Hybrid method for estimating wind loads on ships based on elliptic Fourier analysis and radial basis neural networks

• A novel wind load estimation method was developed.
• A hybrid model based on elliptic Fourier descriptors (EFD) and generalized regression neural network (GRNN) was proposed.
• EFD were used for description of frontal and lateral ship profile and training of GRNN.
• Proposed approach takes into account all aspects of the variability of the above water frontal and lateral ship profile.

Wind loads on ships and marine objects are complicated phenomena 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 a challenge because of its implications for various analyses related to ship stability, ship speed estimation, maneuvering, station keeping, and berthing. This paper presents a new approach to wind load estimation on ships and other marine objects. The method is based on elliptic Fourier features of a closed contour 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. For the purpose of multivariate nonlinear regression, the radial basis neural network is trained by elliptic Fourier features of closed contours and wind load data derived from wind tunnel tests for three groups of ships: offshore supply vessels, car carriers and container ships. The trained neural network is used for the estimation of non-dimensional wind load coefficients. The results are compared with experimental data.