engleski

Wind load estimation method based on Elliptic Fourier Descriptors

Wind loads on ships and marine objects are complicated phenomenon because of the complex configuration of the above water part of the structure. The estimation of wind loads on ships and other marine objects represents the continuous challenge because of its implication on various analysis related to ship stability, ship speed estimation, manoeuvring, station keeping and berthing. This paper presents a new approach of wind loads estimation. The method is based on Elliptic Fourier Descriptors (EFD) which are used for ship frontal and lateral closed contour representation. Therefore, this approach takes into account all aspects of the variability of the above water frontal and lateral ship profile. 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 of an offshore vessel. The results are compared with the experimental data.

Wind load estimation; Elliptic Fourier Descriptors; Generalized Regression Neural Network

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