Using Neural Networks to Predict the Response of a Floating Structure

Floating structures are influenced by the ocean environment which cause responses from the structures known as surge, sway, heave, roll, pitch and yaw. These responses are commonly approximated using linear potential theory, which neglects the viscous effects created by interaction between the body and fluid. For conventional ship hulls, Ikeda developed an empirical method to estimate the roll damping [Ikeda, 1978] which is successful for traditional vessel geometries. However, the development of unconventional ship hulls has resulted in the need of better ways to predict the response and viscous effects. This thesis investigated if Neural Networks could be utilised to predict the response of a floating structure, using experimental results. A two-dimensional model of a mid-ship section with large bilge boxes has been tested in Ladertanken, an experimental wave flume operated by SINTEF Ocean. Due to the large bilge boxes, the model represents an unconventional ship hull shape where the bilge boxes induce viscous effects. The model was freely-floating, although restrained to an area in the model using springs. A wave maker installed in the wave flume created waves with different wave period and wave steepness, which induced motions in sway, heave and roll. The comparison of the created waves versus the theoretical wave showed an average difference between 6.9 % and 7.9 % for both wave steepness and wave period. A bug in the processing software which transforms the analogue signal to a digital signal was found after the experiments were conducted, and there are indications that the results are affected. The results from the experiments could therefore not be used to analyse the development of hydrodynamic effects, however, the results were utilised as data to the response predictions. Firstly, a Linear Regression model using Stochastic Gradient Descent was created to predict sway, heave and roll motion as well as their response amplitude operators. The Linear Regression models obtained a Coefficient of Determination ranging between 30.0 % and 42.0 %, which is unsatisfactory. The linear regression models were included to compare to the Neural Network performance and thereby show the potential of Neural Network within non-linear problems. One Neural Network model was built and trained for each of the measured responses, where the responses were used as targets and the wave period and wave steepness were given as features. The input data is scaled using standar