Modelação em regime elástico-linear de pavimentos rodoviários

Neste trabalho apresenta-se o desenvolvimento de um programa de elementos finitos tridimensionais denominado AE3D1.0, concebido especificamente para a análise de pavimentos rodoviários, partindo do pressuposto de que todos os materiais incorporados possuem comportamento elástico-linear. Por comparação dos resultados do programa AE3D1.0 com as soluções analíticas da teoria da elasticidade para o semi-espaço homogéneo e multiestratificado, confirma-se que é possível estabelecer uma analogia próxima entre ambas as abordagens. Tirando partindo das potencialidades do método dos elementos finitos, e da capacidade do programa de registar os resultados de cálculo em ficheiros digitais que possibilitam a posterior apreciação visual e tratamento dos dados obtidos, comparam-se pavimentos rígidos expostos a carregamentos de canto e de bordo, e é evidenciado o efeito prejudicial que a erosão da estrutura de apoio subjacente à laje de betão tem na longevidade e integridade estrutural do pavimento. São também aplicadas forças de frenagem a pavimentos rígidos em secções confinadas e não confinadas. Elege-se um modelo de pneu para veículos pesados representativo das características do eixo padrão de 130 kN, e analisa-se o efeito que a correspondente impressão ovalizada e distribuição de pressões verticais não uniforme tem na estrutura de um pavimento semi-rígido. Adapta-se e é aplicada uma malha de elementos finitos ao estudo da avaliação da capacidade de carga de pavimentos através de ensaios com o defletómetro de impacto.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Unified min-max and interlacing theorems for linear operators

There exist striking analogies in the behaviour of eigenvalues of Hermitian compact operators, singular values of compact operators and invariant factors of homomorphisms of modules over principal ideal domains, namely diagonalization theorems, interlacing inequalities and Courant-Fischer type formulae. Carlson and Sa [D. Carlson and E.M. Sa, Generalized minimax and interlacing inequalities, Linear Multilinear Algebra 15 (1984) pp. 77-103.] introduced an abstract structure, the s-space, where they proved unified versions of these theorems in the finite-dimensional case. We show that this unification can be done using modular lattices with Goldie dimension, which have a natural structure of s-space in the finite-dimensional case, and extend the unification to the countable-dimensional case.

Are severe occlusal problems more frequent in permanent than deciduous dentition?

OBJECTIVE: To determine the prevalence and severity of occlusal problems in populations at the ages of deciduous and permanent dentition and to carry out a meta-analysis to estimate the weighted odds ratio for occlusal problems comparing both groups. METHODS: Data of a probabilistic sample (n=985) of schoolchildren aged 5 and 12 from an epidemiological study in the municipality of São Paulo, Brazil, were analyzed using univariate logistic regression (MLR). Results of cross-sectional study data published in the last 70 years were examined in the meta-analysis. RESULTS: The prevalence of occlusal problems increased from 49.0% (95% CI =47.4%-50.6%) in the deciduous dentition to 71.3% (95% CI =70.3%-72.3%) in the permanent dentition (p<0.001). Dentition was the only variable significantly associated to the severity of malocclusion (OR=1.87; 95% CI =1.43-2.45; p<0.001). The variables sex, type of school and ethnic group were not significant. The meta-analysis showed that a weighted OR of 1.95 (1.91; 1.98) when compared the second dentition period with deciduous and mixed dentition. CONCLUSIONS: In planning oral health services, some activities are indicated to reduce the proportion of moderate/severe malocclusion to levels that are socially more acceptable and economically sustainable.

An efficient method for optimal location and sizing of fixed and switched shunt capacitors in large distribution systems

This paper proposes a computationally efficient methodology for the optimal location and sizing of static and switched shunt capacitors in large distribution systems. The problem is formulated as the maximization of the savings produced by the reduction in energy losses and the avoided costs due to investment deferral in the expansion of the network. The proposed method selects the nodes to be compensated, as well as the optimal capacitor ratings and their operational characteristics, i.e. fixed or switched. After an appropriate linearization, the optimization problem was formulated as a large-scale mixed-integer linear problem, suitable for being solved by means of a widespread commercial package. Results of the proposed optimizing method are compared with another recent methodology reported in the literature using two test cases: a 15-bus and a 33-bus distribution network. For the both cases tested, the proposed methodology delivers better solutions indicated by higher loss savings, which are achieved with lower amounts of capacitive compensation. The proposed method has also been applied for compensating to an actual large distribution network served by AES-Venezuela in the metropolitan area of Caracas. A convergence time of about 4 seconds after 22298 iterations demonstrates the ability of the proposed methodology for efficiently handling large-scale compensation problems.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Optimization techniques for power distribution planning with uncertainties: a comparative study

Important research effort has been devoted to the topic of optimal planning of distribution systems. The non linear nature of the system, the need to consider a large number of scenarios and the increasing necessity to deal with uncertainties make optimal planning in distribution systems a difficult task. Heuristic techniques approaches have been proposed to deal with these issues, overcoming some of the inherent difficulties of classic methodologies. This paper considers several methodologies used to address planning problems of electrical power distribution networks, namely mixedinteger linear programming (MILP), ant colony algorithms (AC), genetic algorithms (GA), tabu search (TS), branch exchange (BE), simulated annealing (SA) and the Bender´s decomposition deterministic non-linear optimization technique (BD). Adequacy of theses techniques to deal with uncertainties is discussed. The behaviour of each optimization technique is compared from the point of view of the obtained solution and of the methodology performance. The paper presents results of the application of these optimization techniques to a real case of a 10-kV electrical distribution system with 201 nodes that feeds an urban area.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problem

The paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.

Comparação de diferentes técnicas de irradiação de mama em radioterapia com recurso a acelerador linear em modo de fotões

RESUMO: Introdução – A Radioterapia (RT) é uma abordagem terapêutica para tratamento de neoplasia de mama. Contudo, diferentes técnicas de irradiação (TI) podem ser usadas. Objetivos – Comparar 4 TI, considerando a irradiação dos volumes alvo (PTV) e dos órgãos de risco (OAR). Metodologia – Selecionaram-se 7 pacientes com indicação para RT de mama esquerda. Sobre tomografia computorizada foram feitos os contornos do PTV e dos OAR. Foram calculadas 4 planimetrias/paciente para as TI: conformacional externa (EBRT), intensidade modulada com 2 (IMRT2) e 5 campos (IMRT5) e arco dinâmico (DART). Resultados – Histogramas de dose volume foram comparados para todas as TI usando o software de análise estatística, IBM SPSS v20. Com IMRT5 e DART, os OAR recebem mais doses baixas. No entanto, IMRT5 apresenta melhores índices de conformidade e homogeneidade para o PTV. Conclusões – IMRT5 apresenta o melhor índice de conformidade; EBRT e IMRT2 apresentam melhores resultados que DART. Há d.e.s entre as TI, sobretudo em doses mais baixas nos OAR.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Derivative-free optimization and filter methods to solve nonlinear constrained problems

In real optimization problems, usually the analytical expression of the objective function is not known, nor its derivatives, or they are complex. In these cases it becomes essential to use optimization methods where the calculation of the derivatives, or the verification of their existence, is not necessary: the Direct Search Methods or Derivative-free Methods are one solution. When the problem has constraints, penalty functions are often used. Unfortunately the choice of the penalty parameters is, frequently, very difficult, because most strategies for choosing it are heuristics strategies. As an alternative to penalty function appeared the filter methods. A filter algorithm introduces a function that aggregates the constrained violations and constructs a biobjective problem. In this problem the step is accepted if it either reduces the objective function or the constrained violation. This implies that the filter methods are less parameter dependent than a penalty function. In this work, we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of the simplex method and filter methods. This method does not compute or approximate any derivatives, penalty constants or Lagrange multipliers. The basic idea of simplex filter algorithm is to construct an initial simplex and use the simplex to drive the search. We illustrate the behavior of our algorithm through some examples. The proposed methods were implemented in Java.

Numerical optimization experiments using the hyperbolic smoothing strategy to solve MPCC

In this work we solve Mathematical Programs with Complementarity Constraints using the hyperbolic smoothing strategy. Under this approach, the complementarity condition is relaxed through the use of the hyperbolic smoothing function, involving a positive parameter that can be decreased to zero. An iterative algorithm is implemented in MATLAB language and a set of AMPL problems from MacMPEC database were tested.