This investigation focuses on the modelling of a floating oscillating water column (FOWC) wave energy converter with a numerical code (ANSYS AQWA) based on potential flow theory. Free-floating motions predicted by the numerical model were validated against experimental data extrapolated from a 1:36 scale model device in regular and irregular sea states. Upon validation, an assessment of the device’s motions when dynamically coupled with a four-line catenary mooring arrangement was conducted at different incident wave angles and sea states ranging from operational to survivable conditions, including the simulation of the failure of a single mooring line. The lack of viscosity in the numerical modelling led to overpredicted motions in the vicinity of the resonant frequencies; however, the addition of an external linear damping coefficient was shown to be an acceptable method of mitigating these discrepancies. The incident wave angle was found to have a limited influence on the magnitudes of heave, pitch, and surge motions. Furthermore, the obtained results indicated that the mooring restoring force is controlled by the forward mooring lines under the tested conditions.

These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on diverse data sources of varying fidelities. The effectiveness of ML models in real-world applications hinges on the diversity, range, and quality of the data. Linear simulation techniques, chosen for their simplicity and cost-effectiveness, produce unrealistic and overly optimistic results. Conversely, high-fidelity experiments are prohibitively expensive. To address this, the study introduces an innovative feature engineering that incorporates uncertainty into features of linear models derived from higher fidelity modeling. This enhances productive data entropy, positively enhancing feature classification and improving the accuracy and feasibility of ML models in hydrodynamic responses of floating vessels. Tested with data from a known geometrical shape exposed to regular and irregular waves, the technique employs Ansys Aqwa for linear models. The results demonstrate the efficiency of the proposed technique, expanding the applicability of ML models in realistic scenarios. The application of the proposed approach extends beyond and can be further applied to any stochastic process, which expands the ML application for realistic use cases.

This study experimentally evaluates the survivability and hydrodynamic performance of a moored oscillating water column (OWC) wave energy converter (WEC) subjected to extreme cyclonic wave conditions emulating tropical cyclone Oma (2019). Laboratory tests recreated realistic cyclonic sea states using focused wave groups through the NewWave theory, combining singular and embedded focused waves within irregular seas to simulate extreme crests, troughs, and transient slamming events. Three mooring systems, including catenary, vertical-taut, and taut with 45° tendons, were tested to quantify their influence on structural response, chamber pressures, mooring tensions, and motion dynamics. The results revealed a critical trade-off: mooring configurations optimised for energy capture efficiency (e.g., taut systems) exhibited reduced survivability during extreme waves, while survivability-focused designs (e.g., catenary) compromised operational performance. Slamming pressures and transient loads were highly sensitive to wave group and mooring stiffness, with vertical taut systems experiencing the largest peak tensions. By integrating localised slamming pressure data with global mooring load measurements, this work provides a novel framework for balancing energy production and storm resilience in OWC design.