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Wyszukujesz frazę "thermal network" wg kryterium: Temat

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    Wyświetlanie 1-11 z 11
    Tytuł:
    An extended dynamic thermal model of a permanent magnet excited synchronous motor
    Autorzy:
    Mbo'o, Ch. P.
    Hameyer, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/140883.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    thermal modeling
    thermal network
    finite element method
    iron losses
    permanent magnet excited synchronous machine (PMSM)
    Opis:
    The purpose of this paper is to develop a dynamic thermal model of a permanent magnet excited synchronous motor (PMSM). The model estimates the temperature at specific points of the machine during operation. The model is implemented using thermal network theory, whose parameters are determined by means of analytical approaches. Usually thermal models are initialized and referenced to room temperature. However, this can lead to incorrect results, if the simulations are performed when the electrical machine operates under “warm” conditions. An approach is developed and discussed in this paper, which captures the model in critical states of the machine. The model gives feedback by online measured quantities to estimate the initial temperature. The paper provides an extended dynamic thermal model, which leads to a more accurate and more efficient thermal estimation.
    Źródło:
    Archives of Electrical Engineering; 2013, 62, 3; 375-386
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Influence of work environment on thermal state of electric mine motors
    Autorzy:
    Krok, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/140392.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    silnik elektryczny
    maszyna górnicza
    metoda sieci termicznych
    maszyna elektryczna
    wyrobisko górnicze
    electrical mining motors
    thermal network method
    exploitation of electric machines
    coal mine undergrounds
    Opis:
    The paper presents a model for calculations of the temperature field in electric mine motors with a water cooled frame. That model was worked out with use of modified and improved thermal networks developed by the author for determining the temperature distributions in different types of ac machines. Thermal calculations for a selected type of 400 kW mining motor were performed with use of an original computer program. Their results were compared with those obtained from measurements. On the basis of the verified simulation results there was determined the influence of value changes of parameters characterising the work environment condition (ambient temperature, inlet temperature and cooling water discharge, degree of covering the casing with coal dust) on the mining motor thermal state.
    Źródło:
    Archives of Electrical Engineering; 2011, 60, 3; 357-370
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Low-cost IR system for thermal characterization of electronic devices
    Autorzy:
    Kopeć, M.
    Więcek, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/114060.pdf
    Data publikacji:
    2018
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    thermal impedance
    Foster network
    thermal time constants
    temperature measurement
    Opis:
    In this article, a Low-cost measurement Infra-Red (IR) system for dynamic thermal testing of electronic devices is described. The element is powered by a step-function current and simultaneously temperature is measured by a fast single-detector IR head. The thermal impedance Zth(jω) is calculated using the Laplace transform and the Foster network is to get thermal time constants distribution.
    Źródło:
    Measurement Automation Monitoring; 2018, 64, 4; 103-107
    2450-2855
    Pojawia się w:
    Measurement Automation Monitoring
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Thermal modeling of planar and cylindrical biomedical multilayers structures in frequency domain
    Autorzy:
    Strąkowska, Maria
    Więcek, Bogusław
    Powiązania:
    https://bibliotekanauki.pl/articles/114518.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    thermal models
    Vector Fitting
    perfusion
    Pennes model
    Foster network
    Opis:
    Planar and cylindrical thermal models of biomedical multilayer structures with perfusion are presented in this paper. For each layer the models are solved analytically in frequency domain using the Laplace transform. Modeling the multilayer structure allows formulating the set of linear equations with unknown integral constants. As a result, the thermal impedance Zth(jω) is calculated. Next, the poles of thermal impedance are estimated using Vector Fitting (VF) method. Finally, distribution of thermal time constants allows evaluating the temperature response T(t) of the modeled structure.
    Źródło:
    Measurement Automation Monitoring; 2019, 65, 2; 32-36
    2450-2855
    Pojawia się w:
    Measurement Automation Monitoring
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An investigation of the relationship between encoder difference and thermo-elastic machine tool deformation
    Autorzy:
    Brecher, Christian
    Dehn, Mathias
    Neus, Stephan
    Powiązania:
    https://bibliotekanauki.pl/articles/24084708.pdf
    Data publikacji:
    2023
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machine tool
    thermal error compensation
    machine learning
    artificial neural network
    Opis:
    New approaches, using machine learning to model the thermo-elastic machine tool error, often rely on machine internal data, like axis speed or axis position as input data, which have a delayed relation to the thermo-elastic error. Since there is no direct relation to the thermo-elastic error, this can lead to an increased computation inaccuracy of the model or the need for expensive sensor equipment for additional input data. The encoder difference is easy to obtain and has a direct relationship with the thermo-elastic error and therefore has a high potential to improve the accuracy thermo-elastic error models. This paper first investigates causes of the encoder difference and its relationship with the thermo-elastic error. Afterwards, the model is presented, which uses the encoder difference to compute the thermo-elastic error. Due to the complexity of the relationship, it is necessary, to use a machine learning approach for this. To conclude, the potential of the encoder difference as an input of the model is evaluated.
    Źródło:
    Journal of Machine Engineering; 2023, 23, 3; 26--37
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Solar air heater performance prediction using artificial neural network technique with relevant input variables
    Autorzy:
    Ghritlahre, Harish Kumar
    Chandrakar, Purvi
    Ahmad, Ashfaque
    Powiązania:
    https://bibliotekanauki.pl/articles/240435.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    artificial neural network
    solar air heater
    thermal performance
    multilayer perceptron
    Opis:
    Solar air heater (SAH) is an important device for solar energy utilization which is used for space heating, crop drying, timber seasoning etc. Its performance mainly depends on system parameters, operating parameters and meteorological parameters. Many researchers have been used these parameters to predict the performance of SAH by analytical or conventional approach and artificial neural network (ANN) technique, but performance prediction of SAH by using relevant input parameters has not been done so far. Therefore, relevant input parameters have been considered in this study. Total ten parameters were used such as mass flow rate, ambient temperature, wind speed, relative humidity, fluid inlet temperature, fluid mean temperature, plate temperature, wind direction, solar elevation and solar intensity to find out the relevant parameters for ANN prediction. Seven different neural models have been constructed using these parameters. In each model 10 to 20 neurons have been selected to find out the optimal model. The optimal neural models for ANN-I, ANN-II, ANN-III, ANN-IV, ANN-V, ANN-VI and ANN-VII were obtained as 10-17-1, 8-14-1, 6-16-1, 5- 14-1, 4-17-1, 3-16-1 and 2-14-1, respectively. It has been found that ANN-II model with 8-14-1 is the optimal model as compared to other neural models. Values of the sum of squared errors, mean relative error, and coefficient of determination were found to be 0.02138, 1.82% and 0.99387, respectively, which shows that the ANN-II developed with mass flow rate, ambient temperature, inlet and mean temperature of air, plate temperature, wind speed and direction, relative humidity, and relevant input parameters performed better. The above results show that these eight parameters are relevant for prediction.
    Źródło:
    Archives of Thermodynamics; 2020, 41, 3; 255-282
    1231-0956
    2083-6023
    Pojawia się w:
    Archives of Thermodynamics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification of breast thermal images using artificial neural networks
    Autorzy:
    Jakubowska, T.
    Wiecek, B.
    Wysocki, M.
    Drews-Peszynski, C.
    Strzelecki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333564.pdf
    Data publikacji:
    2004
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    przetwarzanie termogramów
    sieci nuronowe
    klasyfikacja
    thermal image processing
    neural network
    classification
    Opis:
    In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used to distinguish healthy breast one with malignant tumour. We use image histogram and co-occurrence matrix to get thermal signatures and analyze symmetry between left and right side. The most promised method was based on wavelet transformation and nonlinear neural network classifier. The proposed approach was used in the pilot investigations in the medical centre which is permanently using thermograph for breast cancer screening, as an adjacent method for other classical diagnostic method, such as mammography.
    Źródło:
    Journal of Medical Informatics & Technologies; 2004, 7; MIP41-50
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling of back propagation neural network to predict the thermal performance of porous bed solar air heater
    Autorzy:
    Ghritlahre, Harish Kumar
    Prasad, Radha Krishna
    Powiązania:
    https://bibliotekanauki.pl/articles/240570.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    solar air heater
    porous bed
    thermal performance
    artificial neural network
    Levenberg-Marquardt algorithm
    Opis:
    The objective of present work is to predict the thermal performance of wire screen porous bed solar air heater using artificial neural network (ANN) technique. This paper also describes the experimental study of porous bed solar air heaters (SAH). Analysis has been performed for two types of porous bed solar air heaters: unidirectional flow and cross flow. The actual experimental data for thermal efficiency of these solar air heaters have been used for developing ANN model and trained with Levenberg-Marquardt (LM) learning algorithm. For an optimal topology the number of neurons in hidden layer is found thirteen (LM-13).The actual experimental values of thermal efficiency of porous bed solar air heaters have been compared with the ANN predicted values. The value of coefficient of determination of proposed network is found as 0.9994 and 0.9964 for unidirectional flow and cross flow types of collector respectively at LM-13. For unidirectional flow SAH, the values of root mean square error, mean absolute error and mean relative percentage error are found to be 0.16359, 0.104235 and 0.24676 respectively, whereas, for cross flow SAH, these values are 0.27693, 0.03428, and 0.36213 respectively. It is concluded that the ANN can be used as an appropriate method for the prediction of thermal performance of porous bed solar air heaters.
    Źródło:
    Archives of Thermodynamics; 2019, 40, 4; 103-128
    1231-0956
    2083-6023
    Pojawia się w:
    Archives of Thermodynamics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Thermal - electrical analogy in dynamic simulations of buildings: comparison of four numerical solution methods
    Autorzy:
    Michalak, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/1819185.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Koszalińska. Wydawnictwo Uczelniane
    Tematy:
    thermal-electrical analogy
    RC network
    lumped capacitance
    BESTEST
    Euler method
    Heun's method
    analogia termiczna
    analogia elektryczna
    metoda Eulera
    Opis:
    The lumped capacitance method is widely used in dynamic modelling of buildings. Models differ in complexity, solution methods and ability to simulate transient behaviour of described objects. The paper presents a mathematical description of a simple 1R1C thermal network model of a building zone. Four numerical methods were applied to solve differential equation describing its dynamics. For validation purposes two test cases (600 and 900) from the BESTEST procedure were used. In both cases detailed results were given. Better ability of the simulation model to reproduce transient behaviour of the modelled buildings was noticed in case of the lightweight object (case 600). Annual heating and cooling demand was within the reference range for heavyweight one (case 900). The kind of the computation method had no significant effect on simulation results.
    Źródło:
    Journal of Mechanical and Energy Engineering; 2020, 4, 2; 179--188
    2544-0780
    2544-1671
    Pojawia się w:
    Journal of Mechanical and Energy Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modeling and prediction of thermal conductivity ratio of metal-oxide based nano-fluids using artificial neural network and power law
    Autorzy:
    Hanief, Mohammad
    Irfan, Quresh
    Parvez, Malik
    Powiązania:
    https://bibliotekanauki.pl/articles/2173427.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    nano-fluids
    thermal conductivity ratio
    artificial neural network
    regression
    ANOVA
    nanociecze
    współczynnik przewodności cieplnej
    sztuczna sieć neuronowa
    regresja
    Opis:
    In this study, the thermal conductivity ratio model for metallic oxide based nano-fluids is proposed. The model was developed by considering the thermal conductivity as a function of particle concentration (percentage volume), temperature, particle size and thermal conductivity of the base fluid and nano-particles. The experimental results for Al2O3, CuO, ZnO, and TiO2 particles dispersed in ethylene glycol, water and a combination of both were adopted from the literature. Artificial neural network (ANN) and power law models were developed and compared with the experimental data based on statistical methods. ANOVA was used to determine the relative importance of contributing factors, which revealed that the concentration of nano-particles in a fluid is the single most important contributing factor of the conductivity ratio.
    Źródło:
    Chemical and Process Engineering; 2022, 43, 2; 159--163
    0208-6425
    2300-1925
    Pojawia się w:
    Chemical and Process Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting European thermal coal spot prices
    Autorzy:
    Krzemień, A.
    Riesgo Fernandez, P.
    Suárez Sánchez, A.
    Sánchez Lasheras, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/92159.pdf
    Data publikacji:
    2015
    Wydawca:
    Główny Instytut Górnictwa
    Tematy:
    thermal coal
    price forecasting
    time series analysis
    neural network
    autoregressive model
    węgiel energetyczny
    prognoza cen
    analiza szeregów czasowych
    sieć neuronowa
    model autoregresyjny
    Opis:
    This paper presents a one-year forecast of European thermal coal spot prices by means of time series analysis, using data from IHS McCloskey NW Europe Steam Coal marker (MCIS). The main purpose was to achieve a good fit for the data using a quick and feasible method and to establish the transformations that better suit this marker, together with an affordable way for its validation. Time series models were selected because the data showed an autocorrelation systematic pattern and also because the number of variables that influence European coal prices is very large, so forecasting coal prices as a dependent variable makes necessary to previously forecast the explanatory variables. A second-order Autoregressive process AR(2) was selected based on the autocorrelation and the partial autocorrelation function. In order to determine if the results obtained are a good fit for the data, the possible drivers that move the European thermal coal spot prices were taken into account, establishing a hypothesis in which they were divided into four categories: (1) energy side drivers, that directly relates coal prices with other energy commodities like oil and natural gas; (2) demand side drivers, that relates coal prices both with the Western World economy and with emerging economies like China, in connection with the demand for electricity in these economies; (3) commodity currency drivers, that have an influence for holders of different commodity currencies in countries that export or import coal; and (4) supply side drivers, involving the production costs, transportation, etc. Finally, in order to analyse the time series model performance a Generalized Regression Neural Network (GRNN) was used and its performance compared against the whole AR(2) process. Empirical results obtained confirmed that there is no statistically significant difference between both methods. The GRNN analysis also allowed pointing out the main drivers that move the European Thermal Coal Spot prices: crude oil, USD/CNY change and supply side drivers.
    Źródło:
    Journal of Sustainable Mining; 2015, 14, 4; 203-210
    2300-1364
    2300-3960
    Pojawia się w:
    Journal of Sustainable Mining
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

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