The primary purpose of the work is to explore the practicality of using Artificial Intelligence (AI); specifically, Machine Learning (ML) and Deep Learning (DL), to predict ship performance characteristics based on time-averaged and time-dependent data. Three application cases are studied. The first modelling case is a time-averaged ship propulsor performance dataset, the second and third modelling cases are a time-averaged and time series prediction of forces on a dynamic positioning ship operating in a broken ice-field. An ML-based model was developed to predict various propulsor coefficients of a podded propulsor, given the advance coefficient, cavitation condition, hub geometric variations, pod configurations and the azimuthing angle. The second modelling case involved developing an ML algorithm to predict time-averaged ice forces on DP-controlled ships at the given ranges of ice concentration, floe size, ice thickness, strength, density, drift speeds and direction. The third modelling case involved predicting the time-dependent forces on a DP-controlled ship at specific operating conditions and ice-field parameters. The AI-ML-based predictive models showed reasonable accuracy compared to the corresponding measurements and performed better than conventional regression-based models.
In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have driven advances in various fields, including medical, financial, research, design, and engineering. From self-driving cars to medical diagnosis, this new technology has changed the way people interact with technology. Given this technological shift, it becomes apparent that investigations into the efficacy of this technology in aiding physical model testing, full-scale measurements, post-processing data analysis and numerical modelling at the Ocean Coastal and River Engineering research centre of the National Research Council (NRC-OCRE) must be investigated.
The primary objective of the current work is to investigate several ML-based models to predict time-averaged and time-series vessel performance characteristics for given sets of vessel, environmental, operational data. The first step in achieving this objective was to conduct a literature review on current ML techniques, a review of open-source platforms for accomplishing this and transferred learning of existing ML model configurations.
