Naslov
A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks

Autori
Prpić-Oršić, Jasna ; Valčić, Marko ; Čarija, Zoran

Izvornik
Journal of marine science and engineering (2077-1312) 8 (2020), 7; 539, 21

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
wind loads ; container ships ; Reynolds-averaged Navier–Stokes equations (RANS) ; Generalized Regression Neural Network (GRNN)

Sažetak
The estimation of wind loads on ships and other marine objects represents a continuous challenge because of its implication for various aspects of exposed structure exploitation. An extended method for estimating the wind loads on container ships is presented. The method uses the Generalized Regression Neural Network (GRNN), which is trained with Elliptic Fourier Descriptors (EFD) of sets of frontal and lateral closed contours as inputs. Wind load coefficients (Cx, Cy, CN), used as outputs for network training, are derived from 3D steady RANS CFD analysis. This approach is very suitable for assessing wind loads on container ships wherever there is a wind load database for a various container configuration. In this way, the cheaper and faster calculation can bridge the gap for the container configurations for which calculations or experiments have not already been made. The results obtained by trained GRNN are in line with available experimental measurements of the wind loads on various container configuration on the deck of a 9000+ TEU container ship obtained through a series of wind tunnel tests, as well as with performed CFD simulation for the same conditions.

Izvorni jezik
Engleski

Znanstvena područja
Brodogradnja, Strojarstvo, Tehnologija prometa i transport, Temeljne tehničke znanosti

POVEZANOST RADA