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Wyszukujesz frazę "thermal network" wg kryterium: Temat

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    Wyświetlanie 1-5 z 5
    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:
    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:
    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:
    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
      Wyświetlanie 1-5 z 5

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