Wind resource modelling

It is essential to have site specific wind data before a wind turbine site can be appropriately chosen. Without physically measuring site specific data there are extensive wind mapping databases available for public use via the Department of Business, Enterprise and Regulatory Reform, European Wind Atlas and the CIBSE TRY data. This data can be used to estimate available power in the wind and therefore potential energy yield of a proposed site. However, these sources usually only list annual average wind speeds. This necessitates the employment of probability distribution techniques such as the Weibull or Rayleigh distribution methods7 to gain an estimate of annual wind speed distribution. Another problem is that recorded data is often measured in rural areas or near airports, which although useful for rural wind farm assessment may not be reliable or appropriate for urban areas. The dense and complex urban topography leads to lower annual mean wind speeds and increased turbulent flow causing rapid changes in wind direction, which produces extra stresses on mechanisms and components lowering the life expectancy of a system. It is, therefore more desirable and

accurate to accumulate site specific atmospheric data to fully understand the sites wind resource. Once an accurate wind resource is known a good estimate of energy yield can be produced to establish the effectiveness of each site, which will contribute towards determining the feasibility of urban turbines.

Fluid flow is a complex discipline and fully understanding the complex interaction of air particles within an urban environment would be beyond the scope of this investigation, but by utilising CFD tools and running computational simulations of the topography surrounding each site we can assess, with sufficient detail, air flow across urban topography. This simulation using atmospheric data collected from LSBU installed anemometry is used to study the urban topographies effect on the energy

generation potential of each site. CFD simulations will also be utilised to investigate 'what if' scenarios to installation optimisation suggestions, if appropriate.

A 1 mile radius 3D model of the area surrounding LSBU and the Strata was built within Trimble SketchUp as displayed in Figure 14.

In an ideal world not bound by computational processing limits a CFD model would have been run on the full 1 mile radius model to give a clear bird's eye view of how wind sweeps across the south of London. Unfortunately, due to the computational power available the models had to be scaled down. Urbawind CFD software was chosen as is it regarded as a robust and fast urban environment modelling tool. As part of the redevelopment of Niigata in Japan The Architectural Institute of Japan (Tomiyanga, Y. 2008) ran varying case studies to compare Urbawind's results with measurements taken before, during and after development had been completed. These case studies varied from a baseline map and simple block construction all the way to the completed construction of a district within Niigata. The results demonstrated the typical error of computations to be at most between 5 - 8.5 %. It was therefore deemed suitable to conduct the wind flow requirements of this project.

Figure 14: Displays a 1 mile radius of London topography centred on the Strata tower. This model was used in conjunction with Urbawind and CadnaA to assess wind flow and environmental noise propagation.

Due to the complexity and scale of calculations required to run a wind flow simulation Urbawind requires the following, minimum, computer specifications to compute a 300 m x 300 m area in under 2 hours: 2.4 Hz, Quad core, 8 GB RAM computer. A computer of similar spec was available but limited the range of simulations to discrete areas. Figure 15 and Figure 16 represent the 300 m2 area used for both the Strata tower and the LSBU campus. The prevailing wind for London is from the south west so to evaluate the south westerly winds effect on the turbine sites both installations were placed in the north eastern quadrant to allow maximum flow up to the turbines in order to assess the existing topographies effect on wind flow.

Work conducted by Mick Sagrillo (American Wind Energy Association Newsletter, 2006)

demonstrates that to sufficiently overcome ground drag and subsequent turbulence a turbines entire rotor should be mounted at least 30 feet (9.14 m) above anything within a 500 ft (152.4 m) radius. This criteria is sufficiently met as the tallest obstacle within a kilometre of the Strata stands at 85 m, 55 m lower than the Strata turbines hub height.

The 300 m2 area restriction due to computational power available for Urbawind is therefore not likely to negatively affect modelling results due to the Strata's height, surrounding topographical heights and in line with work presented by the Wind Energy Association. Of course, future construction could invalidate the results.

Figure 15: Displays a 300 m2 model of the Strata tower area

Figure 16: Displays a 300 m2 model of the LSBU campus including the tower block turbine site

The maximum building height within the LSBU calculation area is 35 m. When compared to the building heights depicted in Figure 14, 97 % of building heights in the area are at or below this height. Showing LSBU's surrounding topography to be typical of the area further demonstrates that the smaller 300 m2 calculation area centred on the LSBU turbine would be adequate.

Simulations run with Urbawind CFD software will be discussed in chapters 4.4 and 5.4 and used alongside atmospheric data to study the energy yield potential of both sites.

Figure 17: LSBU anemometer mounted on the CEREB rooftop.

As well as the previously mentioned defunct anemometry at the Strata site there are sensors installed at two locations within the LSBU campus. One ultrasonic anemometer and wind vane on the Centre for Efficient and Renewable Energy in Buildings (CEREB) building rooftop and one installed by the author next to the turbine at hub height on the LSBU tower block, Figure 18 shows the anemometers to be 100 m apart. The CEREB anemometer is installed upwind of the tower block anemometer in reference to a prevailing south westerly wind and mounted at 1.5 m on the rooftop next to a 1 m solid safety rail. CFD analysis shows the LSBU anemometer to be in a far clearer, less turbulent, strong wind resource from the south west.

Figure 19 and Figure 24 show the mean wind speed coefficients for the CEREB and LSBU

anemometer location, respectively. They show an approximate 50 - 60 % increase in expected wind flow from the south west at the LSBU location. LSBU anemometry readings will more accurately reflect the local wind resource, as it is installed at hub-height to give a reliable insight into LSBU turbine's wind resource. This will aid energy production estimations, the juxtaposition of which against actual energy yield will highlight the manufacturer power curves applicability (or lack of) in the urban environment.

Figure 19: Displays the Urbawind simulated wind flow across the CEREB anemometer

The following atmospheric data will be collected at both CEREB and Tower Block sites:  Wind speed  Wind direction  Temperature  Atmospheric pressure  Humidity

Logging and analysing of this data will allow predictions of site potential energy yield and will be compared with theoretical predictions in an effort to further understand the intricacies of urban wind turbine installation and their dos and don'ts. The comparison will also contribute to verifying the chosen CFD analysis model.

Clearer, stronger wind resources can be obtained with increasing height above obstacles, therefore further CFD simulations at greater heights above the Tower Block will be investigated in later sections to demonstrate possible rewards and returns for alternative installations.