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  1. cordis.europa.eu

    The key achievements corresponding to the four objectives are listed below. 1. Efficient use of HPC resources in the simulation of flow around a wind turbine across all relevant operating conditions: 1.1 Novel algorithms have been developed and implemented for accurate simulation for flow over turbines as well as Fluid-Structure interaction (FSI) algorithm to capture the highly non-linear ...
  2. cordis.europa.eu

    Dec 11, 2023The HPCWE consortium consists of 13 partners representing the top academic institutes, HPC centres and industries in Europe and Brazil. By exploring collaborations between Europe and Brazil, 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 ...
  3. HLRS High Performance Computing Center Stuttgart

    https://www.hlrs.de › projects › detail › hpcwe

    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 ...
  4. Use Case 1: Validation of algorithms and codes for flow around a wind turbine Optimal hybrid solution for wind resources Use Case 2: Validation of algorithms and codes for optimization Effective scale-integration in wind energy beyond state-of-the-art Use Case 3: Validation of algorithms and codes for scale integration Results from WP1 and WP3
  5. The Impact of the HPCWE Project on Wind Resource Assessment Model Efficiency Our research generated methods that deliver better value from a computational efficiency standpoint. Although the project seeks to look ahead to the application of increased computation resources, this must not become an objective by itself. The necessity for increased HPC
  6. Within HPCWE we are developing better ways to model wind farm wakes within the mesoscale modelling, using a "turbine layout aware" approach. This requires an efficient handling of IO related to accessing look up tables of aggregated wind turbine thrust for different wind directions and wind speeds based on pre-calculated higher resolution ...
  7. webarchiv.hlrs.de

    • Pre-HPCWE - tendency averaging was done as post-processing - every 5 sec (same as the model time-step) tendencies written out by WRF • In HPCWE the method of Chen et al (2020) has been implemented - tendency averaging performed during execution ("inline" averaging)
  8. epcc.ed.ac.uk

    The goal of HPCWE is to address the key open challenges in applying HPC to wind energy, including efficient use of HPC resources in wind turbine simulations, accurate integration of meso- and micro-scale simulations, and optimisation. The consortium will develop novel algorithms, implement them in state-of-the-art codes and test the codes in ...

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