Computational modeling of wind flow over the University of Calgary campus

Abstract

This paper describes computer simulations of the wind flow over the University of Calgary campus undertaken to assess the likely regions for siting wind turbines and photovoltaic modules. The importation and cleaning of the building geometries is discussed along with the requirements for modeling adjacent buildings on the accurate simulation over one building in particular. The effect of the terrain and the many trees on the campus is shown to be significant for the wind resource over the roof of a six-storey building. It is shown that the choice of turbulence model is not critical if the purpose of the simulation is to identify regions for further exploration via wind speed measurements.

Keywords

Wind resource assessment urban wind energy computational fluid dynamics terrain trees turbulence models

Introduction

Deploying wind turbines in a built-up environment is a complex task when compared to siting them in a rural area unless the latter has significant variations in topography and roughness. The reasons relate to the complexity of flow over large buildings, which can include significant regions of stagnating and recirculating flow and their interaction with other buildings and any topography. Some of the difficulties are highlighted in the Warwick report on small-scale wind generation in UK cities (Encraft, 2008). With the majority of earth’s population now living in cities, it is essential that opportunities for distributed, often small-scale, electricity generation on rooftops, near industrial areas and so on, are aggressively exploited. Whereas photovoltaics (PV) currently is the most cost-effective technology for small-scale distributed generation when the resource availability is roughly equal, there are natural synergies with wind energy. PV generally requires low wind locations to minimize the extreme wind loads (see e.g. Stathopoulos et al., 2014), whereas wind turbines clearly perform best in high wind speeds and low turbulence. To find suitable locations for both technologies requires detailed wind resource assessment.

The main problem with wind resource assessment for small energy projects is the cost relative to the power produced. In many cases, computational fluid dynamics (CFD) offers a cheaper and more useful alternative than a measurement campaign. In any case, CFD is often a good first choice because it gives the whole wind flow field. This is especially useful in highlighting areas of high wind speed for potential wind turbine installation and low wind areas for PV. The highlighted areas can then be surveyed effectively by cup or other anemometers.

At a scale large compared to building height, the wind flow over a city experiences varying roughness superposed on a possibly varying topography. If the wind flows over a step change in roughness, an internal boundary layer forms and grows with streamwise distance. The flow above it is not affected by the step, apart, possibly, from a displacement required by continuity. Unfortunately, however, most turbines and PV modules will be installed on or around the buildings which represent the roughness elements, so there is no alternative to modeling much of the complex geometry with high fidelity. This has the disadvantage of requiring models for a whole city that can only be solved using massive computational resources (Franke et al., 2011). In addressing the problem of using CFD and wind speed measurements to map the renewable energy possibilities for the city of Calgary with a population of just over 1,100,000 living in an area of approximately 848 km², we decided to begin with the University of Calgary campus with an area of approximately 4.13 km². The reasons were:

the building geometries and topography were available in a form suitable for meshing for CFD analysis;

the University is keen to extend its current renewable energy generation of about 7.4 kWp of PV;

the campus is reasonably unsheltered locally to the west, the predominant wind direction, and so is approximately independent of the remaining city topography.

The major problem we faced was that no useful wind speed data has been collected over the campus. Therefore, we decided to undertake CFD wind flow assessments prior to a measurement campaign. The Energy Environment Experiential Learning (EEEL) building on the campus, shown in Figure 1, was selected as the main focus because it has a flat roof suitable for installation of PV and small wind turbines. The commercial CFD code, CFX, part of ANSYS 15, was chosen for the analyses.

Figure 1.

The EEEL building. Picture taken from the University of Calgary website.

Inlet boundary conditions

The wind rose for the campus was obtained from the Canadian Wind Atlas (CWA) (http://www.windatlas.ca) and is displayed in Figure 2, showing the prevailing wind directions are west, north-west, and southwest. The annual wind speed is 5.21 m/s at a 50 m height. The shape factor from the Weibull distribution given by CWA is 1.74 and the scale factor is 5.85 m/s.

Figure 2.

Annual wind rose from CWA within the campus region.

Based on the above information, the velocity at the inlet for the campus simulations was fitted to a log law, and the turbulent kinetic energy, k, and its dissipation rate, ε, follow the usual formulation for neutral conditions

U = \frac{u_{τ}}{κ} l n (\frac{z + z_{0}}{z_{0}})

(1)

k = \frac{u_{τ}^{2}}{\sqrt{C_{μ}}}

(2)

ε = \frac{u_{τ}^{3}}{κ (z + z_{0})}

(3)

where $u_{τ}$ is the friction velocity, z is the vertical displacement, $z_{0}$ is the aerodynamic roughness length, and κ is the von Kármán constant with a value of 0.412. $C_{μ}$ is a model constant set at 0.09. The value for $u_{τ}$ (0.301 m/s) was determined via the estimate of $z_{0} = 0.04 m$ from the CWA website and the annual wind speed at 50 m. The shear stress transport (SST) k−ω was chosen as the primary turbulence model as Tarokh et al. (2014) found it was the most accurate of six turbulence models when compared with wind tunnel measurements of the flow over a rectangular building.

Generating the campus geometry

The building geometry data was obtained from the Spatial & Numeric Data Services from the University’s Taylor Family Digital Library. The data was processed using ArcGIS software (http://www.esri.com/software/arcgis) for analyzing geographic information systems. It is able to process billions of cloud points taken from LiDAR and convert them into the much more useful digital data structure TIN (Triangulated Irregular Network). The data was converted to shape files before exporting them as a CAD model. The extrusions of the buildings to their actual height were performed in AutoCad Civil 3D. This is where simplifications were made to the buildings.

The ideal computational grid would include every aspect of the physical geometry of the urban setting at least to the scale of $z_{0}$ . This is unrealistic for a manageable computation. In order to reduce the computational burden, buildings surrounding the building of interest should be modeled only to the accuracy necessary for the accurate simulation of the former. The reduced models should capture the main shape (Blocken et al., 2012). Figure 3 shows the resulting campus model and Figure 4 shows the detailed and simplified model for Craigie Hall, one of the buildings surrounding EEEL which is the main focus.

Figure 3.

CAD model of the University of Calgary in AutoCad Civil 3D, when processing the shape files. The arrow shows the direction of a westerly wind and points to the EEEL building in the middle of the figure near the left edge.

Figure 4.

Explicit and implicit modeling of building geometries.

Terrain effects

The log law, equation (1), is a consequence of local equilibrium between the production of turbulence and its dissipation rate which occurs only when the terrain is flat and homogeneous and $z_{0}$ is constant. As shown by Mohamed and Wood (2013), the other terms in the turbulent kinetic energy equation, turbulent diffusion and advection, become important for the case of a flow over complex terrain (which may be moderate hills or other changes in elevation). There have been many calculations of wind flow over urban terrain which do not take into account the effects of terrain (see Blocken et al., 2012; Tabrizi et al., 2014). That is to say, the buildings were placed on a flat surface, not the real terrain. The present calculations used both flat ground and the actual terrain represented as described in the next section.

Representing the terrain

Terrain data in the form of spatial co-ordinates was provided by the Spatial and Numeric Data Services (University of Calgary). The points were imported into Matlab where they were parsed via the Delaunay function into triangulated networks (Figure 5). The surface function was then used before converting the output to a stereolithography (STL) format.

Figure 5.

Surface contour generated in Matlab.

Meshing was done in ANSYS ICEM. Figure 6 shows the meshed geometry of the campus on the generated terrain. Mesh lines are removed for clarity. The mesh was computed using the Octree meshing algorithm, which produces tetrahedral elements around the object surfaces. Four prismatic layers were grown from the terrain while five layers were extruded from the roofs of buildings.

Figure 6.

The meshed campus with meshing lines hidden for clarity. Refer to compass for the layout of the campus buildings. The x-axis points west.

The computational domain for the simulations with a westerly wind is shown in Figure 7. The domain size is based on the recommendations of Franke et al. (2004) who recommended a maximum blockage ratio of 3%. The blockage ratio in CFD is defined as the projected windward area of the buildings to the cross-section of the computational domain. This is to prevent artificial acceleration of the flow over the tallest building. The advection and turbulence numerics were solved via a high resolution scheme which is essentially a blend of first order and second order based on spatial gradient, described in detail by Barth and Jespersen (1989). The convergence criteria, the normalized residuals of the momentum and so on, were set to 10⁻⁵ as recommended by Franke et al. (2004). Details of the mesh size are given in the next section.

Figure 7.

The computational domain for the campus simulations.

Mesh independence

Prior to the actual simulations, grid independence studies were performed at the five locations depicted in Figure 8. The results are shown in Figure 9. Meshes 1, 2 and 3 consisted of 10,748,228 nodes, 13,435,285 nodes, and 16,659,753 nodes respectively.

Figure 8.

Mesh independence taken at five marked locations.

Figure 9.

Velocity profiles for different meshes at four locations on campus. The height of the tallest building on campus $Z_{tb}$ is 66 m and freestream velocity $U_{e}$ is 5.21 m/s. The origin of Location E is the midpoint of the leading edge of the roof of the EEEL building. (a) Location A. (b) Location B. (c) Location C. (d) Location D. (e) Location E.

The grid convergence index (GCI) is often used to report mesh independence studies without the need for grid-doubling (Roache, 1997). It is an error estimator (factor of safety included) between the discrete solutions of two grids based on a generalized Richardson extrapolation. We used this methodology to compute the GCI at the five different locations for mean wind speed profiles (we are primarily interested in the mean wind speed) from the ground to the top of the computational domain. The procedure is described in detail by Roache et al. (1986). $G C I_{12}$ and $G C I_{23}$ , where $G C I_{12}$ is the index for meshes 1 and 2 and so on, for Location A defined in Figure 8, are 1.103% and 0.108% respectively. For Location B, these values are 3.2% and 2.2249%. At Location C, there is a slightly larger error band between mesh 3 and mesh 2. $G C I_{23}$ is 10.4408% while $G C I_{12}$ is 3.8658%. At Location D, $G C I_{12}$ and $G C I_{23}$ are 0.2751% and 0.0728% respectively. Finally, at Location E, $G C I_{12}$ and $G C I_{23}$ are 1.5653% and 0.4435% respectively. Although there is some discrepancy at Location C, the error bands were considered acceptable. Nevertheless, the most refined grid, mesh 3, was chosen for the simulations.

Results and discussion

Figure 10 shows the difference between simulations with and without the terrain for the campus with respect to wind power density. The contours are shown for a plane 4 m above the EEEL building. Taller buildings protrude through the contour. Wind power density per unit area is computed via

P_{w} = \frac{1}{2} ρ A^{3} Γ (1 + \frac{3}{k_{s}})

(4)

where $Γ$ is the gamma function, $A$ is

A = \frac{U}{Γ (1 + 1 / k_{s})}

(5)

and $U$ is the mean wind speed (Romanić et al., 2015). Note that equation (4) is used only for the mean wind speed at 4 m above the roof of the EEEL building for each wind direction. All six simulations imply good wind power potential on the west side 4 m above the roof of the EEEL building (this is a typical height for a building-mounted wind turbine). The height of the EEEL building, $z_{b}$ , is 32 m.

Figure 10.

Distribution of $P_{w}$ over the campus for the actual terrain and flat ground. Wind is simulated at the inlet from the west, north-west and south-west. The height of the plane is 4 m above the roof of the EEEL building. Refer to compass for wind directions. Units for $P_{w}$ are in W/m². (a) Wind from west: actual terrain. (b) Wind from west: flat ground. (c) Wind from north-west: actual terrain. (d) Wind from north-west: flat ground. (e) Wind from south-west: actual terrain. (f) Wind from south-west: flat ground.

The immediate observation from the simulations for a west wind is that there is more $P_{w}$ for the flat ground case than for the one with actual terrain on the west side 4 m above the roof of EEEL. The wind coming from this direction to the EEEL building is affected mostly by terrain and not buildings (see Figure 6(b)) which underlines the importance of terrain and roughness for wind resource on the roofs of buildings.

In order to quantify the differences in $P_{w}$ between terrain and flat ground cases, an area integral of $P_{w}$ was computed for the plane 4 m above the roof of the EEEL building (shown in Figure 11). The results are displayed in Table 1.

Figure 11.

A cut-plane (highlighted in gray) taken 4 m above the roof of the EEEL building for the analysis of area integral of $P_{w}$ .

Table 1.

Area integral of $P_{w}$ comparisons between flat ground and terrain calculations 4 m above the roof of EEEL based on the cut plane shown in Figure 11.

Wind direction and terrain type	$P_{w}$ (W)
*West, flat ground	366, 874
*West, actual terrain	213, 869
*North-west, flat ground	666, 667
*North-west, actual terrain	641, 877
*South-west, flat ground	266, 695
*South-west, actual terrain	234, 990

As shown, flat ground cases give higher $P_{w}$ compared to the actual terrain. This is an important result because the differences clearly highlight terrain effects which are not even mentioned in Franke et al.’s (2004) recommendations on the use of CFD in wind engineering.

For further study of terrain effects, the vertical profiles on the line shown in Figure 12 for normalized U and k were extracted. Both properties are normalized by $U_{e}$ and $U_{e}^{2}$ respectively where $U_{e}$ is the wind speed at 50 m at the inlet, denoted in this paper as the reference wind speed. Figure 13 displays the results.

Figure 12.

Projection of a line from the roof of the EEEL building to assist in analyzing the velocity and turbulent profiles.

Figure 13.

Mean wind speed and turbulent kinetic energy profiles taken along Projection A for flat ground (green lines) and actual terrain (red lines) calculations. The top row shows the mean wind speed in the direction indicated and the bottom row shows k. Freestream velocity $U_{e}$ = 5.21 m/s and the height of the building $z_{b}$ is 32 m. The origin is at the EEEL roof. (a) Wind from west. (b) Wind from north-west. (c) Wind from south-west. (d) Wind from west. (e) Wind from north-west. (f) Wind from south-west (see colour version of this figure online).

As with the conclusion earlier that flat-ground simulations give higher $P_{w}$ , the general trend is similar for mean wind speed profiles taken along Projection A, apart from the north-west wind. For this case, the wind profiles for both cases appear to coincide for $z / z_{b} ⩽ 1$ . Note that the typical height for a building-mounted wind turbine falls in the range $z / z_{b} ⩽ 0.2$ . The flat-ground simulations give higher wind speeds (see Figure 14(a) and (c)) for $z / z_{b} ⩽ 1$ , indicating that it is not always the case that the inclusion of terrain in simulations results in reduced velocities.

Figure 14.

$P_{w}$ taken along Projection A for flat ground (green lines) and actual terrain (red lines) calculations. The height of the building, z_b, is 32 m. The origin is the EEEL roof. (a) Wind from west. (b) Wind from north-west. (c) Wind from south-west(see colour version of this figure online).

The vertical profiles for k shown in Figure 13(d) to (f) show more turbulence for the flat ground case near the roof. In general, more turbulence occurs when the wind is from the west. For the flat ground case, the profile of k relaxes toward the inlet value of $k / U_{e}^{2} = 0.012$ from equation (2) slower when the wind is from the south-west than when it is from the west or north-west. This could be due to interference from the buildings upstream for the south-west case.

The plots for $P_{w}$ profiles along Projection A are shown in Figure 14. The west and south-west profiles have the same trend for flat and actual terrain but there appears to be a shift in magnitudes.

Details of the flow over the roof of the designated building

When the wind hits the edge of a building, the flow separates and high turbulence is usually generated. The flow in this region above the roof is also characterized by a re-circulation region. It is well known that turbine performance drops in the presence of low mean wind speed and high turbulence. Hence it important to know not only the wind energy potential but also the regions and the size of the re-circulation zone. Figures 15, 16, and 17 depict the wind speeds at different planes parallel to the x- and y-axes for the three cases. These planes are re-oriented in the direction of the incoming velocity at the inlet and show the contours of velocity magnitude and streamlines around the EEEL building. The primary purpose of the planes is to show the re-circulation zones. It is important to know the wind energy potential but also the regions and the size of the re-circulation zone. Figures 15, 16, and 17 depict the wind speeds in different planes parallel to the x- and y-axes for the three cases. These planes are re-oriented in the direction of the incoming velocity at the inlet and show the contours of velocity magnitude and streamlines around the EEEL building. The primary purpose of the planes is to show the re-circulation zones.

Figure 15.

Wind speeds over the EEEL building with streamlines depicting areas of re-circulation. Wind is from the west. Wind speed is in m/s. (a) Plane 1. (b) Plane 2. (c) Plane 3. (d) Plane 4. (e) Plane 5.

Figure 16.

Wind speed over the EEEL building with streamlines depicting areas of re-circulation. Wind is from the north-west. Wind speed is in m/s. (a) Plane 1. (b) Plane 2. (c) Plane 3. (d) Plane 4. (e) Plane 5. (f) Plane 6. (g) Plane 7. (h) Plane 8. (i) Plane 9.

Figure 17.

Wind speeds over the EEEL building with streamlines depicting areas of re-circulation. Wind is from the south-west. (a) Plane 1. (b) Plane 2. (c) Plane 3. (d) Plane 4. (e) Plane 5. (f) Plane 6. (g) Plane 7. (h) Plane 8. (i) Plane 9.

As shown in Figures 15, 16 and 17, the wind speed increased (for the three cases) over the western part of the roof of the EEEL building. This region of increased speed is also devoid of re-circulation zones: the two characteristics which are advantageous for a wind turbine. Note however that wind turbines with sufficiently large rotor diameters would not perform well in this region (Van~Bussel and Mertens, 2005) because the large blades will traverse the re-circulation zone below the accelerated flow. At this stage, this region near the west edge of the EEEL building is marked as a potential area of good wind resource. The vector plot for one of the planes is shown in Figure 18.

Figure 18.

Vector plots showing the wind from west.

Figure 19 depicts the re-circulation zone above the EEEL building. It can be inferred from the velocity vectors that if a turbine were to be placed further from the edge of the building, then special attention would need to be paid to its rotor diameter to avoid being immersed in the re-circulation region. As an indicator of the size of the region, Figure 19 shows its depth (3.7 m) at one location.

Figure 19.

A closer look at the vectors on the west side of the EEEL rooftop.

Turbulence

Most wind turbine power curves exclude the effects of turbulence (Lubitz, 2014). It cannot be generalized that high turbulence always degrades the power output of a wind turbine, however, as increased turbulence means more power in the wind that is potentially exploitable. However, turbulence and hence gust induces sudden changes in the wind direction which is undesirable in harnessing wind energy.

The distribution of k for the campus is shown in Figure 20. As before, the k contours are in the horizontal plane 4 m above the roof of the EEEL building. The turbulence levels above the west side of the EEEL building, deemed as a potentially good site, are low compared to the other areas within the concentrated perimeter.

Figure 20.

Turbulent kinetic energy distribution for the three cases. The units for k are m²/s². (a) Wind from west. (b) Wind from west, zoomed in. (c) Wind from north-west. (d) Wind from north-west, zoomed in. (e) Wind from south-west. (f) Wind from south-west, zoomed in.

Effect of trees

As shown by Mohamed and Wood (2015b),trees can distort the flow over buildings as the increased turbulence and velocity defect are convected upwards, even for buildings of heights comparable to the EEEL building. We now analyze their effect on the flow over the EEEL building. The modeling of trees was based on equations (1) to (5) from Mohamed and Wood (2015b). Each tree was modeled as a fluid medium with a sink term introduced into the momentum equation and source terms in the transport of k and ε. The k-ε turbulence model was used. The tree simulations were done for a flat terrain with prismatic layers applied to the cells adjacent to the walls. The positions of the trees were obtained through the vegetative canopy data supplied by the Spatial & Numeric Data Services from the Taylor Family Digital Library; see Figure 21. The mesh with trees is shown in Figure 22(a) with 22(b) displaying the prismatic layers on the buildings and trees. All trees were assumed to be 18 m high which is the approximate height of an array of trees west of the EEEL building, and identical, to simplify the simulation.

Figure 21.

CAD representation of the vegetative canopy at the University of Calgary.

Figure 22.

Computational mesh used for calculating wind flow over the campus with modeled trees. (a) Computational grid with trees. (b) Zoomed-in view of grid.

The simulations followed the previous boundary conditions with the wind from the west. The velocity and k profiles were plotted along Line B (See Figure 23). The line was projected in a region of high wind power density.

Figure 23.

Velocity and turbulent kinetic energy profiles taken along a line at the top of the EEEL building. The wind direction is along the x-axis.

The profiles for velocity and turbulent kinetic energy were plotted along Line B defined in Figure 23 and they are depicted in Figures 24 and 25.

Figure 24.

Effects of trees on the velocity profiles taken at the top of the EEEL building along Line B in Figure 23. Freestream velocity $U_{e}$ is 5.21 m/s and building height $z_{b}$ is 32 m. The origin is at the roof of the EEEL building. (a) $U$ profile in the range $z / z_{b} = 0 - 2$ . (b) U profile very near the roof.

Figure 25.

Effects of trees on the k profiles taken at the top of the EEEL building along Line B in Figure 23. Freestream velocity $U_{e}$ is 5.21 m/s and building height $z_{b}$ is 32 m. The origin is the roof of the EEEL building. (a) k profile in the range $z / z_{b} = 0 - 2$ . (b) k profile very near the roof.

As shown, trees play a significant role in determining the wind speed over the six-storey EEEL building. The wind speed with trees does not exhibit a region of negative $\partial U / \partial z$ and is considerably larger than the treeless case close to the roof. The difference in velocity profiles spans a region between z / z_b ≈ 0.1 and $z / z_{b} \approx 1$ . There is an increase in velocity below z / z_b ≈ 0.1 (the first 3 m from the roof) when trees are included in the simulations. This is an important result because a wind turbine would most likely be installed in this range. This confirms the earlier finding about the effects of trees on the wind flow around the roof of buildings when the tree height is smaller than, but comparable to, the building height.

The turbulent kinetic energy profiles between the two cases show significant differences. Figure 25(a) shows a reduction of k in the range $z / z_{b} > 0.1$ . The energy budget in Figure 26 shows that the advection of k is greater in the presence of trees. The extra k generated by the trees is convected over the roof. The production term is larger for the case with trees near the roof but becomes smaller with no trees in the region $z / z_{b} = 0.01 - 0.1$ .

Figure 26.

Turbulent kinetic energy budget at the top of EEEL along Line B. The acronyms WT and NT stand for “with trees” and “no trees” respectively.

The same trend can seen in the dissipation of k. The production term is bigger for $z / z_{b} < 0.1$ without trees and k is bigger in that region as well when compared to the case with trees. Note that k only depends on the normal stresses. The x,y-plane Reynolds shear stress gives the main production term in the presence of vertical shear. This explains why there is an increase in k with trees for $z / z_{b} < 0.1$ in Figure 24(b).

Turbulence models

One of the uncertainties in CFD is the accuracy of turbulence models. For wind resource assessment in general, the standard $k$ - $ε$ turbulence model remains a popular choice, but as discussed by Mohamed and Wood (2015a) and the references therein, it can seriously overestimate k in built-up environments with many stagnation regions. There have been some recommendations of the use of other models for urban wind flow.

Wright and Easom (1999) recommended the renormalization group (RNG) k-ε for building pressures while Blocken et al. (2012) suggested the realizable k-ε for assessing wind comfort and safety for pedestrians. Franke et al. (2011) proposed using Reynolds stress models (RSM) for the aforementioned applications. The next part of the study assesses the performance of the SST k-ω, baseline (BSL) RSM and the RNG k-ε turbulence models. The focus is the EEEL building. The wind power density for the three turbulence models taken 4 m above the roof of the EEEL building is shown in Figure 27.

Figure 27.

Wind power density per unit area distribution for different turbulence models. The wind is from the west. (a) SST k-ω. (b) $RNG$ k-ε. (c) BSL RSM.

Figure 28.

k distribution for different turbulence models. The simulations were simulated with wind coming from the west at the inlet. (a) SST k-ω. (b) RNG k-ε. (c) BSL RSM.

Figure 29.

U and k distribution for different turbulence models taken along Projection A. The simulations were simulated with wind coming from the west at the inlet. Freestream velocity $U_{e}$ = 5.21 m/s and the height of the building $z_{b}$ is 32 m. The origin is at the roof of the EEEL building. (a) Velocity profile. (b) k profile.

Although there are differences in the distributions of wind power density, all models suggest that the maximum wind resource is located above the west facade of the EEEL building. Further, the three models would suggest similar regions in which to measure the wind speed for confirmation. Table 2 shows the area integral for $P_{w}$ for different turbulence models taken from the plane shown in Figure 11. There is less agreement in turbulence with the RNG k-ε turbulence models on the north-west side of the building.

Table 2.

Area integral of $P_{w}$ comparisons between different turbulence models 4 m above the roof of EEEL based on the cut plane shown in Figure 11.

Turbulence model	$P_{w}$ (W)
*SST k-ω	213, 869
*RNG k-ε	299, 143
*BSL RSM	270, 418

The $U / U_{e}$ profiles along Projection A (see Figure 12) show that the SST k-ω, RNG k-ε, and RSM models make similar predictions for wind speed barring a small extended re-circulation zone near the wall predicted by the RSM model. The SST shows very little overshoot in k near the roof compared to the other two models with the RNG k-ε predicting high levels of turbulence compared to the other two models.

Wind turbine and PV module siting

Microgeneration technologies that benefit from wind resource assessment include not only wind turbines but also PV modules, the latter because of sensitivity to wind loads due to their light weight and large frontal area. In short, PV requires low wind speed while wind turbines need high wind speed.

The above analyses suggest that the potential wind turbine siting zone should be located along the west side of the EEEL building. Due to the inherent nature of accelerated and hence skewed flow near the edge of the west roof of the EEEL building, small turbines with a characteristic size of $D_{t} / z_{b} >$ O(0.1) (Mertens, 2006), where $D_{t}$ is the rotor diameter, should be used.

To obtain maximum power, PV modules are normally inclined at an angle slightly less than the latitude of the site. Although they are susceptible to wind loads, there are not many guidelines or codes to assist designers with this aspect. Most building codes, such as the Canadian National Building Code, NBC (2010), require dynamic pressure in order to determine the extreme wind load. Figure 30 shows the dynamic pressure $q_{h}$ distribution over the Schulich School of Engineering 2 m above the roof of Block D for winds coming in from the west compared to when the wind is coming from the other two directions. This could be due to the roof of Block D being sheltered by the adjacent building to the west. From the three cases, the maximum $q_{h}$ above the roof of Block D is 18.37 Pa.

Figure 30.

Contours of dynamic pressure, $q_{h}$ . Plane taken 2 m from roof of the Schulich Engineering Building Block D. Units are in Pa. The EEEL building is below the axes graphic. (a) Wind from the west. (b) Wind from north-west. (c) Wind from south-west.

On the 27 November 2011, Calgary was hit by “hurricane-strength” winds with wind speeds recorded at 149 km/h. Such high wind speeds can destroy PV modules. The next part of the study is focused on extreme wind speeds. This case used the 149 km/h wind speed as the inlet condition. The SST simulations followed the previous studies of westerly winds with $u_{τ}$ increased to 2.32 m/s to model the quoted value. The results from the extreme wind simulations are depicted in Figure 31.

Figure 31.

Contours of dynamic pressure, $q_{h}$ , for an extreme wind speed case. Plane taken 2 m from roof of the Schulich Engineering Building Block D. Units are in Pa.

Although the roof of Block D is sheltered by the Calgary Centre for Information Technology (CCIT) building in the middle-left of Figure 31 there is a significant increase in $q_{h}$ . The magnification is almost 31 times that due to a mean wind speed of 5.21 m/s at 50 m height.

To see how the results change relative to the change in dynamic pressure, we normalized the dynamic pressure for both cases of extreme wind speed and annual wind speed with the $q_{h}$ based on respective velocities taken at 50 m at the inlet, that is, 41.4 m/s for the extreme wind speed case and 5.21 m/s for the annual wind speed case. As shown in Figure 32, there are more regions of low normalized $q_{h}$ (range between 0 and 0.1) for the extreme wind speed case over Block D than for the annual wind speed case. However, the bulk flow pattern is similar for both cases which highlights the small Reynolds number effect on the flow.

Figure 32.

Contours of normalized dynamic pressure, $q_{h}$ , for an extreme wind speed case and annual wind speed case. Plane taken 2 m from roof of the Schulich Engineering Building Block D. (a) Annual wind speed case. (b) Extreme wind speed case.

Conclusions

An analysis was done for the siting of wind turbines on the roof of the EEEL building of the University of Calgary campus via CFD. The process included several considerations including the effects of terrain, turbulence models, trees, and wind directions. The exclusion of terrain led to significantly higher estimates for wind power density and implies that terrain has to be included in computational wind resource assessment for urban settings. Different turbulence models tended to predict differences in wind power density especially above the EEEL building but they apparently give a fair indication of what range can be expected. Trees also caused deviations in wind speed and turbulence above the roof of the 32 m high EEEL building. It is recommended that any computational assessment of the wind resource include them. All results were carefully considered before choosing the potential location which eventually narrowed down to the edge of the EEEL building on its west side. This study also looked at the wind loads on a possible PV modules installation on the roof of Block D, Schulich School of Engineering, and considered extreme wind conditions. Dynamic pressure can be used to calculate the extreme wind loads on the modules and it was found that under extreme wind conditions the magnification was 31 times over the loads due to the annual average wind speed.

Footnotes

Declaration of conflicting interests

The authors declare that there were no conflicts of interest in this study.

Funding

This research was supported by the Natural Science and Engineering Research Council and the ENMAX Corporation under the Industrial Research Chairs programme.

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