MATHEO – Smart Mathematics for Offshore Wind – Webinars
October 2 - October 30
The JRL-ORE will host three free webinars in October that cover interesting research fields in the physical knowledge and mathematical model of offshore wind:
- Computational and experimental analysis of the overtopping on structures for offshore renewables.
The overtopping effect on offshore structures will be described, followed by an explanation of numerical modelling. The team will provide details about the analytical and experimental validation of the models.
Lecturers: TECNALIA, Department of Nuclear Engineering and Fluid Mechanics of the UPV/EHU
Date: October 2nd, 11am to 12pm
- Modelling and simulation of sediment material for offshore wind energy applications.
Description of the seabed complexity in terms of sediment types and SPH modelling; application of the numerical models to the interaction of drag embedded anchors with the seabed for floating wind.
Lecturers: TECNALIA, Department of Geodynamics of the UPV/EHU, BCAM, Department of Nuclear Engineering and Fluid Mechanics of the UPV/EHU
Date: October 16th, 11am to 12pm
- Challenges of applying deep neural networks to the offshore wind energy sector.
Description of the fundamentals of neural networks and application of deep learning techniques to the structural health monitoring -SHM- for the O&M cost reduction in offshore wind.
Lecturers: TECNALIA, Department of Applied Mathematics, Statistics and Operations Research of the UPV/EHU, BCAM
Date: October 30th, 11am to 12pm
The webinars will have one hour of duration and will be conducted in Spanish. They are aimed at transferring scientific knowledge to industrial companies working on offshore wind, but they are open to researchers and engineers interested in these topics. Registration for the webinars will open in September.
These webinars are part of the dissemination activities of the MATHEO project. MATHEO is a research project funded by the Basque Government under the Elkartek programme with the participation of TECNALIA, BCAM, and three departments of UPV/EHU, to advance in the physical knowledge and in the mathematical modelling of offshore wind, including the following research lines:
- Development of Lagrangian SPH approaches for modelling the non-Newtonian fluid at the seabed, and, investigate the interaction with piles and anchors and improve the future designs, validated experimentally in a wave flume.
- Development and training of algorithms based on data-driven Deep Learning approaches for early-damage detection of critical structural failures, optimal sensoring, estimation of remaining life and future redesign of components.
- Empirical modelling of non-linear phenomena occurring at the splash zone: overtopping, biofouling and corrosion.
- Finite element modelling of fracture and extension to novel materials, as for example fibre reinforced concrete.