Desarrollo de un Procedimiento Basado en Algoritmos de Optimización para el Dimensionado de Absorbedores Puntuales Aplicados a la Conversión de Energía Undimotriz

La energía transportada por el oleaje a través de los océanos (energía undimotriz) se enmarca dentro de las denominadas energías oceánicas. Su aprovechamiento para generar energía eléctrica (o ser aprovechada de alguna otra forma) es una idea reflejada ya hace más de dos siglos en una patente (1799). Desde entonces, y con especial intensidad desde los años 70, ha venido despertando el interés de instituciones ligadas al I+D+i y empresas del sector energético y tecnológico, debido principalmente a la magnitud del recurso disponible. Actualmente se puede considerar al sector en un estado precomercial, con un amplio rango de dispositivos y tecnologías en diferente grado de desarrollo en los que ninguno destaca sobre los otros (ni ha demostrado su viabilidad económica), y sin que se aprecie una tendencia a converger un único dispositivo (o un número reducido de ellos). El recurso energético que se está tratando de aprovechar, pese a compartir la característica de no-controlabilidad con otras fuentes de energía renovable como la eólica o la solar, presenta una variabilidad adicional. De esta manera, diferentes localizaciones, pese a poder presentar recursos de contenido energético similar, presentan oleajes de características muy diferentes en términos de alturas y periodos de oleaje, y en la dispersión estadística de estos valores. Esta variabilidad en el oleaje hace que cobre especial relevancia la adecuación de los dispositivos de aprovechamiento de energía undimotriz (WEC: Wave Energy Converter) a su localización, de cara a mejorar su viabilidad económica. Parece razonable suponer que, en un futuro, el proceso de diseño de un parque de generación undimotriz implique un rediseño (en base a una tecnología conocida) para cada proyecto de implantación en una nueva localización. El objetivo de esta tesis es plantear un procedimiento de dimensionado de una tecnología de aprovechamiento de la energía undimotriz concreta: los absorbedores puntuales. Dicha metodología de diseño se plantea como un problema de optimización matemático, el cual se resuelve utilizando un algoritmo de optimización bioinspirado: evolución diferencial. Este planteamiento permite automatizar la fase previa de dimensionado implementando la metodología en un código de programación. El proceso de diseño de un WEC es un problema de ingería complejo, por lo que no considera factible el planteamiento de un diseño completo mediante un único procedimiento de optimización matemático. En vez de eso, se platea el proceso de diseño en diferentes etapas, de manera que la metodología desarrollada en esta tesis se utilice para obtener las dimensiones básicas de una solución de referencia de WEC, la cual será utilizada como punto de partida para continuar con las etapas posteriores del proceso de diseño. La metodología de dimensionado previo presentada en esta tesis parte de unas condiciones de contorno de diseño definidas previamente, tales como: localización, características del sistema de generación de energía eléctrica (PTO: Power Take-Off), estrategia de extracción de energía eléctrica y concepto concreto de WEC). Utilizando un algoritmo de evolución diferencial multi-objetivo se obtiene un conjunto de soluciones factibles (de acuerdo con una ciertas restricciones técnicas y dimensionales) y óptimas (de acuerdo con una serie de funciones objetivo de pseudo-coste y pseudo-beneficio). Dicho conjunto de soluciones o dimensiones de WEC es utilizado como caso de referencia en las posteriores etapas de diseño. En el documento de la tesis se presentan dos versiones de dicha metodología con dos modelos diferentes de evaluación de las soluciones candidatas. Por un lado, se presenta un modelo en el dominio de la frecuencia que presenta importantes simplificaciones en cuanto al tratamiento del recurso del oleaje. Este procedimiento presenta una menor carga computacional pero una mayor incertidumbre en los resultados, la cual puede traducirse en trabajo adicional en las etapas posteriores del proceso de diseño. Sin embargo, el uso de esta metodología resulta conveniente para realizar análisis paramétricos previos de las condiciones de contorno, tales como la localización seleccionada. Por otro lado, la segunda metodología propuesta utiliza modelos en el domino estocástico, lo que aumenta la carga computacional, pero permite obtener resultados con menos incertidumbre e información estadística muy útil para el proceso de diseño. Por este motivo, esta metodología es más adecuada para su uso en un proceso de dimensionado completo de un WEC. La metodología desarrollada durante la tesis ha sido utilizada en un proyecto industrial de evaluación energética preliminar de una planta de energía undimotriz. En dicho proceso de evaluación, el método de dimensionado previo fue utilizado en una primera etapa, de cara a obtener un conjunto de soluciones factibles de acuerdo con una serie de restricciones técnicas básicas. La selección y refinamiento de la geometría de la solución geométrica de WEC propuesta fue realizada a posteriori (por otros participantes del proyecto) utilizando un modelo detallado en el dominio del tiempo y un modelo de evaluación económica del dispositivo. El uso de esta metodología puede ayudar a reducir las iteraciones manuales y a mejorar los resultados obtenidos en estas últimas etapas del proyecto. ABSTRACT The energy transported by ocean waves (wave energy) is framed within the so-called oceanic energies. Its use to generate electric energy (or desalinate ocean water, etc.) is an idea expressed first time in a patent two centuries ago (1799). Ever since, but specially since the 1970’s, this energy has become interesting for R&D institutions and companies related with the technological and energetic sectors mainly because of the magnitude of available energy. Nowadays the development of this technology can be considered to be in a pre-commercial stage, with a wide range of devices and technologies developed to different degrees but with none standing out nor economically viable. Nor do these technologies seem ready to converge to a single device (or a reduce number of devices). The energy resource to be exploited shares its non-controllability with other renewable energy sources such as wind and solar. However, wave energy presents an additional short-term variability due to its oscillatory nature. Thus, different locations may show waves with similar energy content but different characteristics such as wave height or wave period. This variability in ocean waves makes it very important that the devices for harnessing wave energy (WEC: Wave Energy Converter) fit closely to the characteristics of their location in order to improve their economic viability. It seems reasonable to assume that, in the future, the process of designing a wave power plant will involve a re-design (based on a well-known technology) for each implementation project in any new location. The objective of this PhD thesis is to propose a dimensioning method for a specific wave-energy-harnessing technology: point absorbers. This design methodology is presented as a mathematical optimization problem solved by using an optimization bio-inspired algorithm: differential evolution. This approach allows automating the preliminary dimensioning stage by implementing the methodology in programmed code. The design process of a WEC is a complex engineering problem, so the complete design is not feasible using a single mathematical optimization procedure. Instead, the design process is proposed in different stages, so the methodology developed in this thesis is used for the basic dimensions of a reference solution of the WEC, which would be used as a starting point for the later stages of the design process. The preliminary dimensioning methodology presented in this thesis starts from some previously defined boundary conditions such as: location, power take-off (PTO) characteristic, strategy of energy extraction and specific WEC technology. Using a differential multi-objective evolutionary algorithm produces a set of feasible solutions (according to certain technical and dimensional constraints) and optimal solutions (according to a set of pseudo-cost and pseudo-benefit objective functions). This set of solutions or WEC dimensions are used as a reference case in subsequent stages of design. In the document of this thesis, two versions of this methodology with two different models of evaluation of candidate solutions are presented. On the one hand, a model in the frequency domain that has significant simplifications in the treatment of the wave resource is presented. This method implies a lower computational load but increased uncertainty in the results, which may lead to additional work in the later stages of the design process. However, use of this methodology is useful in order to perform previous parametric analysis of boundary conditions such as the selected location. On the other hand, the second method uses stochastic models, increasing the computational load, but providing results with smaller uncertainty and very useful statistical information for the design process. Therefore, this method is more suitable to be used in a detail design process for full dimensioning of the WEC. The methodology developed throughout the thesis has been used in an industrial project for preliminary energetic assessment of a wave energy power plant. In this assessment process, the method of previous dimensioning was used in the first stage, in order to obtain a set of feasible solutions according to a set of basic technical constraints. The geometry of the WEC was refined and selected subsequently (by other project participants) using a detailed model in the time domain and a model of economic evaluation of the device. Using this methodology can help to reduce the number of design iterations and to improve the results obtained in the last stages of the project.

Distribuição de derivados de petróleo por redes de polidutos: uma abordagem através de algoritmos evolucionários híbridos para um problema triobjetivo

An important problem faced by the oil industry is to distribute multiple oil products through pipelines. Distribution is done in a network composed of refineries (source nodes), storage parks (intermediate nodes), and terminals (demand nodes) interconnected by a set of pipelines transporting oil and derivatives between adjacent areas. Constraints related to storage limits, delivery time, sources availability, sending and receiving limits, among others, must be satisfied. Some researchers deal with this problem under a discrete viewpoint in which the flow in the network is seen as batches sending. Usually, there is no separation device between batches of different products and the losses due to interfaces may be significant. Minimizing delivery time is a typical objective adopted by engineers when scheduling products sending in pipeline networks. However, costs incurred due to losses in interfaces cannot be disregarded. The cost also depends on pumping expenses, which are mostly due to the electricity cost. Since industrial electricity tariff varies over the day, pumping at different time periods have different cost. This work presents an experimental investigation of computational methods designed to deal with the problem of distributing oil derivatives in networks considering three minimization objectives simultaneously: delivery time, losses due to interfaces and electricity cost. The problem is NP-hard and is addressed with hybrid evolutionary algorithms. Hybridizations are mainly focused on Transgenetic Algorithms and classical multi-objective evolutionary algorithm architectures such as MOEA/D, NSGA2 and SPEA2. Three architectures named MOTA/D, NSTA and SPETA are applied to the problem. An experimental study compares the algorithms on thirty test cases. To analyse the results obtained with the algorithms Pareto-compliant quality indicators are used and the significance of the results evaluated with non-parametric statistical tests.

Uma metaheurística para a programação de projectos com multi-modos e recursos limitados

Este artigo apresenta uma nova abordagem (MM-GAV-FBI), aplicável ao problema da programação de projectos com restrições de recursos e vários modos de execução por actividade, problema conhecido na literatura anglo-saxónica por MRCPSP. Cada projecto tem um conjunto de actividades com precedências tecnológicas definidas e um conjunto de recursos limitados, sendo que cada actividade pode ter mais do que um modo de realização. A programação dos projectos é realizada com recurso a um esquema de geração de planos (do inglês Schedule Generation Scheme - SGS) integrado com uma metaheurística. A metaheurística é baseada no paradigma dos algoritmos genéticos. As prioridades das actividades são obtidas a partir de um algoritmo genético. A representação cromossómica utilizada baseia-se em chaves aleatórias. O SGS gera planos não-atrasados. Após a obtenção de uma solução é aplicada uma melhoria local. O objectivo da abordagem é encontrar o melhor plano (planning), ou seja, o plano que tenha a menor duração temporal possível, satisfazendo as precedências das actividades e as restrições de recursos. A abordagem proposta é testada num conjunto de problemas retirados da literatura da especialidade e os resultados computacionais são comparados com outras abordagens. Os resultados computacionais validam o bom desempenho da abordagem, não apenas em termos de qualidade da solução, mas também em termos de tempo útil.

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems

A hybridised and Knowledge-based Evolutionary Algorithm (KEA) is applied to the multi-criterion minimum spanning tree problems. Hybridisation is used across its three phases. In the first phase a deterministic single objective optimization algorithm finds the extreme points of the Pareto front. In the second phase a K-best approach finds the first neighbours of the extreme points, which serve as an elitist parent population to an evolutionary algorithm in the third phase. A knowledge-based mutation operator is applied in each generation to reproduce individuals that are at least as good as the unique parent. The advantages of KEA over previous algorithms include its speed (making it applicable to large real-world problems), its scalability to more than two criteria, and its ability to find both the supported and unsupported optimal solutions.

A robust controller design method for feedback substitution schemes using genetic algorithms

Controllers for feedback substitution schemes demonstrate a trade-off between noise power gain and normalized response time. Using as an example the design of a controller for a radiometric transduction process subjected to arbitrary noise power gain and robustness constraints, a Pareto-front of optimal controller solutions fulfilling a range of time-domain design objectives can be derived. In this work, we consider designs using a loop shaping design procedure (LSDP). The approach uses linear matrix inequalities to specify a range of objectives and a genetic algorithm (GA) to perform a multi-objective optimization for the controller weights (MOGA). A clonal selection algorithm is used to further provide a directed search of the GA towards the Pareto front. We demonstrate that with the proposed methodology, it is possible to design higher order controllers with superior performance in terms of response time, noise power gain and robustness.

Probabilistic approaches in civil engineering: generation of random fields and structural identification with genetic algorithms

The inherent stochastic character of most of the physical quantities involved in engineering models has led to an always increasing interest for probabilistic analysis. Many approaches to stochastic analysis have been proposed. However, it is widely acknowledged that the only universal method available to solve accurately any kind of stochastic mechanics problem is Monte Carlo Simulation. One of the key parts in the implementation of this technique is the accurate and efficient generation of samples of the random processes and fields involved in the problem at hand. In the present thesis an original method for the simulation of homogeneous, multi-dimensional, multi-variate, non-Gaussian random fields is proposed. The algorithm has proved to be very accurate in matching both the target spectrum and the marginal probability. The computational efficiency and robustness are very good too, even when dealing with strongly non-Gaussian distributions. What is more, the resulting samples posses all the relevant, welldefined and desired properties of “translation fields”, including crossing rates and distributions of extremes. The topic of the second part of the thesis lies in the field of non-destructive parametric structural identification. Its objective is to evaluate the mechanical characteristics of constituent bars in existing truss structures, using static loads and strain measurements. In the cases of missing data and of damages that interest only a small portion of the bar, Genetic Algorithm have proved to be an effective tool to solve the problem.

Optimal design of a regenerative dynamic dynamometer using genetic algorithms

This paper presents an approach for optimal design of a fully regenerative dynamic dynamometer using genetic algorithms. The proposed dynamometer system includes an energy storage mechanism to adaptively absorb the energy variations following the dynamometer transients. This allows the minimum power electronics requirement at the mains power supply grid to compensate for the losses. The overall dynamometer system is a dynamic complex system and design of the system is a multi-objective problem, which requires advanced optimisation techniques such as genetic algorithms. The case study of designing and simulation of the dynamometer system indicates that the genetic algorithm based approach is able to locate a best available solution in view of system performance and computational costs.

Performance of the Equivalent Reservoir Modelling Technique for Multi-Reservoir Hydropower Systems

This paper investigates the validity of a simplified equivalent reservoir representation of a multi-reservoir hydroelectric system for modelling its optimal operation for power maximization. This simplification, proposed by Arvanitidis and Rosing (IEEE Trans Power Appar Syst 89(2):319-325, 1970), imputes a potential energy equivalent reservoir with energy inflows and outflows. The hydroelectric system is also modelled for power maximization considering individual reservoir characteristics without simplifications. Both optimization models employed MINOS package for solution of the non-linear programming problems. A comparison between total optimized power generation over the planning horizon by the two methods shows that the equivalent reservoir is capable of producing satisfactory power estimates with less than 6% underestimation. The generation and total reservoir storage trajectories along the planning horizon obtained by equivalent reservoir method, however, presented significant discrepancies as compared to those found in the detailed modelling. This study is motivated by the fact that Brazilian generation system operations are based on the equivalent reservoir method as part of the power dispatch procedures. The potential energy equivalent reservoir is an alternative which eliminates problems with the dimensionality of state variables in a dynamic programming model.

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer Engineering

A multi-criteria decision support system for a routing problem in waste collection

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Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Non-Linear Unsteady Aerodynamic Response Approximation Using Multi-Layer Functionals

Non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. A recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. For a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (FIR) neural networks. Identification of an appropriate FIR neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. The present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. The training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. Application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. The approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of Mach numbers in the transonic regime.