Validation and Optimization of an Open-Source Novel Nonlinear Froude-Krylov Model
for Advanced Design of Wave Energy Converters
Wave energy represents a great untapped potential, but modern technologies are not economically viable yet, mainly due to high investment risk and modelling uncertainties during design/development stages. Accurate and computationally fast mathematical models are essential tools for effectively and reliably designing wave energy converters (WECs).
Although WEC dynamics are typically very nonlinear, linear (imprecise) models are extensively used due to their computational convenience; in contrast, nonlinear models currently available are more accurate but too slow for design optimisation or control applications.
This fellowship purports to develop, validate, and disseminate a novel class of nonlinear models, which will realise an unprecedented pairing of accuracy and computational speed (100 to 1000 times faster than homologous existing models).
Conversely to other, slower nonlinear models, this novel model can facilitate effective design and optimisation of the device, enable real-time power optimisation and model-based control. The project will greatly impact the wave energy community, making a high-performance modelling tool easily accessible to any stakeholder for a variety of advanced design purposes. This project is comprised of 3 work packages, which accomplish: (1) validation of the model for axisymmetric devices; (2) expansion and
validation of the model for pitching platform devices, and (3) enhancement of computation performance and release of an open-source software.
In addition, this fellowship will expand the career horizons of the fellow: a highly multidisciplinary plan is defined, building upon and extending beyond his current competencies. The fellow is well-positioned to undertake this project, allowing him to fully develop innovative ideas from his PhD research. This fellowship will provide the fellow with an unparalleled opportunity to grow as a scientist and engineer, launching him on a trajectory to a productive and rewarding scientific career.
This research has received funding from the European Research Executive Agency (REA) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 832140