Toward the Coevolution of Novel Vertical-Axis Wind Turbines
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 836656
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2014.2316199
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 284
- Volume
- 19
- Issue
- 2
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2014
- URL
-
http://dx.doi.org/10.1109/TEVC.2014.2316199
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 29
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper describes the first known design of a pair of heterogeneous, interacting vertical wind axis turbines, achieved using machine learning techniques and 3D printing. The work, supported by the Leverhulme Trust, was later scaled to wind tunnel testing and received a variety of media coverage, eg, Vice’s Motherboard “Design Mining Reimagines Engineering with Evolution and Neural Networking” (19/1/15). Under EPSRC funding (EP/N00574/1), the approach presented has since been applied to the design of arrays of microbial fuel cells in collaboration with microbiologists seeking to use them to provide cheap energy and water purification in developing countries.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
