The project is concerned with non-convectional direct stator winding slot cooling using water. The aim is to find optimal algorithm for control of water cooling. The control algorithms are tested on the experimental device, which is part of real synchronous machine with permanent magnets. The thermal model was built as a base for computational model of a machine without thermal sensors. The thermal model is possible used as predictor of machine heating in real time. This type of water cooling shows better effect on the machine heating than common water cooling system on the cover. and Obsahuje seznam literatury
The paper is concerned with computational simulation of stator winding heating of the synchronous machine. Software ANSYS 8.0 was used for computational simulation. Computational model considers heat pipe in the middle of winding slot. The results of computer simulation show the effect of direct winding cooling with water. The results of both methods of cooling were compared. Experimental device was created for verification of computational simulations. and Obsahuje seznam literatury
Carbonate rocks host several large water and hydrocarbon reservoirs worldwide, some of them highly heterogeneous involving complex pore systems. Pre-salt reservoirs in the Santos Basin off the south-east coast of Brazil, are an example of such rocks, with much attention focused on proper characterization of their petrophysical and multiphase flow properties. Since it is very difficult to obtain rock samples (coquinas) from these very deep reservoirs, analogues from north-eastern Brazil are often used because of very similar geological age and petrophysical properties. We used a coquina plug from an outcrop in a quarry in northeast Brazil to perform a comprehensive set of analyses. They included Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD), and micro-computed tomography (μCT) image acquisition using a series of pixel sizes, as well as direct permeability/ porosity measurements. Some of the experimental data were collected from the plug itself, and some from a small sample of the rock slab, including thin sections. Results included the carbonate rock composition and the pore system at different scales, thus allowing us to reconstruct and model the porosity and absolute permeability of the coquina using 3D digital imaging and numerical simulations with pore network models (PNMs). The experimental and numerical data provided critical information about the well-connected pore network of the coquina, thereby facilitating improved predictions of fluid flow through the sample, with as ultimate objective to improve hydrocarbon recovery procedures.
This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to re-direct their attention and resources in order to keep on searching for an optimum solution.
The flow of a mixture of liquid and solid particles at medium and high volume fraction through an expansion in a rectangular duct is considered. In order to improve the modelling of the phenomenon with respect to a previous investigation (Messa and Malavasi, 2013), use is made of a two-fluid model specifically derived for dense flows that we developed and implemented in the PHOENICS code via user-defined subroutines. Due to the lack of experimental data, the two-fluid model was validated in the horizontal pipe case, reporting good agreement with measurements from different authors for fully-suspended flows. A 3D system is simulated in order to account for the effect of side walls. A wider range of the parameters characterizing the mixture (particle size, particle density, and delivered solid volume fraction) is considered. A parametric analysis is performed to investigate the role played by the key physical mechanisms on the development of the two-phase flow for different compositions of the mixture. The main focuses are the distribution of the particles in the system and the pressure recovery.
In this paper a problem of multiple solutions of steady gradually varied flow equation in the form of the ordinary differential energy equation is discussed from the viewpoint of its numerical solution. Using the Lipschitz theorem dealing with the uniqueness of solution of an initial value problem for the ordinary differential equation it was shown that the steady gradually varied flow equation can have more than one solution. This fact implies that the nonlinear algebraic equation approximating the ordinary differential energy equation, which additionally coincides with the wellknown standard step method usually applied for computing of the flow profile, can have variable number of roots. Consequently, more than one alternative solution corresponding to the same initial condition can be provided. Using this property it is possible to compute the water flow profile passing through the critical stage.
The computational model of the reed-based element is in scope of this article. This element is studied for its potential suitability to generate an arfificial source voice. Compressed air is being used like a source of the energy to produce the voiced speech (similar to the healthy voiced speech). Two-way iteraction of solid and fluid part of the computational model has been considered for the solution. Computation has been performed by the finite element method (Ansys) and results have been processed by the sofsware MATLAB. Basic characteristics like a frequency spectrum and a fundamental frequency of generated source voice are evaluated. Relationship between deformation of the reed and the pressure in front of the reed is presented. This characteristic represents one of the basic phonation theories which are in our scope. This theory is based on the compressed air ‘bubble‘. and Obsahuje seznam literatury
Recently, there has been a significant emphasis in the forecasting of the electricity demand due to the increase in the power consumption. Energy demand forecasting is a very important task in the electric power distribution system to enable appropriate planning for future power generation. Quantitative and qualitative methods have been utilizedpreviously for the electricity demand forecasting. Due to the limitations inthe availability of data, these methods fail to provide effective results. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. This paper presents the computational modeling of electricity consumption based on the Neural Network (NN) training algorithms. The main aim of the work is to determine the optimal training algorithm for electricity demand forecasting. From the experimental analysis, it is concluded that the Bayesian regularization training algorithm exhibits low relative error and high correlation coefficient than other training algorithms. Thus, the Bayesian Regularization training algorithm is selected as the optimal training algorithm for the effective prediction of the electricity demand. Finally, the economic input attributes are forecasted for next 15 years using time series forecasting. Using this forecasted economic attributes and with the optimal Bayesian Regularization training algorithm, the electricity demand for the next 15 years ispredicted. The comparative analysis of the NN training algorithms for the proposed dataset and larger datasets obtained from the UCI repository and American Statistical Association shows that the Bayesian Regularization training algorithm yields higher correlation value and lower relative error than other training algorithms.
Using the STDP rule with metaplasticity, we show that to evoke long-term depression (LTD) or depotentiation of synaptic weights in the spiking model of granule cell is not easy. This is in accordance with a number of experimental studies. On the other hand, heterosynaptic LTD which accompanies homosynaptic long-term potentiation (LTP) is induced readily both in the model as well as in experiments. We offer possible explanation of these phenomena from STDP, metaplasticity and spontaneous activity. We suggest conditions under which it would be possible to induce homosynaptic LTD and depotentiation.
This paper presents a brief review of selected approaches used for computational modelling of bimaterial failure and for evaluation of interface failure resistance. Attention is paid to the approaches that assume absence of initial interface crack. The applicability of such approaches to rubber-steel interface failure evaluation is discussed in the paper. The approach based on the so called ‘cohesive zone model‘ is preferred and demonstrated by an example of computational modelling of rubber-steel interface failure during a peel-test. The results of peel-test computational modelling are presented. The influence of cohesive zone element number on the results is also analysed. The results are consistent with experimental data. and Obsahuje seznam literatury