The two-year project will involve 12 partners from across Europe and Brazil with a proven track record of HPC hardware, wind energy, numerical methods, software development and industrial applications.
Annual energy production by wind turbines reached around 10.4 percent in the EU by the end of 2016. Brazil, meanwhile, has the ninth largest wind capacity in the world and is experiencing more than 10 percent annual growth with nearly 500 wind farms already deployed.
HPC has been a must for each of these activities and is also essential to the supply change of wind energy, as well as other related industries, such as power systems, energy storage, etc.
"Wind as a clean and renewable alternative to fossil fuels has become an increasingly important contributor to the energy portfolio of both Europe and Brazil. By 2025 the wind power market is expected to grow over $110 billion. The HPC market is also a very important and growing market reaching total revenues of $11.4 billion by 2015 alone," explained project lead, Dr. Xuerui Mao, Faculty of Engineering at the University of Nottingham.
With €2 million in Horizon 2020 funding, the 'High Performance Computing for Wind Energy' (HPCWE) project will deliver a step change in the application of HPC on wind flow simulations and reshape almost every stage of wind energy exploration.
The goal of HPCWE is to address the key open challenges in applying HPC on wind energy, including efficient use of HPC resources in wind turbine simulations, accurate integration of meso- and micro-scale simulations, and optimization.
The consortium will develop algorithms, implement them in codes and test the codes in academic and industrial cases to benefit the wind energy industry and research in both Europe and Brazil.
Dr. Mao added, "HPCWE will have a direct impact on societal issues such as reduction of CO2 through promoting wind energy exploitation. The new HPC techniques will create job opportunities, particularly in consultancy in various aspects of wind energy such as wind resource assessment and wind farm optimization."
