The rise of a low-carbon society, compatible with economic growth and environmental sustainability, is pending on a number of technological evolutions and breakthroughs. In that line, the role played by wind energy is deemed to increase further in the next decades. The development of performant wind farms is pending upon the performance of each turbine composing the wind farm, and on the optimal harvesting of the local wind resources. A wind park performance is nowadays predicted assuming standard profiles of mean incoming velocity, turbulence intensities and scales, etc. corresponding to standard terrain topographies and atmospheric conditions.
One main limitation of such standards is that the assumed flow and turbulence properties were established to fit databases gathered on a limited number of locations, which are by definition not representative of the quite various terrain configurations nor local micro-meteorological situations that can be met in practice. This is a concern for complex terrains and is furthermore hampering the implementation of wind turbines in urban environments, which constitutes nevertheless an important component of future environmentally-friendly Smart Cities thanks to the favorable local flow accelerations, pressure build-up, canyon effects, etc. offered by an urban canopy.
The ambition of this multi-disciplinary training platform is the development and application of advanced meso/microscale atmospheric models and the assessment of the impact of real terrain and local atmospheric effects on the predicted aerodynamic performance, structural dynamics and noise emissions. Obviously, human factors become a critical issue when considering implementing wind turbines in densely populated urban environments. The inter-dependencies between those factors (visual vs. acoustic effects, age or occupation, etc.), which complicate further the analysis of the motivations for a community to endorse or reject a new project, will be addressed as well.
The coordinator of the project is Dr. Sophia Buckingham from the von Karman Institute for Fluid Dynamics and the deputy coordinator is Professor Leandro Dantas De Santana from the University of Twente.
The partners of the project are Valeo Systèmes Thermiques SAS (VAL), Dedan Kimathi University of Technology (DKUT), Enerall srl (ENE) and Université de Sherbrooke (UdeS).
The beneficiaries are von Karman Institute for Fluid Dynamics (VKI), Universiteit Twente(UTW), National Technical University of Athens (NTUA), Siemens Industry Software nv (SISW), Samtech SA (SAMTECH), Wageningen University (WU), Technische Universiteit Delft (TUD), Universidad Politécnica de Madrid (UPM), Siemens Gamesa Renewable Energy AS (SGRE), The Nottingham Trent University (NTU), Universidad Nacional del Litoral (UNL), Centre Scientifique et Technique du Bâtiment (CSTB).
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 860101.
zEPHYR has the following objectives:
Two on-shore application areas will be considered:
i) horizontal axis wind turbines (HAWTs) with sizes corresponding to the current state-of-the-art (~ 5-10 MW), and
ii) urban wind turbines, with a focus on the most promising concepts for urban integration such as vertical axis wind turbines (VAWTs), diffuser-augmented wind turbines (DAWTs) and building-integrated wind turbines (BIWTs).
Besides the aerodynamic performance and durability issues that are raised by the specific flow features of urban environments, the successful deployment of green Aeolian energy in cities will crucially depend on the visual and acoustic annoyance aspects, affecting the societal acceptance of such novel concepts.
As a matter of fact, it now appears to this consortium that the efficient exploitation of available wind resources is hindered by several factors:
Clearly, addressing the above issues requires a very comprehensive approach. A key difficulty thereto is the quite complex interlacing of multi-physical aspects (aerodynamic, structural, acoustic, etc.), which cannot be tackled separately. In that respect, progress is somewhat undermined by the classical engineering education system, where scientific teaching is rather fragmented discipline-wise. As a result, freshly graduated engineers are lacking the multi-disciplinary background that is necessary to tackle these challenges and propose innovative technologies. More specifically, freshly graduated engineers show an insufficient knowledge about the possibilities that are nowadays offered by new optimization techniques, robust design based on Uncertainty Quantification and other rapidly emerging fields such as Big Data and machine learning, to account for variable and/or uncertain input parameters. While it is acknowledged that those advanced techniques will become part of the corpus of methods that must be mastered by young engineers, they are rarely taught at university level.