As energy developers plan to ramp up U.S. offshore wind production, they need to design turbines that can withstand hurricanes and other powerful storms. Although intense winds, high waves, and torrential rains can severely damage turbines and other wind farm infrastructure, the impacts of such extreme weather events are not fully understood by the energy industry.

In a comprehensive review paper published last month in the Journal of Renewable and Sustainable Energy, a team of scientists critically examined the landscape of tropical storm observation technology. They also reviewed advanced physics-based modeling and data-driven models that use artificial intelligence and machine learning.

The scientists, led by Argonne National Laboratory with co-authors at the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR), concluded that more research is needed to strengthen the resilience of next-generation offshore wind turbines against extreme weather conditions. This may include the application of new methods, such as machine learning, to bridge gaps from the scale of weather events (a few kilometers) to the scale of turbines (as small as a few meters).

“This type of collaboration is vital for assessing gaps in our knowledge about these extreme events so we can help the nation make use of its excellent offshore wind resources,” said NSF NCAR scientist Sue Ellen Haupt, a co-author of the paper.

The research is part of a larger effort by scientists at NSF NCAR and collaborators to better understand and predict the impact of weather conditions and changing climate on offshore wind production.

For more about last month’s paper, see the Journal of Renewable and Sustainable Energy press release.

See all News