American Institute of Aeronautics and Astronautics1

Effect of Roof Slope on a Building-Mounted Wind Turbine

William D. Lubitz1 and Rohan Hakimi2

University of Guelph, School of Engineering. Guelph, ON, Canada. N1G 2W1.

Knowledge of the wind climate above peaked roofs is necessary to determine whetherinstalling small wind turbines on low-rise peaked roof buildings is feasible. There is littlepublished data available documenting how wind speeds above peaked roofs vary relative to areference open field condition. The wind characteristics at a representative peaked roof barnin southern Ontario, Canada were investigated to help address this need. The barn wassimulated using a boundary layer wind tunnel, and the commercial code Fluent. Fieldmeasurements at the barn were collected using sonic anemometers and compared to thesimulation results. Wind speed amplification was confined to a region immediately above theroof and was relatively low for wind energy purposes. It was found that with Fluent,renormalization group (RNG) k-epsilon turbulence closure predicted winds above the roofpeak better than standard k-epsilon. Simulation of buildings with a range of roof slopesfound that moderately sloped roofs appear to offer a better combination of wind speedamplification and low turbulence levels at the roof peak, compared to either flat or verysteep roofs. Considering only wind-related factors, the placing of very small micro-windturbines on roof peaks may be warranted. However, if sufficient space is available, placingsmall turbines on a tower, rather than on the peaked roof of a low-rise building, will usuallybe the best approach.

NomenclatureA = swept areaCs, Cµ = constantsh = height above surfaceH = reference height (7.32 m)k = turbulent kinetic energyP = powerU = wind speedU* = friction velocityx = horizontal distancez = height above groundzo = roughness heightε = turbulence dissipationκ = von Karmen’s constant (0.41)ρ = air density

I. Introductiontower tall enough to position in a wind turbine in higher-speed winds is one of the largest costs of an installedwind turbine. While wind turbines are not usually placed on top of houses because of concerns about noise,

safety and vibrations from the turbine being transmitted through the structure, these concerns are reduced for othernon-residential structures (Grauthoff, 1991). One possible way to reduce the total costs of a small wind turbineinstallation is by placing the turbine on the top of an existing tall structure, such as a barn or storage building (Mannet al., 2006). Additionally, air moving over a structure is often accelerated, producing higher wind speeds over someparts of a building roof than would be found at a similar height in an open area. A wind turbine located in a

1 Assistant Professor, University of Guelph, School of Engineering, Guelph, ON, Canada. N1G 2W1. AIAAMember.2 Undergraduate Researcher, University of Guelph, School of Engineering, Guelph, ON, Canada. N1G 2W1.

A

46th AIAA Aerospace Sciences Meeting and Exhibit7 - 10 January 2008, Reno, Nevada

AIAA 2008-1325

Copyright © 2008 by W. Lubitz and R. Hakimi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

American Institute of Aeronautics and Astronautics2

favorable location on a building roof could potentially produce more power than a comparable tower-mountedturbine at the same height above ground.

The amount of additional power generated by a roof-mounted wind turbine depends strongly on the windcharacteristics above the building, which can be quite complex (Mertens, 2005). More research is needed tocharacterize the wind field above building roofs, particularly those aspects most relevant to wind turbineinstallations, such as mean wind speeds and turbulence intensities. Most previous studies in this area have beenlimited to buildings with flat roofs, or have involved a building primarily designed to integrate a large wind turbine(e.g. Campbell et al., 2001, Mertens, 2003). Little work has been done on buildings with peaked or curved roofs,which are much more common in rural areas than flat roofs. Relevant field measurements are particularly needed(Heath et al., 2007). This research is an initial study to address this need using a combination of field measurements,boundary layer wind tunnel (BLWT) experiments and computational fluid dynamics (CFD) simulations usingFluent. A primary goal is to validate the simulation techniques against actual field data, to allow greater confidencewhen simulating different building configurations.

II. MethodsThe wind field above a case study building was investigated using several different tools, to evaluate the

prediction accuracy of each of the tools. Simulations were performed using a BLWT, and the commercial CFD codeFluent. Results of the simulations were compared to field data collected using sonic anemometers.

A. SiteThe study building is a storage barn in a rural area of southwestern Ontario, Canada (43.30° N, 80.55° W), with

a rectangular footprint 18.34 meters by 24.43 meters, and height of 7.32 m. The roof is peaked with a slope of 1:3,and roofline oriented north-south. A second barn 7.62 m high, 30.48 m long and 15.24 m wide with a half-cylindercross-section and long axis oriented east-west is located 14.2 m east of the study building. There is a small shedadjacent to the southeast corner of the study building that is 2.60 meters tall at its peak and has a footprint of 4.80meters by 6.72 meters. A row of pine trees aligned east-west, with heights to 12 m, is located south of the studybuilding.

B. Field ExperimentsFor the field experiments, a 4.1 cm diameter tubular vertical mast was placed at the roof peak midway along the

roofline. A 10.7 m guyed mast was placed 44.2 meters west of the west wall of the barn in a flat open alfalfa field.Two Kaijo DA-600 sonic anemometers were placed on booms projecting from the west side of the mast at 4.9 m and10.1 m to measure the approaching wind profile, and a third DA-600 was placed on the roof mast at a height of 1.9m above the roof peak, on a boom projecting 0.75 m west of the mast. DA-600 anemometers are extremelysensitive, but only accurately measure winds over a direction range of 60°. All of the anemometers were orientedhorizontally and pointed west (into the prevailing wind direction), allowing winds from 240° to 300° to beaccurately recorded.

Prior to the collection of field data, all of the sonic anemometers were calibrated in the University of GuelphBLWT. They were also placed in an open field west of the study building at the same location as the guyed mast forone week at a height of 1.0 meter. It was found that the anemometers agreed within 5% over a wind speed range of 2– 8 m/s.

A total of 347.5 hours of field data were collected between June 6th and June 24th, 2007. Unfortunately, theanemometer at 10.1 m did not function properly, and therefore data from this level was not used in the study. Allthree anemometers were connected via a data acquisition board to a desktop PC located inside the barn. Threecomponents of wind speed plus temperature were simultaneously sampled at 20 Hz. This raw data was then post-

Figure 1. Site viewed from the north side, facing due south. Prevailing west winds blow from right to left in thisimage. Yellow peaked-roof barn is study building, guyed anemometer mast projects above trees in backgroundon right.

American Institute of Aeronautics and Astronautics3

processed to 10 minute averages using Matlab scripts.The DA-600 sonic anemometers are only accurate when the wind direction is within 30 degrees of the long axis

of the instrument. The 10 minute average readings were filtered based on instantaneous wind directions to removethose with excess readings outside this range. Ten minute averages where at least 2/3 of the instantaneous readingswere within ±30° range were retained. A total of 100 ten minute averages met this criteria and were used foranalysis.

C. Wind Tunnel TestsA 1:96 scale model of the study area was tested in the University of Guelph Boundary Layer Wind Tunnel

(BLWT) to determine the variability of wind speed and turbulence above the peaked roof. The area modeledincluded all of the buildings. The trees south of the study barn were modelled using small pieces of foam affixed tovertically oriented stiff wires. The model was centered in the wind tunnel test section.

The BLWT is an open-return suction tunnel with a converging intake, flow straighteners and an 8 meter flowdevelopment section followed by a 2 meter test section. The test section is 1.2 m wide, 1.2 m tall, and was outfittedwith a traversing system and a TSI IFA-300 hotwire anemometry system for these experiments. The hotwire wascalibrated using a sensitive manometer and pitot tube. Output from the hotwire system was recorded at 1000 Hzusing a National Instruments USB-6210 data acquisition system and LabView 8.0 software. A configuration ofspires and roughness elements produced a 30 cm deep boundary layer with a mean velocity shear exponent of α =0.17. Wind speed and turbulence intensity profiles were taken along an east-west transect bisecting the barn, whilesimulating a west wind.

D. Fluent SimulationsBoth two- and three-dimensional, turbulent, steady state simulations were carried out using Fluent 6.2.16.

(Fluent Inc., Lebanon, NH, USA), a general purpose CFD solver with a variety of meshing and solver options. Forthis study, non-uniform tetrahedral/triangular meshing was used with several turbulence closures, including standardk-epsilon and renormalization group (RNG) k-epsilon.

The domain used for three-dimensional simulations was made up of two volumes, an inner cylindrical domaincentered on the central point of the barn and an outer rectangular domain which fits around the inner section (Fig. 2).Domain dimensions are 107 m high and 605 m long. The three-dimensional domain is 420 m wide. The domain wasdivided into several regions shown in Fig. 2. Sizing functions were used to vary the density of the mesh within eachdomain region. A nominal cell size of 0.5 m was used adjacent to the study barn, with 0.34 m cells adjacent to theeaves (Fig. 2). Cell size increases with height and lateral distance from the barn to a maximum cell size of 15 m.

The top (and for the three-dimensional case, lateral) sides of the domain were set as “symmetry” boundaries,defining parallel flow along these surfaces, an approach that has been used successfully in wind simulations (Franke,2006). The outlet was set as a “pressure outlet.” The ground and barn surfaces were set as “wall” boundaries. In thethree-dimensional simulation, the trees were modeled as a face with a “porous jump”.

Inlets were specified as “velocity inlets.” Profiles of mean velocity U, turbulent kinetic energy k and turbulencedissipation ε were imposed on the inlet using a User Defined Function (UDF). A compromise was necessary whensetting the surface roughness. Based on the agricultural fields surrounding the site, surface roughness was estimatedto be zo = 0.04 m (ESDU, 1979). In Fluent, surface roughness is modeled as sand grain roughness Ks (Blocken et al.,2007),

ss C

zK 0793.9

=(1)

which gives Ks = 0.78 m with constant Cs = 0.09 . However, when using wall functions in Fluent, the firstcomputational node, which is at half the height of the cell, should be farther from the wall than Ks (Franke, 2006).Since the cell size at ground level adjacent to the study barn is 0.5 m, a roughness value of 0.24 m was used in thesimulations to meet this criteria.

A correct simulation of the atmospheric boundary requires longitudinal equilibrium throughout the volume,which can be difficult to achieve (Hoxey and Richards, 1993). Initially, profiles for mean velocity U, turbulentkinetic energy k and turbulence dissipation ε from Richards and Hoxey (1993) were used:

American Institute of Aeronautics and Astronautics4

)ln(*

o

o

z

zzUU

+=κ

(2)

µC

Uk

2*=(3)

)(

*

0

3

zz

U

+=κ

ε(4)

where κ is von Karmen’s constant (taken as 0.41), U* = 0.364 m/s and zo = 0.04 m, corresponding to a powerlaw velocity profile with shear exponent of α = 0.17. When this was implemented in an empty domain meshedequivalently to that used in the simulations, it was observed that k at the outlet was reduced by over 50% relative tothe inlet values. Other investigators have also reported this occurrence (Hargreaves and Wright, 2007). An iterativeprocess was used to “evolve” an equilibrium boundary layer, in which profiles of k and ε from the outlet werereapplied at the inlet (while reapplying Eqn. 2 for the velocity profile). After several iterations, the profiles of U, kand ε were judged to have reached equilibrium throughout the domain. For the simulations, the inlet profiles usedwere

1323

354759611

10748.210556.710030.1

10345.310006.510603.310007.1−−−

−−−−

++−

+−+×−=xzxzx

zxzxzxzk

(5)

y

sm 231014485626.0=ε(6)

and Eqn. (2). These inlet profiles were used for both two- and three-dimensional simulations.

III. Study Barn ResultsAll wind speeds were non-dimensionalized by a reference mean wind speed measured at a height of z/H = 2,

where H = 7.32 m is the height of the study barn at the roof peak. Normalizing velocities for the BLWT and Fluentsimulations were taken from wind speed profiles at the location of the barn in an empty domain or test section.

A. Simulation ResultsThe prevailing wind direction at the site is from the west, which corresponds to winds that are perpendicular to

the roofline on the study barn. Figure 3 shows mean wind speed profiles predicted by the BLWT and Fluent. The

Figure 2. Domain used for three-dimensional Fluent simulations (left) and mesh density near buildings (right).

American Institute of Aeronautics and Astronautics5

turbulence closure models used in Fluent were standard k-ε, realizable k-ε, renormalization group (RNG) k-ε andthe Reynolds Stress Model. Default values were used for the parameters in each model.

Some aspects of the flow were reproduced fairly closely by all of the simulations. All simulations predict thatwind speed amplification is greatest at the roof peak. A significant increase in wind speeds occurs in a limited regionabove the roof that is only a few meters deep (Fig. 3). On the windward roof surface, mean velocities close to thesurface (z/H < 0.5) are increased at a given height compared to the upwind “(1)” profile. The simulations convergeto very similar wind speed profiles as height increases above approximately z/H = 2 (or lower for the upwindprofiles). On the upwind side of the barn, there is some difference between the BLWT and the Fluent simulationsadjacent to the ground, especially at the most upwind profile. This is believed to be due to reduced resolution of theFluent model in this location relative to the wind tunnel measurements.

The variation between the simulations is greatest in the region very close to the roof peak, and in the wake regionabove the leeward roof surface. Wind tunnel results show increasing velocities up the windward side of the roof,separation at the roof peak, and a large wake region above the leeward roof. Of the Fluent simulations, only theRNG k-ε closure predicted wind profiles above the leeward roof qualitatively similar to the wind tunnelmeasurements. The results from the Fluent simulations using standard and realizable k-ε models did not predictseparation at the peak, and predict very shallow wake regions above the leeward roof. This results in anoverestimation of wind speed at the peak. The Reynolds stress model maintains a similar profile after the peakthough the velocity increases more gradually above the roof.

It is well known that the standard k-ε model does not reproduce the separation and the reverse flow downstreamof a sharp edge such as a building roof peak (Mochida et al, 2006). The results shown here support this finding andindicate that for this geometry and class of problem, the RNG k-ε model gives better results than the realizable orstandard k-ε models.

0

0.5

1

1.5

2

2.5

3

Non-Dimensionalized Velocity

Hei

ght

[z/H

]

Wind Tunnel

Standard K-Epsilon

RNG K-Epsilon

Realizable K-Epsilon

Reynolds Stress Model

Figure 3. West wind non-dimensionalized mean velocity profiles along an east-west transect through the centreof the study barn. One horizontal grid spacing represents a non dimensionalized velocity of 2. Data presented isfrom Fluent simulations using specified turbulence closure models and the wind tunnel. Left side of figure isupwind. Profile locations from left are (1) x/H = 2.52, (2) x/H= 1.89, (3) windward edge of roof at x/H=1.26, (4)x/H=0.63, (5) roof peak at x/H=0, (6) x/H=-0.63, (7) leeward edge of roof at x/H=-1.26.

American Institute of Aeronautics and Astronautics6

B. Field DataField data was collected for the case of a west wind.

The velocity and turbulence intensity measurementsfrom the rooftop anemometer (at 1.92 m or h/H = 0.26above the roof peak) can be compared to both theanemometer at the tower (4.9 m or z/H = 0.67 aboveground) and to predictions from the simulations.Investigating the velocity ratio observed between theanemometers, it is apparent that the wind speeds at theroof anemometer were greater than those on the tower(Fig. 4). Based on this data, it was judged that the meanvelocity 1.92 m above the roof can be represented as115% of the mean velocity at 4.9 m on the tower at windspeeds of interest for wind energy purposes (i.e., greaterthan 3 m/s). Turbulence intensities, although showinggreater scatter than the velocity data, are about 15%lower at the anemometer above the barn roof (Fig. 5).Note that in Fig. 5, the wind tunnel and Fluent RNGresults were identical and are overlapping.

The simulations reproduced the field and wind tunneldata with varying degrees of accuracy that dependedmostly on the simulation’s ability to reproduce the roofpeak separation point and the wake region above theleeward roof surface. When modeling the barn in Fluent,the k-ε RNG closure more accurately reproduced thefield measurements than standard k-ε, for both velocityand turbulence. The standard k-ε model did not capturethe separation occurring at the roofline, resulting inpredictions of increased flow speed-up across the peak.

It was estimated that the alfalfa fields surroundingthe barn would result in a shear exponent α of 0.17 androughness height zo of 4 cm. The wind tunnel spires androughness elements were adjusted to reproduce thiscondition, and it was used as a boundary condition forthe Fluent simulations. Unfortunately, the fieldmeasurements were inadequate for calculating theseparameters directly from the anemometer data, and itwas impossible to verify if these predictions wererepresentative of the field site. It is possible thatdifferences between the wind tunnel and field data ratiosare due to a different wind profile being simulated thanwas actually occurring in the field.

IV. Roof-Mounted Wind TurbinesThe difference in power output between a micro-

wind turbine placed at the roof peak, versus the sameturbine in an open area on a conventional tower wasinvestigated. A Southwest Windpower AIR-X turbinehaving rotor diameter of 1.14 m was chosen as arepresentative micro-wind turbine. The AIR-X powercurve (Fig. 6) utilized was measured at the NationalRenewable Energy Laboratory (NREL) (Van Dam et al.,2003). The wind power density per swept area (P/A) foreach roof peak was also calculated using

0

1

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3

4

5

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7

8

9

0 1 2 3 4 5 6 7 8 9

Wind Speed at 1.92 m Above Roof Peak [m/s]

Win

dS

peed

at4.

9m

Tow

erLe

vel[

m/s

]

Field Data

Wind Tunnel

Fluent Standard K-Epsilon

Fluent RNG K-Epsilon

1:1

Figure 4. Comparison of wind speeds at 1.92 m aboveroof peak and 4.9 m above ground on upwind mast.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Turbulence Intensity at 1.92 m Above Roof Peak

Tur

bule

nce

Inte

nsity

at4.

9m

Tow

erLe

ve

Field Data

Wind Tunnel

FLUENT Standard K-Epsilon

FLUENT RNG K-Epsilon

1:1

Figure 5. Comparison of turbulence intensity at 1.92m above roof peak and 4.9 m above ground on upwindmast.

-20

0

20

40

60

80

100

120

140

160

0 5 10 15 20

Wind Speed [m/s]

Po

wer

[W]

Figure 6. Power curve of Southwest AIR-X wind turbine.(Van Dam et al., 2003)

American Institute of Aeronautics and Astronautics7

3

2

1U

A

P ρ= (7)

where U is the wind speed at the location being examined. A power law velocity profile at the tower with shearexponent α = 0.17, Rayleigh-distributed wind speed frequency with a mean wind speed of 7 m/s at 10 m aboveground and an air density of ρ = 1.225 kg/m3 were assumed. For the west wind case (wind direction perpendicular toroofline), the consensus value of 1.15 for the ratio of mean wind speeds 1.92 m above the barn roof and 4.9 m aboveground on the tower was used to extrapolate wind velocities from the tower to the roof peak.

The AIR-X power output and wind power density for both the tower and roof peak are given in Table 1. Thewind was assumed to blow only from a direction perpendicular to the roofline, with no upwind obstructions. Thiscorresponds to a west wind for the study barn. Since wind direction affects wind speeds above the roof, and winddirections aligned with the roofline are associated with reduced wind velocity, this analysis corresponds to a “bestcase” prediction of the wind energy potential of the roof. Additionally, winds parallel to the roofline would beexpected to produce higher turbulence intensities than would be experienced on a tower.

Placing a wind turbine at 1.92 m above the roof peak (i.e., 8.24 m above ground) produces the same power asplacing it on a tower in an open area at a height of 11.2 m. In this case, the amplification of the wind above the roofis significant, and in some cases there may be an advantage to placing the turbine above the roof. It is important toremember that this is a best case scenario in which the wind direction is always perpendicular to the roofline. Inreality, most sites experience a range of wind directions, and it would be expected that performance would bereduced with winds from other directions.

The relatively low power output of the turbine should also be noted. It has been shown that significant windspeed amplification occurs only in a region a few meters above the roof of a low-rise building (Fig. 3). However,ambient wind speeds are typically low at these heights, and standard practice is to place even small wind turbines ontowers of 15 – 30 m. At these heights (z/H > 2) there is little significant difference in the wind climate between astand-alone tower and a tower projecting upward from a peaked-roof building. Therefore, only micro-turbines withrotor diameters of less than a few meters could be expected to be able take significant advantage of wind speedamplification caused by a moderately-sloped peaked roof like the one on the case study building.

Table 1. Predicted mean wind speed, AIR-X wind turbine power output and wind power density at differentlocations for a Rayleigh-distributed 7 m/s mean wind at 10 m above ground.

Location Height AboveGround (m)

Wind Speed(m/s)

AIR-X Power(W)

Wind PowerDensity (W/m2)

Roof Peak 8.24 7.13 53 424Tower 4.9 6.20 45 279Tower 8.24 6.77 50 363Tower 11.2 7.13 53 424

V. Effect of Roof Slope on Above-Roof WindsDuring the course of this study, it became apparent that

roof slope had the potential to have a significant impact onwind speeds and wind energy potential. An initial screeningstudy was therefore conducted to see if this was the case. Forexpediency, two-dimensional simulations were used. Fivedifferent building cross-sections were simulated using Fluentto determine the effect of roof slope on wind speed andturbulence above a low-rise building roof. RNG turbulenceclosure was utilized. As shown in Fig. 7, building geometriestested varied from flat roof (“Building A”) to vertical wall(“Building E”).

Figures 8 and 9 show profiles of mean wind speed andturbulence intensity above the roof peak (x = 0) for each ofthe buildings shown in Fig. 7. It is apparent that the roofgeometry does indeed have a marked impact on the windclimate immediately above the peak.

0

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6

7

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10

-10 -5 0 5 10

X (m)

Y(m

)

A - Slope = 0:1 (0°)

B - Slope = 1:6 (9°)

C - Slope = 1:3 (18°)

D - Slope = 1:1 (45°)

E - Slope = 1:0 (90°)

Figure 7. Cross-sections of simulated buildings.

American Institute of Aeronautics and Astronautics8

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Non-Dimensionalized Wind Speed

z/H

No BuildingBuilding ABuilding BBuilding CBuilding DBuilding EBuilding C - 3D SimulationNo Building - 3D Simulation

Figure 8. Mean wind speed profiles above roof peaks.

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.05 0.1 0.15 0.2 0.25

Turbulence Intensity

z/H

No BuildingBuilding ABuilding BBuilding CBuilding DBuilding E

Figure 9. Turbulence intensity profiles above roof peaks. Building A turbulence intensity maximum is 3.2 atz/H = 1.17, due to recirculation zone in this region.

American Institute of Aeronautics and Astronautics9

Flat-roofed buildings like Building A have a zone of very low wind speeds and very high turbulence directlyabove the roof. Flow separation at the upwind edge of the Building A roof produced a recirculation zone withreversed flow adjacent to the roof surface. These results support the suggestion of Mertens (2005) to ensure that awind turbine placed on a flat roof is located high enough to avoid this turbulent, low speed region. Interestingly, thewind speed profile above Building B, which has a roof slope of 1:6 (9°), shows that even a modest roof slopeeliminates most of the wake region that forms downwind of the upwind edge, and therefore changes the flow regimeadjacent to the roof surface markedly. A roof slope of 1:3 (Building C) ensures high-speed flow adjacent to theupwind roof surface from edge to peak, while reducing turbulence levels.

Simulations of the very steep roofs (45° or greater, Buildings D and E) actually show somewhat reduced windspeeds just above the peak, and higher turbulence levels, relative to the more moderately sloped roofs of Buildings Band C. The wake region downwind of the roof peak is larger and extends higher than over the more moderatelysloped roofs, resulting in somewhat reduced maximum wind speeds above the peak.

In terms of mean wind speed and turbulence, a moderate roof slope (i.e., Building C) appears to offer a goodcompromise between flat and very steep roofs. Wind speed amplification due to this moderate roof slope is almostas great as for a flat roof, while turbulence levels are reduced relative to both the flat roof and very steep roof cases.

Since these were two-dimensional simulations, only the relative differences between the simulations have beendiscussed. The magnitudes of the wind speed amplification seen in the two-dimensional simulations is much higherthan that seen in the three-dimensional simulations. The roof peak mean wind speed profile from the three-dimensional simulation (equivalent to Building C) has been included in Figure 8 to illustrate the impact of includingthe third dimension. Not surprisingly, wind speed amplification (relative to the “no building” case) extends upwardsto a far greater extent in the two-dimensional simulations. Wakes also take significantly longer to recover in the two-dimensional simulations. Because of these considerations, simulations should be performed in three dimensions forquantitative assessments.

VI. ConclusionThe wind flow over a peaked roof barn was simulated in a boundary layer wind tunnel and numerically using the

commercial code Fluent. Of the turbulence closures used in Fluent, the RNG k-ε model was the most consistent withthe wind tunnel simulations and field data, and appears to offer the best accuracy for simulating wind conditionsdirectly above a peaked roof building.

Wind tunnel and CFD simulations showed that the mean wind speed above the roof was increased in a regionseveral meters deep for most wind directions. When the simulation results were applied to predicting wind energyproduction potential above the roof, it was found that a turbine mounted just above the roof would produce moreenergy than one at the same height on a conventional tower. However, if the wind blows regularly parallel to theroofline or from directions with upwind obstructions, the energy production of a roof-mounted wind turbine willlikely be significantly impaired. Additional work is warranted to quantify the effect of wind direction at the roofpeak.

Performing three-dimensional simulations above a variety of peaked roofs would provide further insight into theeffect of roof slope on the near-roof wind climate, and allow quantification of wind energy production potential.This study would also benefit from the collection of field data over a longer term, at several sites, to moredefinitively validate the accuracy of the utilized simulation methods. The accuracy of simulation results for this classof flows is very sensitive to the turbulence closure employed. Testing of additional turbulence closure models foraccuracy at roof-peak locations, such as higher order schemes and large eddy simulation, would yield additionalinsights into model performance, and possibly result in improved accuracy over the results given here.

Simulations of roofs with varying slopes suggests that moderately sloped (e.g. about 1:3) peaked roofs provide agood combination of wind speed amplification without production of excess turbulence, relative to conditions on aconventional tower in an open area. Turbines above flat roofs must be elevated above the wake region generated byflow over the upwind edge of the building. Compared to roofs with moderate slopes, very steep roofs producestrongly non-horizontal flow and somewhat increased turbulence levels while providing little extra wind speedamplification above the ridge peak.

AcknowledgmentsThe authors wish to thank Profs. Jon Warland and Bill Nickling at the University of Guelph for assistance

meeting the equipment needs for this study. Several staff at RWDI Inc. contributed time and technical advice,including Michael Roth, Darryl Cann, Meiring Beyers and Hanqing Wu. Mr. Hakimi was partially supported by aUniversity of Guelph Undergraduate Research Assistantship.

American Institute of Aeronautics and Astronautics10

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