A compact superconducting magnet for magnetic resonance microscopy

Magnetic resonance microscopy (MRM) depends on the use of high field, superconducting magnet systems for its operation. The magnets that are conventionally used are those that were initially designed for chemical structural analysis work. A novel, compact magnet designed specifically for MRM is presented here, and while preserving high field, high homogeneity conditions, has a length less than one-third that of conventional systems. This enables much better access to samples, an important consideration in many MRM experiments. As the homogeneity of a magnet is strongly dependent on its length, novel geometries and optimization techniques are required to meet the requirements of MRM in a compact system. An important outcome of the stochastic optimization performed in this work, is that the use used of a thin superconducting solenoid surrounded by counterwound disk windings provides a mechanism for drastic length reductions over conventional magnet designs. (C) 1998 American Institute of Physics.

Rapid computation of static fields produced by thick circular solenoids

A straightforward method is proposed for computing the magnetic field produced by a circular coil that contains a large number of turns wound onto a solenoid of rectangular cross section. The coil is thus approximated by a circular ring containing a continuous constant current density, which is very close to the real situation when sire of rectangular cross section is used. All that is required is to evaluate two functions, which are defined as integrals of periodic quantities; this is done accurately and efficiently using trapezoidal-rule quadrature. The solution can be obtained so rapidly that this procedure is ideally suited for use in stochastic optimization, An example is given, in which this approach is combined with a simulated annealing routine to optimize shielded profile coils for NMR.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Análise de desempenho de algoritmos de otimização para extração de parâmetros em modelos semicondutores de potência

Apresenta-se nesta tese uma revisão da literatura sobre a modelação de semicondutores de potência baseada na física e posterior análise de desempenho de dois métodos estocásticos, Particle Swarm Optimizaton (PSO) e Simulated Annealing (SA), quando utilizado para identificação eficiente de parâmetros de modelos de dispositivos semicondutores de potência, baseado na física. O conhecimento dos valores destes parâmetros, para cada dispositivo, é fundamental para uma simulação precisa do comportamento dinâmico do semicondutor. Os parâmetros são extraídos passo-a-passo durante simulação transiente e desempenham um papel relevante. Uma outra abordagem interessante nesta tese relaciona-se com o facto de que nos últimos anos, os métodos de modelação para dispositivos de potência têm emergido, com alta precisão e baixo tempo de execução baseado na Equação de Difusão Ambipolar (EDA) para díodos de potência e implementação no MATLAB numa estratégia de optimização formal. A equação da EDA é resolvida numericamente sob várias condições de injeções e o modelo é desenvolvido e implementado como um subcircuito no simulador IsSpice. Larguras de camada de depleção, área total do dispositivo, nível de dopagem, entre outras, são alguns dos parâmetros extraídos do modelo. Extração de parâmetros é uma parte importante de desenvolvimento de modelo. O objectivo de extração de parâmetros e otimização é determinar tais valores de parâmetros de modelo de dispositivo que minimiza as diferenças entre um conjunto de características medidas e resultados obtidos pela simulação de modelo de dispositivo. Este processo de minimização é frequentemente chamado de ajuste de características de modelos para dados de medição. O algoritmo implementado, PSO é uma técnica de heurística de otimização promissora, eficiente e recentemente proposta por Kennedy e Eberhart, baseado no comportamento social. As técnicas propostas são encontradas para serem robustas e capazes de alcançar uma solução que é caracterizada para ser precisa e global. Comparada com algoritmo SA já realizada, o desempenho da técnica proposta tem sido testado utilizando dados experimentais para extrair parâmetros de dispositivos reais das características I-V medidas. Para validar o modelo, comparação entre resultados de modelo desenvolvido com um outro modelo já desenvolvido são apresentados.

Development of a methodology to incorporate risk and uncertainty in electricity power planning

Dissertação de mestrado em Engenharia Industrial

Otimização das características de um chassis tubular de automóvel de competição com tração a duas rodas motrizes

Dissertação de mestrado integrado em Engenharia Mecânica

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Optimisation des horaires des agents et du routage des appels dans les centres d’appels

Nous étudions la gestion de centres d'appels multi-compétences, ayant plusieurs types d'appels et groupes d'agents. Un centre d'appels est un système de files d'attente très complexe, où il faut généralement utiliser un simulateur pour évaluer ses performances. Tout d'abord, nous développons un simulateur de centres d'appels basé sur la simulation d'une chaîne de Markov en temps continu (CMTC), qui est plus rapide que la simulation conventionnelle par événements discrets. À l'aide d'une méthode d'uniformisation de la CMTC, le simulateur simule la chaîne de Markov en temps discret imbriquée de la CMTC. Nous proposons des stratégies pour utiliser efficacement ce simulateur dans l'optimisation de l'affectation des agents. En particulier, nous étudions l'utilisation des variables aléatoires communes. Deuxièmement, nous optimisons les horaires des agents sur plusieurs périodes en proposant un algorithme basé sur des coupes de sous-gradients et la simulation. Ce problème est généralement trop grand pour être optimisé par la programmation en nombres entiers. Alors, nous relaxons l'intégralité des variables et nous proposons des méthodes pour arrondir les solutions. Nous présentons une recherche locale pour améliorer la solution finale. Ensuite, nous étudions l'optimisation du routage des appels aux agents. Nous proposons une nouvelle politique de routage basé sur des poids, les temps d'attente des appels, et les temps d'inoccupation des agents ou le nombre d'agents libres. Nous développons un algorithme génétique modifié pour optimiser les paramètres de routage. Au lieu d'effectuer des mutations ou des croisements, cet algorithme optimise les paramètres des lois de probabilité qui génèrent la population de solutions. Par la suite, nous développons un algorithme d'affectation des agents basé sur l'agrégation, la théorie des files d'attente et la probabilité de délai. Cet algorithme heuristique est rapide, car il n'emploie pas la simulation. La contrainte sur le niveau de service est convertie en une contrainte sur la probabilité de délai. Par après, nous proposons une variante d'un modèle de CMTC basé sur le temps d'attente du client à la tête de la file. Et finalement, nous présentons une extension d'un algorithme de coupe pour l'optimisation stochastique avec recours de l'affectation des agents dans un centre d'appels multi-compétences.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.

A common Tabu search algorithm for the global optimization of engineering problems

A novel common Tabu algorithm for global optimizations of engineering problems is presented. The robustness and efficiency of the presented method are evaluated by using standard mathematical functions and hy solving a practical engineering problem. The numerical results show that the proposed method is (i) superior to the conventional Tabu search algorithm in robustness, and (ii) superior to the simulated annealing algorithm in efficiency. (C) 2001 Elsevier B.V. B.V. All rights reserved.

Simulation studies of correlation functions and relaxation in polymeric systems

We investigate the statics and dynamics of a glassy,non-entangled, short bead-spring polymer melt with moleculardynamics simulations. Temperature ranges from slightlyabove the mode-coupling critical temperature to the liquidregime where features of a glassy liquid are absent. Ouraim is to work out the polymer specific effects on therelaxation and particle correlation. We find the intra-chain static structure unaffected bytemperature, it depends only on the distance of monomersalong the backbone. In contrast, the distinct inter-chainstructure shows pronounced site-dependence effects at thelength-scales of the chain and the nearest neighbordistance. There, we also find the strongest temperaturedependence which drives the glass transition. Both the siteaveraged coupling of the monomer and center of mass (CM) andthe CM-CM coupling are weak and presumably not responsiblefor a peak in the coherent relaxation time at the chain'slength scale. Chains rather emerge as soft, easilyinterpenetrating objects. Three particle correlations arewell reproduced by the convolution approximation with theexception of model dependent deviations. In the spatially heterogeneous dynamics of our system weidentify highly mobile monomers which tend to follow eachother in one-dimensional paths forming ``strings''. Thesestrings have an exponential length distribution and aregenerally short compared to the chain length. Thus, arelaxation mechanism in which neighboring mobile monomersmove along the backbone of the chain seems unlikely.However, the correlation of bonded neighbors is enhanced. When liquids are confined between two surfaces in relativesliding motion kinetic friction is observed. We study ageneric model setup by molecular dynamics simulations for awide range of sliding speeds, temperatures, loads, andlubricant coverings for simple and molecular fluids. Instabilities in the particle trajectories are identified asthe origin of kinetic friction. They lead to high particlevelocities of fluid atoms which are gradually dissipatedresulting in a friction force. In commensurate systemsfluid atoms follow continuous trajectories for sub-monolayercoverings and consequently, friction vanishes at low slidingspeeds. For incommensurate systems the velocity probabilitydistribution exhibits approximately exponential tails. Weconnect this velocity distribution to the kinetic frictionforce which reaches a constant value at low sliding speeds. This approach agrees well with the friction obtaineddirectly from simulations and explains Amontons' law on themicroscopic level. Molecular bonds in commensurate systemslead to incommensurate behavior, but do not change thequalitative behavior of incommensurate systems. However,crossed chains form stable load bearing asperities whichstrongly increase friction.

Validation of the CFD code NEPTUNE for a full scale simulator for decay heat removal systems with in-pool heat exchangers

In the present work, a multi physics simulation of an innovative safety system for light water nuclear reactor is performed, with the aim to increase the reliability of its main decay heat removal system. The system studied, denoted by the acronym PERSEO (in Pool Energy Removal System for Emergency Operation) is able to remove the decay power from the primary side of the light water nuclear reactor through a heat suppression pool. The experimental facility, located at SIET laboratories (PIACENZA), is an evolution of the Thermal Valve concept where the triggering valve is installed liquid side, on a line connecting two pools at the bottom. During the normal operation, the valve is closed, while in emergency conditions it opens, the heat exchanger is flooded with consequent heat transfer from the primary side to the pool side. In order to verify the correct system behavior during long term accidental transient, two main experimental PERSEO tests are analyzed. For this purpose, a coupling between the mono dimensional system code CATHARE, which reproduces the system scale behavior, with a three-dimensional CFD code NEPTUNE CFD, allowing a full investigation of the pools and the injector, is implemented. The coupling between the two codes is realized through the boundary conditions. In a first analysis, the facility is simulated by the system code CATHARE V2.5 to validate the results with the experimental data. The comparison of the numerical results obtained shows a different void distribution during the boiling conditions inside the heat suppression pool for the two cases of single nodalization and three volume nodalization scheme of the pool. Finaly, to improve the investigation capability of the void distribution inside the pool and the temperature stratification phenomena below the injector, a two and three dimensional CFD models with a simplified geometry of the system are adopted.

Bestimmung der fehlenden transversalen Energie auf der ersten Triggerstufe beim ATLAS-Experiment und Optimierung der Triggerselektion von supersymmetrischen Ereignissen

Das Standardmodell der Teilchenphysik, das drei der vier fundamentalen Wechselwirkungen beschreibt, stimmt bisher sehr gut mit den Messergebnissen der Experimente am CERN, dem Fermilab und anderen Forschungseinrichtungen überein. rnAllerdings können im Rahmen dieses Modells nicht alle Fragen der Teilchenphysik beantwortet werden. So lässt sich z.B. die vierte fundamentale Kraft, die Gravitation, nicht in das Standardmodell einbauen.rnDarüber hinaus hat das Standardmodell auch keinen Kandidaten für dunkle Materie, die nach kosmologischen Messungen etwa 25 % unseres Universum ausmacht.rnAls eine der vielversprechendsten Lösungen für diese offenen Fragen wird die Supersymmetrie angesehen, die eine Symmetrie zwischen Fermionen und Bosonen einführt. rnAus diesem Modell ergeben sich sogenannte supersymmetrische Teilchen, denen jeweils ein Standardmodell-Teilchen als Partner zugeordnet sind.rnEin mögliches Modell dieser Symmetrie ist das R-Paritätserhaltende mSUGRA-Modell, falls Supersymmetrie in der Natur realisiert ist.rnIn diesem Modell ist das leichteste supersymmetrische Teilchen (LSP) neutral und schwach wechselwirkend, sodass es nicht direkt im Detektor nachgewiesen werden kann, sondern indirekt über die vom LSP fortgetragene Energie, die fehlende transversale Energie (etmiss), nachgewiesen werden muss.rnrnDas ATLAS-Experiment wird 2010 mit Hilfe des pp-Beschleunigers LHC mit einer Schwerpunktenergie von sqrt(s)=7-10 TeV mit einer Luminosität von 10^32 #/(cm^2*s) mit der Suche nach neuer Physik starten.rnDurch die sehr hohe Datenrate, resultierend aus den etwa 10^8 Auslesekanälen des ATLAS-Detektors bei einer Bunchcrossingrate von 40 MHz, wird ein Triggersystem benötigt, um die zu speichernde Datenmenge zu reduzieren.rnDabei muss ein Kompromiss zwischen der verfügbaren Triggerrate und einer sehr hohen Triggereffizienz für die interessanten Ereignisse geschlossen werden, da etwa nur jedes 10^8-te Ereignisse für die Suche nach neuer Physik interessant ist.rnZur Erfüllung der Anforderungen an das Triggersystem wird im Experiment ein dreistufiges System verwendet, bei dem auf der ersten Triggerstufe mit Abstand die höchste Datenreduktion stattfindet.rnrnIm Rahmen dieser Arbeit rn%, die vollständig auf Monte-Carlo-Simulationen basiert, rnist zum einen ein wesentlicher Beitrag zum grundlegenden Verständnis der Eigenschaft der fehlenden transversalen Energie auf der ersten Triggerstufe geleistet worden.rnZum anderen werden Methoden vorgestellt, mit denen es möglich ist, die etmiss-Triggereffizienz für Standardmodellprozesse und mögliche mSUGRA-Szenarien aus Daten zu bestimmen. rnBei der Optimierung der etmiss-Triggerschwellen für die erste Triggerstufe ist die Triggerrate bei einer Luminosität von 10^33 #/(cm^2*s) auf 100 Hz festgelegt worden.rnFür die Triggeroptimierung wurden verschiedene Simulationen benötigt, bei denen eigene Entwicklungsarbeit eingeflossen ist.rnMit Hilfe dieser Simulationen und den entwickelten Optimierungsalgorithmen wird gezeigt, dass trotz der niedrigen Triggerrate das Entdeckungspotential (für eine Signalsignifikanz von mindestens 5 sigma) durch Kombinationen der etmiss-Schwelle mit Lepton bzw. Jet-Triggerschwellen gegenüber dem bestehenden ATLAS-Triggermenü auf der ersten Triggerstufe um bis zu 66 % erhöht wird.