To validate the CFD model, the long term measurements at the two fixed positions (EM and KL, Section 4.3) were used (Section 4.3). The mobile measurements at QB were made only for a few hours and they cannot serve validation purposes alone [Schatzmann and Leitl,2011;Schatzmann et al.,1997], but they can give confidence to the CFD model when long term measurements exist. The short-term data SM1 and SM3 were only used as the SM2 were not uniform and could not be used for comparison purposes. Although the anemometer at EM building failed on August, 2015 due to a fault on one transducer pair, 6 months of measurements (February 2015 - July 2015) are considered sufficiently long period to be used as validation data [Blocken et al.,2012].
the values of mean wind speed and wind direction. The measurements were clus- tered in wind direction intervals of 10, 45 and 90 degrees in order to investigate the influence of the wind sector ‘width’ on the results. Then, the wind speed ratios were calculated by dividing the wind speed values at the locations of the anemometers (UKL, USM1, USM2) by the reference wind speed. The Edith Murphy building was used as the reference wind speed (UEM,ref) for the Kimberlin Library and the Kim- berlin Library was used as the reference wind speed for the short-term measurements (SM) (UKL,ref).
As shown in Table4.2 the wind speed ratios for 10, 45 and 90 degrees wind direction intervals are similar for North and South wind directions. For the West direction there is a deviation of around 23% between 10 or 45 degree intervals and 90 degrees. As the wind aligns to the west the Library is located in the wake region of the Queens building and the wind speed reduces. On the other hand, the west direction benefits the anemometer on the Edith Murphy building, where the wind speed increases and hence the wind ratio UKL/UEM decreases. However, the impact of the wind direction on the wind speed in absolute values is small. Specifically, the mean wind speed at EM increases from 4.12 m/s to 4.50 m/s as the wind interval decreases from 90 degrees to 45 degrees and the wind speed at KL decreases from 2.87 m/s to 2.47 m/s respectively. Hence, although the reduction of the wind speed ratio is quite large (around 23%) the wind speed difference is only 0.3-0.4 m/s at each measurements point. This is attributed to the fact that the wind speeds are small and little increase or decrease of them results in high percentage error. In the East direction there is also a quite high percentage error of around 20% between the 10 or 45 degree intervals and 90 degrees. However, the absolute difference in wind speeds is 0.06 m/s at EM (from 3.67 m/s to 3.73 m/s) when the wind direction interval reduces from 90 degrees to 10 degrees and 0.24 m/s at KL (from 2.11 m/s to 1.87 m/s).
Wind speed ratios (UKL/UEM) Measurements clustered in:
10o 45o 90o CFD
N 0.70 0.72 0.67 0.68
E 0.49 0.47 0.59 0.46
S 0.79 0.78 0.81 0.81
W 0.57 0.57 0.70 0.57
Table 4.2: Wind speed ratios as calculated from the wind measurements clustered into 10 degrees, 45 degrees, 90 degrees and as calculated from CFD for four wind directions.
Generally, the smaller the wind direction intervals, the more accurate the results. However, it is impractical to do calculations for each wind direction and the measure- ments should be clustered in wind direction intervals. Intervals of 45 to 10 degrees i.e. 8 to 36 wind directions, have been used, based on how much the change in wind direction affects the wind speed ratio [Bechmann, 2012; Irshad, 2012; Kalmikov et al.,2010].
In this work, the wind flow was calculated for four wind directions, since the wind speed ratios were not significantly influenced by the wind direction, based on the wind measurements at the locations the anemometers were installed.
Table 4.2 also presents the wind speed ratios as calculated from the CFD simula- tions using the statistically averaged flows from the DES calculations (Section 4.5) and Figure4.15 compares them with the measurements. As shown, there is a good agreement with a deviation of less than 10% apart from the North direction of SM1 where a discrepancy of 15% was found. In this position, only two hours measure- ments were carried out and hence, there are not enough data to rely on. Also, noticeable speed gradients exist (Figure4.16) and hence, some shift in measurement position can influence the simulation results.
Figure 4.15: Comparison between numerical and experimental (10 degree interval) wind speed ratios (U/Uref) in the locations of anemometers for four wind directions -North (N), East (E), South (S), West (W).
Figure4.17shows the difference in wind direction between the locations of anemome- ters and the reference wind as calculated for the field measurements and the CFD results. A good agreement between measurements and CFD results is found for the angle deviation of the wind direction between the KL and the reference wind at EM (∆φ = φ− φref). Although the deviation exceeds 20% in 3 of the 4 measurements (West, North and East direction) and at first sight the discrepancy might seem to be quite large, the actual difference between measured and simulated values are less than 3 degrees. This is explained by the fact that the ∆φ is very small (<10), and little difference in the angle deviation, of the order of 2 to 3 degrees, gives a high deviation. Figure 4.18 illustrates the situation, presenting the values of the angle deviation indicating a rather good agreement.
The results for the short-term measurements (SM) are less good in terms of ∆φ (50% to 70% deviation). This might be attributed to the fact that the anemometer was not aligned correctly to the North direction, as for the short-term measurements
(a)
(b)
Figure 4.16: Wind speed mean in the position (red point) of SM1 for North wind direction at (a) xz plane and (b) at yz plane.
the alignment was based on eye observations and hence the error increases.
Summarising, the overall agreement is quite good, and the discrepancies at some points can be attributed to the difficulty to extract the exact coordinates of the points of measurements and the failure to calibrate the anemometer to read the North direction.
As regards the number of the wind directions simulated, they should be chosen based on how much the wind direction affects the wind speed ratios. In this work, the measurements indicate that there is small effect. However, if one wants to investigate
Figure 4.17: Comparison between numerical and experimental (10 degree interval) angle deviation between the locations of anemometers and the reference wind (∆φ = φ− φref) for four wind directions -North (N), East (E), South (S), West (W).
Figure 4.18: Comparison between numerical and experimental (10 degree interval) angle deviation of wind direction between the location of anemometers at KL and the reference wind at EM (∆φ = φ− φref) for four wind directions -North (N), East (E), South (S), West (W).
further the response of this model to the wind direction, wind simulations of smaller wind intervals should be done and found the impact of the wind direction on wind speed ratio at various places.
4.8
Conclusions
In this Chapter the predictive capabilities of the DDES-SA model using the Open- FOAM CFD library were further investigated. The DDES-SA approach was applied at the DMU campus and the results compared with high frequency anemometer data. The mean velocity predictions above rooftop at a well exposed building and at a partly sheltered building were examined and found to be in good agreement with the anemometer data. Very limited CFD studies of complex urban areas have been validated using field measurements in this way.
The results obtained with the DDES-SA model in this study as well as the study of the two test cases developed by AIJ (Chapter 3) have offered robustness and accuracy over a range of wind conditions. However, this benefit came at some cost in terms of the computational resources required and time. Three super computers were engaged to perform the simulations (one at the DMU, one at Loughborough university and one at the university of Leeds) and it took almost a year to obtain the results. Nevertheless, during this time a lot more cases were tested before setting-up the final configuration of the models.
In the further study (Chapter 6) of wind resource at the De Montfort university campus, the wind behaviour will be described in terms of the reduction factors (Section 6.3) i.e. wind speeds normalised by the reference wind speed (8 m/s at 60 m). As the simulations have very high Reynolds number, the wind behaviour is the same at any reasonable reference wind speed [Heath et al., 2007].
In a highly turbulent environment, such as the urban areas, turbulence intensity can affect substantially turbine performance and hence, further studies of turbu- lence predictions above the roof are required. This issue will be examined later in Section7.2.2.
Meteorological data collection and
analysis
5.1
Introduction
The use of reliable meteorological data is of major importance in wind turbines installation planning. However, it is not usually economic or practical to make long- term measurements in any potential urban development site for installation and therefore, the use of existing data is imperative. Consequently, for a methodology to be generalized to a wide range of sites, it is desirable to be able to translate data available from public weather stations to the target location.
Here, a method of estimating the hourly annual wind speed of a selected site using one year’s recorded wind data at a remote site, such as airport weather stations, is presented. This process utilizes one year measured wind data of one site to extrapolate the annual wind speed at a new site, using the Wind Atlas Methodology [Landberg et al., 2003] as it is illustrated by Millward-Hopkins et al. [2013b]. In particular, this method scales wind speeds from the remote weather station up to the top of the urban boundary layer, the height at which the frictional effect of
the surface is assumed to be absent (Section 2.2.2.3), and then scales them down to a reference height, where the flow is assumed to be horizontally homogeneous [Grimmond and Oke,1999].
In this work, hourly concurrent measurements from East Midlands airport weather station and anemometers at De Montfort university are used. Statistical analysis has been used to investigate the proper cross-correlation of the wind speed between the sites. Then, wind data from the East Midlands airport weather station was transferred at the De Montfort university campus and the predictions compared with the field measurements in order to test the validity of the methodology.