993 resultados para Induction heating


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The numerical modelling of electromagnetic waves has been the focus of many research areas in the past. Some specific applications of electromagnetic wave scattering are in the fields of Microwave Heating and Radar Communication Systems. The equations that govern the fundamental behaviour of electromagnetic wave propagation in waveguides and cavities are the Maxwell's equations. In the literature, a number of methods have been employed to solve these equations. Of these methods, the classical Finite-Difference Time-Domain scheme, which uses a staggered time and space discretisation, is the most well known and widely used. However, it is complicated to implement this method on an irregular computational domain using an unstructured mesh. In this work, a coupled method is introduced for the solution of Maxwell's equations. It is proposed that the free-space component of the solution is computed in the time domain, whilst the load is resolved using the frequency dependent electric field Helmholtz equation. This methodology results in a timefrequency domain hybrid scheme. For the Helmholtz equation, boundary conditions are generated from the time dependent free-space solutions. The boundary information is mapped into the frequency domain using the Discrete Fourier Transform. The solution for the electric field components is obtained by solving a sparse-complex system of linear equations. The hybrid method has been tested for both waveguide and cavity configurations. Numerical tests performed on waveguides and cavities for inhomogeneous lossy materials highlight the accuracy and computational efficiency of the newly proposed hybrid computational electromagnetic strategy.

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Developmental progression and differentiation of distinct cell types depend on the regulation of gene expression in space and time. Tools that allow spatial and temporal control of gene expression are crucial for the accurate elucidation of gene function. Most systems to manipulate gene expression allow control of only one factor, space or time, and currently available systems that control both temporal and spatial expression of genes have their limitations. We have developed a versatile two-component system that overcomes these limitations, providing reliable, conditional gene activation in restricted tissues or cell types. This system allows conditional tissue-specific ectopic gene expression and provides a tool for conditional cell type- or tissue-specific complementation of mutants. The chimeric transcription factor XVE, in conjunction with Gateway recombination cloning technology, was used to generate a tractable system that can efficiently and faithfully activate target genes in a variety of cell types. Six promoters/enhancers, each with different tissue specificities (including vascular tissue, trichomes, root, and reproductive cell types), were used in activation constructs to generate different expression patterns of XVE. Conditional transactivation of reporter genes was achieved in a predictable, tissue-specific pattern of expression, following the insertion of the activator or the responder T-DNA in a wide variety of positions in the genome. Expression patterns were faithfully replicated in independent transgenic plant lines. Results demonstrate that we can also induce mutant phenotypes using conditional ectopic gene expression. One of these mutant phenotypes could not have been identified using noninducible ectopic gene expression approaches.

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Neural networks (NNs) are discussed in connection with their possible use in induction machine drives. The mathematical model of the NN as well as a commonly used learning algorithm is presented. Possible applications of NNs to induction machine control are discussed. A simulation of an NN successfully identifying the nonlinear multivariable model of an induction-machine stator transfer function is presented. Previously published applications are discussed, and some possible future applications are proposed.

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The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

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This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

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The design and implementation of a high-power (2 MW peak) vector control drive is described. The inverter switching frequency is low, resulting in high-harmonic-content current waveforms. A block diagram of the physical system is given, and each component is described in some detail. The problem of commanded slip noise sensitivity, inherent in high-power vector control drives, is discussed, and a solution is proposed. Results are given which demonstrate the successful functioning of the system

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The aim of this project was to investigate the in vitro osteogenic potential of human mesenchymal progenitor cells in novel matrix architectures built by means of a three-dimensional bioresorbable synthetic framework in combination with a hydrogel. Human mesenchymal progenitor cells (hMPCs) were isolated from a human bone marrow aspirate by gradient centrifugation. Before in vitro engineering of scaffold-hMPC constructs, the adipogenic and osteogenic differentiation potential was demonstrated by staining of neutral lipids and induction of bone-specific proteins, respectively. After expansion in monolayer cultures, the cells were enzymatically detached and then seeded in combination with a hydrogel into polycaprolactone (PCL) and polycaprolactone-hydroxyapatite (PCL-HA) frameworks. This scaffold design concept is characterized by novel matrix architecture, good mechanical properties, and slow degradation kinetics of the framework and a biomimetic milieu for cell delivery and proliferation. To induce osteogenic differentiation, the specimens were cultured in an osteogenic cell culture medium and were maintained in vitro for 6 weeks. Cellular distribution and viability within three-dimensional hMPC bone grafts were documented by scanning electron microscopy, cell metabolism assays, and confocal laser microscopy. Secretion of the osteogenic marker molecules type I procollagen and osteocalcin was analyzed by semiquantitative immunocytochemistry assays. Alkaline phosphatase activity was visualized by p-nitrophenyl phosphate substrate reaction. During osteogenic stimulation, hMPCs proliferated toward and onto the PCL and PCL-HA scaffold surfaces and metabolic activity increased, reaching a plateau by day 15. The temporal pattern of bone-related marker molecules produced by in vitro tissue-engineered scaffold-cell constructs revealed that hMPCs differentiated better within the biomimetic matrix architecture along the osteogenic lineage.

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The Arabidopsis thaliana NPR1 has been shown to be a key regulator of gene expression during the onset of a plant disease-resistance response known as systemic acquired resistance. The npr1 mutant plants fail to respond to systemic acquired resistance-inducing signals such as salicylic acid (SA), or express SA-induced pathogenesis-related (PR) genes. Using NPR1 as bait in a yeast two-hybrid screen, we identified a subclass of transcription factors in the basic leucine zipper protein family (AHBP-1b and TGA6) and showed that they interact specifically in yeast and in vitro with NPR1. Point mutations that abolish the NPR1 function in A. thaliana also impair the interactions between NPR1 and the transcription factors in the yeast two-hybrid assay. Furthermore, a gel mobility shift assay showed that the purified transcription factor protein, AHBP-1b, binds specifically to an SA-responsive promoter element of the A. thaliana PR-1 gene. These data suggest that NPR1 may regulate PR-1 gene expression by interacting with a subclass of basic leucine zipper protein transcription factors.

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A series of lithium niobate powders were synthesized by the combustion method at different heating rates. The effect of heating rate on the crystal composition of lithium niobate powders was investigated by powder X-ray diffraction measurements. It has been found that the lithium content in the as-synthesized lithium niobate powders increases with decreasing the heating rate. On the basis of the existed structure-property relationship of lithium niobate single crystals, it was concluded that high quality lithium niobate powders can be effectively synthesized at a lower heating rate (in the range of 5-10 C/min) by the combustion method.

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Pipe insulation between the collector and storage tank on pumped storage (commonly called split), solar water heaters can be subject to high temperatures, with a maximum equal to the collector stagnation temperature. The frequency of occurrence of these temperatures is dependent on many factors including climate, hot water demand, system size and efficiency. This paper outlines the findings of a computer modelling study to quantify the frequency of occurrence of pipe temperatures of 80 degrees Celsius or greater at the outlet of the collectors for these systems. This study will help insulation suppliers determine the suitability of their materials for this application. The TRNSYS program was used to model the performance of a common size of domestic split solar system, using both flat plate and evacuated tube, selective surface collectors. Each system was modelled at a representative city in each of the 6 climate zones for Australia and New Zealand, according to AS/NZS4234 - Heat Water Systems - Calculation of energy consumption, and the ORER RECs calculation method. TRNSYS was used to predict the frequency of occurrence of the temperatures that the pipe insulation would be exposed to over an average year, for hot water consumption patterns specified in AS/NZS4234, and for worst case conditions in each of the climate zones. The results show; * For selectively surfaced, flat plate collectors in the hottest location (Alice Sprints) with a medium size hot water demand according to AS/NZS2434, the annual frequency of occurrence of temperatures at and above 80 degrees Celsius was 33 hours. The frequency of temperatures at and above 140 degrees Celsius was insignificant. * For evacuated tube collectors in the hottest location (Alice Springs), the annual frequency of temperatures at and above 80 degrees Celsius was 50 hours. Temperatures at and above 140 degrees Celsius were significant and were estimated to occur for more than 21 hours per year in this climate zone. Even in Melbourne, temperatures at and above 80 degrees can occur for 12 hours per year and at and above 140 degrees for 5 hours per year. * The worst case identified was for evacuated tube collectors in Alice Springs, with mostly afternoon loads in January. Under these conditions, the frequency of temperatures at and above 80 degrees Celsius was 10 hours for this month only. Temperatures at and above 140 degrees Celsius were predicted to occur for 5 hours in January.

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Appropriate pipe insulation on domestic, pumped storage (split), solar water heating systems forms an integral part of energy conservation measures of well engineered systems. However, its importance over the life of the system is often overlooked. This study outlines the findings of computer modelling to quantify the energy and cost savings by using pipe insulation between the collector and storage tank. System sizes of 270 Litre storage tank, together with either selectively surfaced, flat plate collectors (4m2 area), or 30 evacuated tube collectors, were used. Insulation thicknesses of 13mm and 15mm, pipe runs both ways of 10, 15 and 20 metres and both electric and gas boosting of systems were all considered. The TRNSYS program was used to model the system performance at a representative city in each of the 6 climate zones for Australia and New Zealand, according to AS/NZS4234 – Heat Water Systems – Calculation of energy consumption and the ORER RECs calculation method. The results show:  Energy savings from pipe insulation are very significant, even in mild climates such as Rockhampton. Across all climates zones, savings ranged from 0.16 to 3.5GJ per system per year, or about 2 to 23 percent of the annual load.  There is very little advantage in increasing the insulation thickness from 13 to 15mm. For electricity at 19c/kWh and gas at 2 c/MJ, cost savings of between $27 and $100 per year are achieved across the climate zones. Both energy and cost savings would increase in colder climates with increased system size, solar contribution and water temperatures.  The pipe insulation substantially improves the solar contribution (or fraction) and Renewable Energy Certificates (RECs), as well as giving small savings in circulating pump running costs in milder climates. Solar contribution increased by up to 23 percent points and RECs by over 7 in some cases.  The study highlights the need to install and maintain the integrity of appropriate pipe insulation on solar water heaters over their life time in Australia and New Zealand.

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This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.