, nanofluid, alkali, and foam. This combined approach has the potential to reduce interfacial tension to ultralow levels, decrease adsorption, and offer other benefits. However, due to the various mechanism, surfactant flooding poses a more complex model for simulators by encountering numerical issues (e.g., the appearance of spurious oscillations, erratic pulses, and numerical instabilities), rendering the methods ineffective. To address these challenges, the analytical modelling technique of surfactant flooding was studied, leading to the development of a novel inversion method in the MATLAB programming environment. Numerical accuracy issues were discovered in 1D models that used typical cell sizes found in well-scale models, leading to pulses in the oil bank and a dip in water saturation, particularly for low levels of adsorption, highlighting the need for more refined models. Based on these findings, we examined the surfactant flooding technique in 2D models to recover viscous oil in short reservoir aspect ratios. Instabilities such as viscous fingering and gravity tongue were observed on the flood fronts, and the magnitude of the viscous fingers was influenced by vertical dispersion, resulting in errors in computed mobility values at the fronts. Interestingly, introducing heterogeneity only minimally affected the spreading of the front and did not significantly impact viscous fingering or numerical artifacts. To optimize the nonlinearity of flow behaviour and degree of mobility control at the fronts, a homogenous model was considered to develop the inversion method. In summary, the developed inversion method accurately estimated the two-phase relative permeability curves, which were validated using fractional flow theory. The precision of the inverted curves was further improved using the optimization algorithm, demonstrating the method's ability to predict outcomes closer to the observed values for 2D models with instabilities. The obtained results are of significant value for core flood analysis, interpretation, matching, and upscaling, providing insights into the potential of surfactant flooding for enhanced oil recovery. Additionally, the use of the developed MATLAB Scripts promotes open innovation and reproducibility, contributing to the benchmarking, analytical, and numerical method development exercises for tutorials aimed at improving the overall understanding of surfactant flooding

    Distributed AC Optimal Power Flow for Generic Integrated Transmission-Distribution Systems

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    Coordination of transmission and distribution power systems is increasingly critical in the context of the ongoing energy transition. However, traditional centralized energy management faces challenges related to privacy and/or sovereignty concerns, leading to growing research interests in distributed approaches. Nevertheless, solving distributed AC optimal power flow (OPF) problems encounters difficulties due to their nonlinearity and nonconvexity, making it challenging for state-of-the-art distributed approaches. To solve this issue, the present paper focuses on investigating the distributed AC OPF problem of generic integrated transmission-distribution (ITD) systems, considering complex grid topology, by employing a new variant of the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). In contrast to the standard ALADIN, we introduce a second-order correction into ALADIN to enhance its numerical robustness and properly convexify distribution subproblems within the ALADIN framework for computing efficiency. Moreover, a rigorous proof shows that the locally quadratic convergence rate can be preserved for solving the resulting distributed nonconvex problems. Extensive numerical simulations with varying problem sizes and grid topologies demonstrate the effectiveness of the proposed algorithm, outperforming state-of-the-art approaches in terms of numerical robustness, convergence speed, and scalability

    Integration of economic MPC and modifier adaptation in slow dynamic processes with structural model uncertainty

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    Real-Time Optimization, known by its acronym RTO, uses a steady-state nonlinear model of the process to optimize a plant's economic objective subject to process constraints. This is the technology currently used in commercial RTO applications. However, no model is a perfect representation of reality, and structural and parametric model uncertainties make the optimum calculated by RTO do not match those of the actual process. One way to address this problem is to modify the optimization problem so that the Necessary Conditions of Optimality (NCO) of the problem match those of the actual plant. This strategy is known as Modifier Adaptation (MA) methodology. The MA methodology requires the gradient values of the real plant and the model to calculate the modifiers. There are several ways to accurately estimate model gradients, but estimation of the real process gradients are more difficult. In addition, the need to use stationary data is a limitation of RTO with MA, especially for slow dynamic systems. This thesis focuses on ways to mitigate the weaknesses of RTO and MA unification that we consider most critical for its application in industry. To this end, it is proposed to couple the RTO and control layers with the concepts of the Modifier Adaptation (MA) methodology by estimating process gradients or directly the MA modifiers using transient data.La Optimización en Tiempo Real, conocida por la sigla en inglés RTO usa un modelo no lineal estacionario del proceso para optimizar un objetivo económico de la planta frente a restricciones del proceso. Esta es la tecnología usada actualmente por las aplicaciones comerciales de RTO. Sin embargo, ningún modelo es una representación perfecta de la realidad y las incertidumbres estructurales y paramétricas de los modelos hacen que los óptimos calculados por la RTO no coincidan con los del proceso real. Una forma de abordar este problema es modificar el problema de optimización de modo que las condiciones necesarias de optimalidad del problema (NCO) se igualen a los de la planta real. Esa estrategia es conocida como la metodología de adaptación de modificadores (Modifier Adaptation, MA). La metodología MA necesita de los valores de gradiente de la planta real y del modelo para el cálculo de los modificadores. Hay diversas formas de estimar los gradientes del modelo con exactitud, sin embargo, la estimación en proceso real es más difícil. Además, la necesidad de usar datos en estacionario sigue siendo una limitación fundamental de la RTO con MA, principalmente para sistemas dinámicos lentos. Esta tesis se enfoca en formas de mitigar las debilidades de la unificación RTO y MA que consideramos las más críticas para su aplicación en la industria. Para eso se propone que las capas de RTO y control se unan con los conceptos de la metodología de adaptación de modificadores (Modifier Adaptation, MA) estimando los gradientes de proceso o directamente los modificadores de MA usando datos de transitorio.Escuela de DoctoradoDoctorado en Ingeniería Industria

    Challenging the gold standard: a methodological study of the quality and errors of web tracking data

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    Measuring what people consume and do online is crucial across the social sciences. In the last few years, web tracking data has gained popularity, being considered by most as the gold standard for measuring online behaviours. This thesis studies whether this prevailing notion holds true. Specifically, through a combination of traditional survey and computational methods, I assess the quality of web tracking data, its associated errors, and the consequences of these. The thesis is comprised of three distinct papers. In the first paper, inspired by the Total Survey Error, I present a Total Error framework for digital traces collected with Meters (TEM). The TEM framework describes the data generation and the analysis process for web tracking data and documents the sources of bias and variance that may arise in each step of this process. The framework suggests that metered data might indeed be affected by the error sources identified in our framework and, to some extent, biased. The second paper adopts an empirical approach to address a key error identified in the TEM framework: researchers’ failure to capture data from all the devices and browsers that individuals utilize to go online. The paper shows that tracking undercoverage is highly prevalent when using commercial panels. Additionally, through a simulation study, it demonstrates that web tracking estimates, both univariate and multivariate, are often substantially biased due to tracking undercoverage. The third paper explores the validity and reliability of web tracking data when used to measure media exposure. Merging traditional psychometric and computational techniques, I conduct a multiverse analysis to assess the predictive validity and true-score reliability of thousands of web tracking measures of media exposure. The findings show that web tracking measures have an overall low validity but remarkably high reliability. Additionally, results suggest that the design decisions made by researchers when designing web tracking measurements can have a substantial impact on their measurement properties. Collectively, this thesis challenges the prevailing belief in web tracking data as the gold standard to measure online behaviours. Methodologically, it illustrates how computational methods can be used to adapt survey methodology techniques to assess the quality of digital trace data
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