Pregled bibliografske jedinice broj: 450038
Neural network selection of a maximum efficiency ship screw propeller
Neural network selection of a maximum efficiency ship screw propeller // Proceedings of the 13th Congress of International Maritime Association of Mediterranean (IMAM 2009) / Goren, Omer ; Okan, Barbaros ; Karakas, Safak ; (ur.).
Istanbul: ITU Faculty of Naval Architecture and Ocean Engineering, 2009. str. 447-451 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 450038 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Neural network selection of a maximum efficiency ship screw propeller
Autori
Matulja, Dunja ; Dejhalla, Roko ; Bukovac, Ozren ;
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 13th Congress of International Maritime Association of Mediterranean (IMAM 2009)
/ Goren, Omer ; Okan, Barbaros ; Karakas, Safak ; - Istanbul : ITU Faculty of Naval Architecture and Ocean Engineering, 2009, 447-451
ISBN
978-975-561-355-0
Skup
Congress of International Maritime Association of Mediterranean (13 ; 2009)
Mjesto i datum
Istanbul, Turska, 12.10.2009. - 15.10.2009
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
ship screw propeller; maximum efficiency; neural network
Sažetak
The choice of an optimum ship screw propeller is one of the persistent problems in naval architecture. Even though the selection can be performed using the propeller series diagrams or regression polynomials, the idea of the paper is to apply the advantages of the neural networks to this relatively simple problem as an introduction to more complex ship design problems. The network presented in the paper was created and trained to provide the characteristics of the maximum efficiency propeller. The neural network was created using Fast Artificial Neural Network Library. The structure is formed by a multilayer perceptron network with a single hidden layer. To train the network, data regarding the blade number, advance speed, delivered power, rate of revolution, diameter, pitch ratio, expanded area ratio as well as thrust and efficiency were set as inputs and outputs. The testing of the network proved its efficiency, which makes it not only a reliable tool for the preliminary screw propeller selection, but also a step towards the further development of neural networks applicable in various aspects of ship design process.
Izvorni jezik
Engleski
Znanstvena područja
Brodogradnja
POVEZANOST RADA
Projekti:
069-0691736-1667 - Numeričko modeliranje hidrodinamičkog opterećenja i odziva pomorskih objekata (Prpić-Oršić, Jasna, MZOS ) ( CroRIS)
Ustanove:
Tehnički fakultet, Rijeka