Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "approximation network" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
The ANN approximation of the CH4 combustion model : the mixture composition
Autorzy:
Kowalski, J.
Powiązania:
https://bibliotekanauki.pl/articles/246942.pdf
Data publikacji:
2010
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
modeling
internal combustion engines
approximation
artificial neural network
combustion process
chemical species
Opis:
The calculation of the changing of the combustion mixture composition during the combustion process of the CH4 is presented of the paper. Correct calculation results of the mixture composition during the combustion process in combustion chambers of internal combustion engines is important to define the heat release calculation, modeling and simulation of the combustion phenomena. The paper presents results of calculations for the GriMech 3 kinetic mechanism of the methane combustion for different thermodynamic parameters and the composition of the combusted mixture. Results of the kinetic calculation of combustion process are qualitatively consistent with the data available in literature. The second purpose of research was the approximation of obtained results with the trained artificial neural network. Input data needed to approximate mole fractions of considered in the GriMech 3 mechanism combustion process chemical species consisted of 52 mole fractions of initial chemical species and temperature and pressure process. For all considered chemical species the mean square error did not exceed a value of 1-10-2 %, but the maximum error for a single value of 43 species excess even more than 100% of the value of mole fraction values taken from kinetic calculations. Single values of errors disqualify the neural network application for modeling of mole fractions of chemical species.
Źródło:
Journal of KONES; 2010, 17, 2; 233-240
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The ANN approximation of the CH4 combustion model : the heat release
Autorzy:
Kowalski, J.
Powiązania:
https://bibliotekanauki.pl/articles/246946.pdf
Data publikacji:
2010
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
modelling
internal combustion engines
approximation
artificial neural network
combustion process
heat release
Opis:
The calculation of the heat release from the combustion process of the CH4 is presented of the paper. Correct calculation results of the heat released from combustion is important for design, modelling and testing phenomena in combustion chambers of internal combustion engines. The paper presents results of calculations for the kinetic mechanism of methane combustion GriMech 3 for different thermodynamic parameters and composition of the combusted mixture. The calculations were performed for all possible configurations of the variable temperaturę range from 1100K to 3600K, the variable pressure in the range of 2MPa to 5MPa, variable humidity of charged air from 10 to 30 grams of water per l kg of air and variable mole fractions of charge air. Results of the kinetic calculation of combustion process are qualitatively consistent with the data available in literature. The next stage of research was approximation of obtained results with the trained artificial neural network. Input data needed to approximate the energy of the combustion process consisted of 52 mole fractions of chemical species and temperature and pressure process. Approximation results have meant square error not exceeded 0.04% for the test data and 0.02% for the validation data. The maximum error for a single result was 1.9% compared to data obtained with chemical kinetic calculations.
Źródło:
Journal of KONES; 2010, 17, 2; 225-232
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An artifical neural network usage in measurement of exhaust gas emission from marine engines: case study
Autorzy:
Kowalski, J.
Rudzki, K.
Powiązania:
https://bibliotekanauki.pl/articles/2073611.pdf
Data publikacji:
2016
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
marine engine
emission
neural network
measurement
power approximation
silnik okrętowy
emisja
sieć neuronowa
pomiar
Opis:
The paper presents the case study of use the artificial neural network to predict the main propulsion marine engine load. Mentioned load of the engine is important parameter to assess the emission level of toxic compounds into the atmosphere according to ISO standard and MARPOL convention. The engine load depends on the ship speed, rotational speed of the engine, propeller blades settings, the direction and the speed of wind, the condition of sea and the direction and the speed of sea currents and construction parameters of the ship. The realization of the aim of the work requires the direct measurement of presented parameters and measurement of exhaust gas composition. The experiment was carried out onboard STS “Pogoria”. Obtained results are enough to use the ANN to predict engine load to measure the emission level of toxic compounds.
Źródło:
Journal of Polish CIMEEAC; 2016, 11, 1; 87--93
1231-3998
Pojawia się w:
Journal of Polish CIMEEAC
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies