Sensitivity analysis of wind load estimation method based on elliptic Fourier descriptors

This paper presents a new approach of wind loads estimation. The method used in the paper is based on Elliptic Fourier Descriptors (EFD) which are used for ship frontal and lateral closed contour representation. This approach takes into account all aspects of the variability of the above-water frontal and lateral ship profile. It is very suitable for assessing wind loads on marine structures wherever we have a wind load database for a group of similar vessels. In this way the cheaper and faster calculation can bridge the gap between ship shapes for which calculations or experiments have already been made. The Generalized Regression Neural Network (GRNN) is trained by elliptic Fourier descriptors of closed contours and Blendermann wind load data derived from wind tunnel tests for a group of ships. The trained neural network is used for the wind coefficient estimation with respect to the variability of lateral container vessel contours. The results are compared with available experimental data and investigation how small changes in the contour geometry affect the overall estimation is performed.