4 papers · 1 filter
Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation
Yezhang Li, Stephan C. Kramer, Matthew D. Piggott
This study investigates the use of machine learning based mesh movement method, specifically the Universal Mesh Movement Network (UM2N), with depth integrated non-hydrostatic shall…
Geographic variability in reanalysis wind speed biases: A high-resolution bias correction approach for UK wind energy
Yan Wang, Simon C. Warder, Ellyess F. Benmoufok +4
Reanalysis datasets have become indispensable tools for wind resource assessment and wind power simulation, offering long-term and spatially continuous wind fields across large reg…
Mapping global offshore wind wake losses, layout optimisation potential, and climate change effects
Simon C Warder, Matthew D Piggott
This study assesses global offshore wind energy resources, wake-induced losses, array layout optimisation potential and climate change impacts. Global offshore ambient potential is…
The future of offshore wind power production: wake and climate impacts
Simon C Warder, Matthew D Piggott
Rapid deployment of offshore wind is expected within the coming decades to help meet climate goals. With offshore wind turbine lifetimes of 25-30 years, and new offshore leases spa…