posted on 2015-02-10, 10:22authored byPeter Argyle
Offshore wind power generation is projected to be the United Kingdom’s largest
contributor to the European Union’s 2020 renewable energy target, with large
numbers of wind turbines clustered into wind farms with capacities comparable to
fossil fuelled power stations. The degree of power loss caused by the wake affected
region behind each turbine is known to vary under different atmospheric stability
conditions. Accurately predicting these losses for a variety of likely scenarios before
new farms are built can significantly reduce the financial risk of private investment.
The aim of this work was to investigate the structure of the offshore atmosphere and
incorporate the findings relating to atmospheric stability into Computational Fluid
Dynamics (CFD) simulations of large offshore wind farms to reduce financial
investment risk in non-neutral stability conditions. This work incorporates three
meteorologically established methods of calculating stability conditions into CFD
simulations of large offshore wind farms using the Monin-Obukhov Similarity Theory
(MOST). As MOST ideally requires meteorological parameters measured on-site
using a mast for extended periods of time to obtain even a small collection of
validation data, alternative methods of describing atmospheric conditions and
corresponding wake behaviour are investigated which only require data obtainable
by LiDAR. This has the potential to reduce the length of data collection campaigns,
whilst also using more flexible instruments and thus increasing cost efficiency.
The software front-end tool Windmodeller, which drives the ANSYS CFX software, is
used to benchmark four separate two-equation turbulence models, each assuming
neutral atmospheric stability conditions. Production data from four European offshore
wind farms are used for validation purposes. Of these models, the Shear Stress
Transport (SST) model consistently performed the worst, whilst modifying the RANS
turbulence constant, 𝐶𝜇, only alters the location within a line of turbines where the
standard 𝑘-𝜀 model was most accurate. The unsteady RANS model variation, which
incorporates both the Coriolis effect and a stably stratified capping layer, was found
III
to have the smallest root-mean-squared error values for the largest wind farm and so
was chosen to form the basis of the simulations incorporating atmospheric stability.
The Obukhov Length required for MOST is incorporated into the CFD simulations
using surface fluxes, water temperatures and atmospheric thermal gradients. There
are only small variations in simulation accuracy between methods when simulating
Neutral conditions, with the thermal gradient method performing best. Under stable
conditions the sea surface temperature approach is most accurate, although it is also
the least accurate under unstable conditions and was unable to generate the more
extreme Unstable conditions. Although the flux method was less accurate than the
gradient method in absolute terms, the variance of its errors at individual turbine
locations was consistently smaller. The validation process for using MOST
techniques was complicated by a lack of sufficient field data after the rigorous
filtering required by the theory’s assumptions.
The preliminary work using alternative methods of describing atmospheric conditions
within CFD simulations did not suffer from a lack of validation data, but was
unsuccessful at maintaining the required wind shear profiles across the whole
domain. Recommendations are made to improve control over these parameters with
models such as unsteady RANS, and to find a suitable successor to the actuator
disc theory now wind shear values across a turbine are becoming significant.
Funding
E.On through an EPSRC CASE award
History
School
Mechanical, Electrical and Manufacturing Engineering
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Publication date
2014
Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.