On the use of particle swarm optimization to maximize bending stiffness of functionally graded structures

Functionally graded materials are a type of composite materials which are tailored to provide continuously varying properties, according to specific constituent's mixing distributions. These materials are known to provide superior thermal and mechanical performances when compared to the traditional laminated composites, because of this continuous properties variation characteristic, which enables among other advantages, smoother stresses distribution profiles. Therefore the growing trend on the use of these materials brings together the interest and the need for getting optimum configurations concerning to each specific application. In this work it is studied the use of particle swarm optimization technique for the maximization of a functionally graded sandwich beam bending stiffness. For this purpose, a set of case studies is analyzed, in order to enable to understand in a detailed way, how the different optimization parameters tuning can influence the whole process. It is also considered a re-initialization strategy, which is not a common approach in particle swarm optimization as far as it was possible to conclude from the published research works. As it will be shown, this strategy can provide good results and also present some advantages in some conditions. This work was developed and programmed on symbolic computation platform Maple 14. (C) 2013 Elsevier B.V. All rights reserved.

Role of abrasive material on micro-abrasion wear tests

Micro-abrasion wear tests with ball-cratering configuration are widely used. Sources of variability are already studied by different authors and conditions for testing are parameterized by BS EN 1071-6: 2007 standard which refers silicon carbide as abrasive. However, the use of other abrasives is possible and allowed. In this work, ball-cratering wear tests were performed using four different abrasive particles of three dissimilar materials: diamond, alumina and silicon carbide. Tests were carried out under the same conditions on a steel plate provided with TiB2 hard coating. For each abrasive, five different test durations were used allowing understanding the initial wear phenomena. Composition and shape of abrasive particles were investigated by SEM and EDS. Scar areas were observed by optical and electronic microscopy in order to understand the wear effects caused by each of them. Scar geometry and grooves were analyzed and compared. Wear coefficient was calculated for each situation. It was observed that diamond particles produce well-defined and circular wear scars. Different silicon carbide particles presented dissimilar results as consequence of distinct particle shape and size distribution.

Particle swarm optimization with fractional-order velocity

This paper proposes a novel method for controlling the convergence rate of a particle swarm optimization algorithm using fractional calculus (FC) concepts. The optimization is tested for several well-known functions and the relationship between the fractional order velocity and the convergence of the algorithm is observed. The FC demonstrates a potential for interpreting evolution of the algorithm and to control its convergence.

Preservice teachers' knowledge on elementary geometry concepts

This text is based on a research, which is still in progress, whose main objective is to identify and understand what are the main difficulties of future mathematics teachers of basic education are, regarding their content knowledge in geometry in the context of the curricular unit of Geometry during their undergraduate degree. We chose a qualitative approach in the form of case study, in which data collection was done through observation, interviews, a diverse set of tasks, a diagnostic test and other documents. This paper focuses on the test given to prospective teachers at the beginning of the course. The preliminary analysis of the data points to a weak performance of preservice teachers in the test issues addressing elementary knowledge of Geometry.

Particle swarm design of digital circuits

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Particle swarm optimization (PSO) is a form of SI, and a population-based search algorithm that is initialized with a population of random solutions, called particles. These particles are flying through hyperspace and have two essential reasoning capabilities: their memory of their own best position and knowledge of the swarm's best position. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. This work proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. It is also presented a comparison with other two Evolutionary Algorithms, namely Genetic and Memetic Algorithms.

Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Probabilistic tsunami hazard in the Northeast Atlantic from near- and far-field tectonic sources

In this article, we present the first study on probabilistic tsunami hazard assessment for the Northeast (NE) Atlantic region related to earthquake sources. The methodology combines the probabilistic seismic hazard assessment, tsunami numerical modeling, and statistical approaches. We consider three main tsunamigenic areas, namely the Southwest Iberian Margin, the Gloria, and the Caribbean. For each tsunamigenic zone, we derive the annual recurrence rate for each magnitude range, from Mw 8.0 up to Mw 9.0, with a regular interval, using the Bayesian method, which incorporates seismic information from historical and instrumental catalogs. A numerical code, solving the shallow water equations, is employed to simulate the tsunami propagation and compute near shore wave heights. The probability of exceeding a specific tsunami hazard level during a given time period is calculated using the Poisson distribution. The results are presented in terms of the probability of exceedance of a given tsunami amplitude for 100- and 500-year return periods. The hazard level varies along the NE Atlantic coast, being maximum along the northern segment of the Morocco Atlantic coast, the southern Portuguese coast, and the Spanish coast of the Gulf of Cadiz. We find that the probability that a maximum wave height exceeds 1 m somewhere in the NE Atlantic region reaches 60 and 100 % for 100- and 500-year return periods, respectively. These probability values decrease, respectively, to about 15 and 50 % when considering the exceedance threshold of 5 m for the same return periods of 100 and 500 years.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

Reply to: Comments on “Particle Swarm Optimization with Fractional-Order Velocity”

We agree with Ling-Yun et al. [5] and Zhang and Duan comments [2] about the typing error in equation (9) of the manuscript [8]. The correct formula was initially proposed in [6, 7]. The formula adopted in our algorithms discussed in our papers [1, 3, 4, 8] is, in fact, the following: ...

Uma abordagem à incorporação de lenhinosulfonatos e materiais reciclados em misturas de SBR para produção sustentável de solas

Mestrado em Engenharia Química - Ramo Otimização Energética na Indústria Química

Gelatin particle indirect agglutination test for serodiagnosis of schistosomiasis: comparative study with enzyme-linked immunosorbent assay

A new serological test, the gelatin particle agglutination test (GPAT), was used for the serodiagnosis of schistosomiasis mansoni. This technique showed the sensitivity (90.6%) and specificity (97.8%) close to those of enzyme-linked immunosorbent assay. The GPAT can be easily and rapidly performed without specialized equipment, by using lyophilized antigen-coated gelatin particles. The test also seems to be useful for mass screening of Schistosoma infection in field conditions.

A didactic sequence of elementary geometric optics Informed by history and philosophy of science

The concepts and instruments required for the teaching and learning of geometric optics are introduced in the didactic processwithout a proper didactic transposition. This claim is secured by the ample evidence of both wide- and deep-rooted alternative concepts on the topic. Didactic transposition is a theory that comes from a reflection on the teaching and learning process in mathematics but has been used in other disciplinary fields. It will be used in this work in order to clear up the main obstacles in the teachinglearning process of geometric optics. We proceed to argue that since Newton’s approach to optics, in his Book I of Opticks, is independent of the corpuscular or undulatory nature of light, it is the most suitable for a constructivist learning environment. However, Newton’s theory must be subject to a proper didactic transposition to help overcome the referred alternative concepts. Then is described our didactic transposition in order to create knowledge to be taught using a dialogical process between students’ previous knowledge, history of optics and the desired outcomes on geometrical optics in an elementary pre-service teacher training course. Finally, we use the scheme-facet structure of knowledge both to analyse and discuss our results as well as to illuminate shortcomings that must be addressed in our next stage of the inquiry.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

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.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.