Design of a laminated composite multi-cel structure subjected to torsion

Laminate composite multi-cell structures have to support both axial and shear stresses when sustaining variable twist. Thus the properties and design of the laminate may not be the most adequate at all cross-sections to support the torsion imposed on the cells. In this work, the effect of some material and geometric parameters on the optimal mechanical behaviour of a multi-cell composite laminate structure is studied when torsion is present. A particle swarm optimization technique is used to maximize the multi-cell structure torsion constant that can be used to obtain the angle of twist of the composite laminate profile.

Optimal location of the workpiece in a PKM-based machining robotic cell

Most machining tasks require high accuracy and are carried out by dedicated machine-tools. On the other hand, traditional robots are flexible and easy to program, but they are rather inaccurate for certain tasks. Parallel kinematic robots could combine the accuracy and flexibility that are usually needed in machining operations. Achieving this goal requires proper design of the parallel robot. In this chapter, a multi-objective particle swarm optimization algorithm is used to optimize the structure of a parallel robot according to specific criteria. Afterwards, for a chosen optimal structure, the best location of the workpiece with respect to the robot, in a machining robotic cell, is analyzed based on the power consumed by the manipulator during the machining process.

A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Mu-Chlorido-Bridged Dimanganese(II) complexes of the schiff base derived from [2+2] condensation of 2,6-diformyl-4-methylphenol and 1,3-bis(3-aminopropyl)tetramethyldisloxane: structure, magnetismo, electrochemical behaviour, and catalytic oxidation of secondary alcohols

The reaction of 2,6-diformyl-4-methylphenol with 1,3-bis(3-aminopropyl)tetramethyldisiloxane in the presence of MnCl2 in a 1:1:2 molar ratio in methanol afforded a dinuclear -chlorido-bridged manganese(II) complex of the macrocyclic [2+2] condensation product (H2L), namely, [Mn2Cl2(H2L)(HL)]Cl center dot 3H(2)O (1). The latter afforded a new compound, namely, [Mn2Cl2(H2L)(2)][MnCl4]center dot 4CH(3)CN center dot 0.5CHCl(3 center dot)0.4H(2)O (2), after recrystallisation from 1:1 CHCl3/CH3CN. The co-existence of the free and complexed azomethine groups, phenolato donors, mu-chlorido bridges, and the disiloxane unit were well evidenced by ESI mass spectrometry and FTIR spectroscopy and confirmed by X-ray crystallography. The magnetic measurements revealed an antiferromagnetic interaction between the two high-spin (S = 5/2, g = 2) manganese(II) ions through the mu-chlorido bridging ligands. The electrochemical behaviour of 1 and 2 has been studied, and details of their redox properties are reported. Both compounds act as catalysts or catalyst precursors in the solvent-free low-power microwave-assisted oxidation of selected secondary alcohols, for example, 1-phenylethanol, cyclohexanol, 2- and 3-octanol, to the corresponding ketones in the absence of solvent. The highest yield of 72% was achieved for 1-phenylethanol by using a maximum of 1% molar ratio of catalyst relative to substrate.

Project scheduling under multiple resources constraints using a genetic algorithm

The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Project scheduling using a competitive genetic algorithm

- The resource constrained project scheduling problem (RCPSP) is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions. This paper proposes a genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities and delay times of the activities are defined by the genetic algorithm. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm

µ-Chlorido-bridged dimanganese(II) complexes of the schiff base derived from [2+2] condensation of 2,6-diformyl-4-methylphenol and 1,3-bis(3-aminopropyl)tetramethyldisiloxane: structure, magnetismo, electrochemical behaviour, and catalytic oxidation of secondary alcohols

The reaction of 2,6-diformyl-4-methylphenol with 1,3-bis(3-aminopropyl)tetramethyldisiloxane in the presence of MnCl2 in a 1:1:2 molar ratio in methanol afforded a dinuclear -chlorido-bridged manganese(II) complex of the macrocyclic [2+2] condensation product (H2L), namely, [Mn2Cl2(H2L)(HL)]Cl center dot 3H(2)O (1). The latter afforded a new compound, namely, [Mn2Cl2(H2L)(2)][MnCl4]center dot 4CH(3)CN center dot 0.5CHCl(3 center dot)0.4H(2)O (2), after recrystallisation from 1:1 CHCl3/CH3CN. The co-existence of the free and complexed azomethine groups, phenolato donors, mu-chlorido bridges, and the disiloxane unit were well evidenced by ESI mass spectrometry and FTIR spectroscopy and confirmed by X-ray crystallography. The magnetic measurements revealed an antiferromagnetic interaction between the two high-spin (S = 5/2, g = 2) manganese(II) ions through the mu-chlorido bridging ligands. The electrochemical behaviour of 1 and 2 has been studied, and details of their redox properties are reported. Both compounds act as catalysts or catalyst precursors in the solvent-free low-power microwave-assisted oxidation of selected secondary alcohols, for example, 1-phenylethanol, cyclohexanol, 2- and 3-octanol, to the corresponding ketones in the absence of solvent. The highest yield of 72% was achieved for 1-phenylethanol by using a maximum of 1% molar ratio of catalyst relative to substrate.

Internal structure of the nanogratings generated inside bulk fused sílica by ultrafast laser direct writing

The aim of the present work was to characterize the internal structure of nanogratings generated inside bulk fused silica by ultrafast laser processing and to study the influence of diluted hydrofluoric acid etching on their structure. The nanogratings were inscribed at a depth of 100 mu m within fused silica wafers by a direct writing method, using 1030 nm radiation wavelength and the following processing parameters: E = 5 mu J, tau = 560 fs, f = 10 kHz, and v = 100 mu m/s. The results achieved show that the laser-affected regions are elongated ellipsoids with a typical major diameter of about 30 mu m and a minor diameter of about 6 mu m. The nanogratings within these regions are composed of alternating nanoplanes of damaged and undamaged material, with an average periodicity of 351 +/- 21 nm. The damaged nanoplanes contain nanopores randomly dispersed in a material containing a large density of defects. These nanopores present a roughly bimodal size distribution with average dimensions for each class of pores 65 +/- 20 x 16 +/- 8 x 69 +/- 16 nm(3) and 367 +/- 239 x 16 +/- 8 x 360 +/- 194 nm(3), respectively. The number and size of the nanopores increases drastically when an hydrofluoric acid treatment is performed, leading to the coalescence of these voids into large planar discontinuities parallel to the nanoplanes. The preferential etching of the damaged material by the hydrofluoric acid solution, which is responsible for the pores growth and coalescence, confirms its high defect density. (C) 2014 AIP Publishing LLC.

Molecular mobility, composition and structure analysis in glycerol plasticised chitosan films

This study was developed with the purpose to investigate the effect of polysaccharide/plasticiser concentration on the microstructure and molecular dynamics of polymeric film systems, using transmission electron microscope imaging (TEM) and nuclear magnetic resonance (NMR) techniques. Experiments were carried out in chitosan/glycerol films prepared with solutions of different composition. The films obtained after drying and equilibration were characterised in terms of composition, thickness and water activity. Results show that glycerol quantities used in film forming solutions were responsible for films composition; while polymer/total plasticiser ratio in the solution determined the thickness (and thus structure) of the films. These results were confirmed by TEM. NMR allowed understanding the films molecular rearrangement. Two different behaviours for the two components analysed, water and glycerol were observed: the first is predominantly moving free in the matrix, while glycerol is mainly bounded to the chitosan chain. (C) 2013 Elsevier Ltd. All rights reserved.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

A genetic algorithm for the job shop scheduling with a new local search using Monte Carlo method

This paper presents a methodology for applying scheduling algorithms using Monte Carlo simulation. The methodology is based on a decision support system (DSS). The proposed methodology combines a genetic algorithm with a new local search using Monte Carlo Method. The methodology is applied to the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The methodology is tested on a set of standard instances taken from the literature and compared with others. The computation results validate the effectiveness of the proposed methodology. The DSS developed can be utilized in a common industrial or construction environment.

Algebraic structure for the crossing of balanced and stair nested designs

Stair nesting allows us to work with fewer observations than the most usual form of nesting, the balanced nesting. In the case of stair nesting the amount of information for the different factors is more evenly distributed. This new design leads to greater economy, because we can work with fewer observations. In this work we present the algebraic structure of the cross of balanced nested and stair nested designs, using binary operations on commutative Jordan algebras. This new cross requires fewer observations than the usual cross balanced nested designs and it is easy to carry out inference.

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

Multi-region routing

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores