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This consortium of academic institutes, HPC centers, and industrial partners in Europe and Brazil is developing novel algorithms and state-of-the-art codes to support the development of more efficient technologies for wind power.

Wind energy has become an increasingly important as a clean and renewable alternative to fossil fuels in the energy portfolios of both Europe and Brazil. At almost every stage in wind energy exploitation ranging from wind turbine design, wind resource assessment to wind farm layout and operations, the application of HPC is a must. The goal of HPCWE is to address the following key open challenges in applying HPC on wind energy: (i) efficient use of HPC resources in wind turbine simulations, via the development and implementation of novel algorithms. This leads to the development of methods for verification, validation, uncertainty quantification (VVUQ) and in-situ scientific data interpretation; (ii) accurate integration of meso-scale atmosphere dynamics and micro-scale wind turbine flow simulations, as this interface is the key for accurate wind energy simulations. In HPCWE a novel scale integration approach will be applied and tested through test cases in a Brazil wind farm; and (iii) adjoint-based optimization, which implies large I/O consumption as well as storing data on large-scale file systems. HPCWE research aims at alleviating the bottlenecks caused by data transfer from memory to disk.

The HPCWE consortium consists of 13 partners representing the top academic institutes, HPC centres and industries in Europe and Brazil. By exploring this collaboration, this consortium will develop novel algorithms, implement them in state-of-the-art codes and test the codes in academic and industrial cases to benefit the wind energy industry and research in both Europe and Brazil.

HLRS will lead the Dissemination & Exploitation work package and co-organize a project workshop with the University of Nottingham. The HLRS will also contribute to development of an HPC framework for verification, validation, uncertainty quantification (VVUQ). This framework will support the evaluation of considered scale integration, as well as data reduction techniques of I/O data according to its suitability for wind energy optimization.


01. June 2019 -
30. November 2021


EU Horizon 2020

Project partners

  • The University of Nottingham, United Kingdom
  • Danmarks Tekniske Universitet, Denmark
  • Electricité de France (EDF), France
  • Imperial College of Science Technology and Medicine, United Kingdom
  • The University of Edinburgh (EPCC), United Kingdom
  • Universidade de São Paulo, Brazil
  • Universidade Estadual de Campinas, Brazil
  • Universidade Federal de Santa Catarina, Brazil
  • Universiteit Twente, Netherlands
  • VORTEX Factoria de Calculs SL, Spain


Eu Logo: Funded by the European Union