The tasks associated with STFC are broken down in further detail below:
3.1.2 Wind Tunnel Test Case
STFC will be taking the output from this test case for use in 3.3.4.
3.3.1 Analysis of CFD and LIDAR output data formats
In order for the comparison between CFD and experimental data to be performed, the format of these data must be known
in order to facilitate transformations to a common format. STFC will liaise with zCFD and LIDAR manufacturers to obtain
sample data for study.
3.3.2 Numerical tools for SWEPT Data Comparison
STFC will leverage our existing big data software to create a validation tool for CFD against LIDAR data. The figure below
demonstrates how this software will fit into the workflow; LIDAR and CFD output will be input into the software and
MapReduce algorithms will be used to transform the data into a common format, perform comparisons and summarise the
results. The comparisons will be modifiable to produce different outputs depending on the requirements of the user.
3.3.3 Numerical tools for SWEPT Data Comparison Report
A report on the reliability, performance and scope of possible data comparison and analysis will be produced.
3.3.4 Big Data for Windtunnel/CFD Validation
The tool developed in 3.3.2 will be used to validate the wind tunnel simulations from 3.1 against the experimental results
from 3.2.
3.3.5 Big Data for LIDAR/CFD Validation
The tool developed in 3.3.2 will be used to validate simulations from 3.1 against the LIDAR data gathered in 3.2 for a
variety of test cases.
3.3.6 Big Data for Validation Report
The findings of 3.3.4 and 3.3.5 will be reported, detailing results from several MapReduce algorithms and presenting these
results in such a manner that they can be used to manipulate future CFD simulations to improve their accuracy.
3.3.7 Automated Data Comparison
The potential for automated feedback from the results of CFD validation into the parameters of future CFD simulations will
be explored, beginning with the identification of areas of the simulation requiring a higher level of resolution. Such feedback
would add another level of innovation to the project.
3.3.8 Automated Data Comparison Report
The findings of 3.3.7 will be presented and recommendations made on how automated feedback could form part of future
projects.

Potential 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 and publication in the relevant journals. The main beneficiaries of the SWEPT2 project and the resulting modelling tool will be wind farm designers, developers and
researchers in the field of wind farm optimisation.