Competences-full

Luleå University of Technology:

  • Using models to understand the interaction between electromagnetic radiation and the components of the atmosphere (gases, aerosols, clouds, …) & (to apply this knowledge).
  • Instrument development (in-situ, air-or space-borne).
  • Acquirement, processing, & interpretation of measurements to understand relationships between the atmosphere and the environment.
  • More information : https://atmospheres.research.ltu.se

 

Novia University of Applied Sciences:

  • Experience of research within the wind power sector and contributes with broad knowledge regarding among others wind measurements, wind analyzes, knowledge about social acceptance and wind turbine noice.
  • Experience with environmental geo-spatial data, including climate summaries, acoustic measurements and machine learning methods.
  • Sonic Detection and Ranging (SODAR) equipment for wind speed measurements.
  • Experience and software tools for terrain and wind modeling (WindPro & WAsP)

 

Seinäjoki University of Applied Sciences:

  • Wide expertise on 3D Modeling, Animation, Virtual Reality and CAVE Visualization of Virtual 3D Models.
  • CAVE (Cave Automatic Virtual Environment) is used for Visualization of 3D Objects and Worlds in a Immersive Virtual Room where the viewer is surrounded by five display walls of computer generated stereoscopic, tracked Virtual Reality.

Read more about virtual reality tools.

 

Tampere university of Technology:

  • Experience on Virtual Engineering and User testing with Virtual Prototypes. The group has State-of-the-art facilities in Virtual reality tools. Special knowledge on human testing with Virtual Reality tools.
  • Knowledge of modelling and simulation of mechanical and fluid power systems. Knowledge on mechanical and hydraulic drivelines.

 

Umeå University:

  • Department of Mathematics and Mathematical Statistics, has research groups in Mathematical Statistics developing stochastic models and statistical methods for analysis of spatiotemporal data, functional data, and high-dimensional genomics data.
  • Expertise in spatiotemporal statistical modeling with various application areas. Related to WindCoE, we are dealing with spatiotemporal data, such as wind turbine acoustics data, atmosphere and climate data, wind energy production in space and time.
  • Department of Mathematics and Mathematical Statistics, and its research group on computational mathematics, has broad knowledge and tools which enable possibilities for advanced simulations and analysis.
  • Department of Computer Science, and its research group on Logic and Applications (http://www.cs.umu.se/english/research/groups/logic/), provides individualization of logic, and also embracing a concept of logics in dialogue
  • Logic is also tightly coupled with logical methodology for computational intelligence, where logically enriched computational intelligence provides a logical scope going far beyond traditional approaches to logic and ontology, and can be seen in applications for the public and private sectors
  • Information and process is intertwined in particular in industrial area ecosystems, like WindCoE’s overall focus area of energy production, distribution and consumption

 

UiT The Arctic University of Norway:

  • Good expertise & knowledge in the field of Wind energy in cold climate particularly in icing conditions
  • Good infrastructure and facilities for numerically simulating the wind resource assessment over large terrains and complex multiphase numerical simulations of atmospheric ice accretion on large wind turbines.
  • Expertise in designing of ice detection and mitigation systems for operations in cold climate.
  • Wind tunnels and cold chamber facility suitable for testing arctic wind power.
  • Laboratory for simulating electricity in the grid.

Read more about our research facilities.

 

University of Vaasa:

  • Modelling and measurement activities about wind turbine sound.
  • Measurement microphones and other equipment for recording wind turbine sound.
  • Experience in multivariate statistics and machine learning algorithms which are needed in measurement data analysis.