SWEPT2

Lead Research Organisation: University of Bristol
Department Name: Aerospace Engineering

Abstract

The team at Bristol will be responsible for leading the experimental validation of the developed simulation tool (zCFD). This
will be performed using agreed test cases and, in particular, a detailed, iterative, comparison will be performed with data
from the experimental test case to be run at University of Surrey.

Planned Impact

The UK offshore wind sector is projected to grow to £8bn annually by 2020 so the economic benefits estimated to result
from the new wake modelling tool, at over 1% of project costs, could be considerable across the UK investment.
Research results will be communicated through the ORE Catapult (a project partners ideally suited to this) and publication
in the relevant journals.
 
Description Simulation tools have been developed that allow more accurate modelling of physics associated with off-shore wind turbines. New wake and turbulence models developed, and new experimental data has been produced for validations.
Exploitation Route Technology developed will provide new capability in terms of wind turbine modelling for accurate measures of energy production. New experimental data has also been produced for future validations. As a results University of Bristol has continued collaborative research with DNV-GL, and a recent EPSRC DTP-funded student is working in this area.
Sectors Aerospace

Defence and Marine

Digital/Communication/Information Technologies (including Software)

Energy

Environment

URL https://cfms.org.uk/news-events-opinions/news/2018/january/consortium-develops-advanced-wake-modelling-capability-for-the-wind-sector/
 
Description The impact of this work is recorded against grant ref EP/N508500/1.
First Year Of Impact 2020
Sector Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Energy,Environment
Impact Types Societal

Economic

 
Title CFD tool 
Description High fidelity simulation tool for wind turbines and off-shore flow properties. 
Type Of Material Improvements to research infrastructure 
Year Produced 2017 
Provided To Others? No  
Impact DNV-GL now using this tool for turbine performance prediction. 
 
Description Zenotech/CFMS 
Organisation Zenotech
Country United Kingdom 
Sector Private 
PI Contribution Via this grant, partnership formed with Zenotech and CFMS generally, considering automatic meshing and incorporating AI into the process.
Collaborator Contribution They will be providing meshes and/or test cases. Also possibility of funding a PhD studentship
Impact CFD and mesh algorithms integrated with AI technology.
Start Year 2016
 
Title CFD tool 
Description High fidelity simulation tool for wind turbines and off-shore flow properties. 
Type Of Technology Software 
Year Produced 2017 
Impact DNV-GL now using this tool for turbine performance prediction.