Development of Artificial Neural Networks to Predict the Response Amplitude Operators for Seakeeping of Ships Navigating at Forward Speed

Abstract

This work focuses on the application of Artificial Neural Network (ANN) to assess the seakeeping of ships navigating with forward speed, based on the calculation of the ship’s Response Amplitude Operators (RAO). This research presents a methodology for obtaining the optimal ANN architecture, generating the ship database used for training, and data treatment to enable the prediction of the targets. The dataset is generated with a customized 3D potential code used to solve the wave diffraction-radiation problem using the Boundary Element Method (BEM) for different wave headings and a range of Froude numbers.In order to assess the developed tool, six ships not included within training database are used to compared the ANNs predictions against BEM results. The results show deviations of less than 3% compared to BEM for RAO curves. Moreover, RAO curves exhibit high adjustment compared with BEM results for different encounter wave frequencies. Furthermore, ANN’s computational times show a speedup of x3750 respect to BEM computations.

Keywords: machine learning, Artificial Neural network, Seakeeping, Response Amplitude Operator, artificial intelligence