Describing fatigue crack growth and load ratio effects in Al 2524 T3 alloy with an enhanced exponential model

The fatigue crack behavior in metals and alloys under constant amplitude test conditions is usually described by relationships between the crack growth rate da/dN and the stress intensity factor range Delta K. In the present work, an enhanced two-parameter exponential equation of fatigue crack growth was introduced in order to describe sub-critical crack propagation behavior of Al 2524-T3 alloy, commonly used in aircraft engineering applications. It was demonstrated that besides adequately correlating the load ratio effects, the exponential model also accounts for the slight deviations from linearity shown by the experimental curves. A comparison with Elber, Kujawski and "Unified Approach" models allowed for verifying the better performance, when confronted to the other tested models, presented by the exponential model. (C) 2012 Elsevier Ltd. All rights reserved.

Load compensation in four-wire electrical power systems

This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and another in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the methodology was adapted to accept different power factors for the system to be compensated. on the other hand, the determination of the compensation susceptances is based on the instantaneous values of the load currents. The results are obtained using the MatLab - Simulink environment.

Electrical load forecasting formulation by a fast neural network

The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time. (C) 2011 Elsevier Ltd. All rights reserved.

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.

Modified stabilization method for the tibial tuberosity advancement technique: a biomechanical study

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mechanical resistance of the modified stabilization method for the tibial tuberosity advancement technique: ex vivo experimental study in dogs

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

THE EFFECT of E-GLASS FIBERS and ACRYLIC RESIN THICKNESS on FRACTURE LOAD IN A SIMULATED IMPLANT-SUPPORTED OVERDENTURE PROSTHESIS

Statement of problem. Implant overdenture prostheses are prone to acrylic resin fracture because of space limitations around the implant overdenture components.Purpose. The purpose of this study was to evaluate the influence of E-glass fibers and acrylic resin thickness in resisting acrylic resin fracture around a simulated overdenture abutment.Material and methods. A model was developed to simulate the clinical situation of an implant overdenture abutment with varying acrylic resin thickness (1.5 or 3.0 mm) with or without E-glass fiber reinforcement. Forty-eight specimens with an underlying simulated abutment were divided into 4 groups (n=12): 1.5 mm acrylic resin without E-glass fibers identified as thin with no E-glass fiber mesh (TN-N); 1.5 mm acrylic resin with E-glass fibers identified as thin with E-glass fiber mesh (TN-F); 3.0 mm acrylic resin without E-glass fibers identified as thick without E-glass fiber mesh (TK-N); and 3.0 mm acrylic resin with E-glass fibers identified as thick with E-glass fiber mesh (TK-F). All specimens were submitted to a 3-point bending test and fracture loads (N) were analyzed with a 2-way ANOVA and Tukey's post hoc test (alpha=.05).Results. The results revealed significant differences in fracture load among the 4 groups, with significant effects from both thickness (P<.001) and inclusion of the mesh (P<.001). Results demonstrated no interaction between mesh and thickness (P=.690). The TN-N: 39 +/- 5 N; TN-F: 50 +/- 6.9 N; TK-N: 162 +/- 13 N; and TK-F: 193 +/- 21 N groups were all statistically different (P<.001).Conclusions. The fracture load of a processed, acrylic resin implant-supported overdenture can be significantly increased by the addition of E-glass fibers even when using thin acrylic resin sections. on a relative basis, the increase in fracture load was similar when adding E-glass fibers or increasing acrylic resin thickness. (J Prosthet Dent 2011;106:373-377)

Influence of root-surface conditioning with acid and chelating agents on clot stabilization

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)