Advanced Analytics

Fluids modelling to enhance wind turbine performance

Computational fluid dynamics (CFD) is an indispensable tool for optimising wind turbine performance.

Our wind turbine CFD simulation demonstrates how we leverage advanced numerical approaches to analyse and enhance the efficiency of turbine designs.

Assessing the impact of extreme waves on wave energy converters

Wave energy converters (WECs) are devices designed to harness the huge potential renewable energy source of ocean waves. In contrast to typical offshore structures these devices are generally designed to be in-tune with waves and hence undergo large motions due to wave forces.

It is of significant importance to accurately estimate not only their performance in moderate sea conditions but also their responses in extreme waves. CFD provide an indispensable tool to model wave interaction with WECs in range of conditions.

Rafiee, A., and Fievez, J., “Numerical Prediction of Extreme Loads on CETO Wave Energy Converter”, In: Proceedings of EWTEC 2015, The 11th European Wave and Tidal Energy Conference, Nantes, France 2015.

Machine learning to improve ship motions

To develop a superior motion control system for a high speed ship, we built a model of the highly dynamic physical system of the ship motions from observation data using state-of-art machine learning algorithms.

The machine learning models are used with real-time optimisation algorithms to estimate and enhance system response to external disturbances and control inputs. Various sources of data are used, such as sensors and data from external APIs, for both model building and in the deployed solution.

Rafiee, A., van Someren, M., Ellery, D., Pretlove, L., and Malcolm, A., “Predictive Motion Control of High Speed Crafts”, In: 20th Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT'21), 2021.