Figure 1: Wind Distributions and Direction
Wind Rose in Figure 1 depicts the annual wind directions
indicating the predomi...
Figure 3: Y-axis wake simulation from west wind
Figure 4 depicts a 3-D top view of the wake simulation from
the west wind;...
Figure 6: Power Curve of 50 kW Turbine
The energy yield estimations were based on the monthly
average wind data which was ...
same appears as technically unfeasible at the
present stage using the existing technology.
• However further detailed stud...
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Laminar Flow And Turbulence Modeling For Domestic Scale Wind Turbine Siting


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Laminar Flow And Turbulence Modeling For Domestic Scale Wind Turbine Siting

  1. 1. LAMINAR FLOW AND TURBULENCE MODELING TO TRACE WAKE PATTERN AROUND BUILDING CLUSTERS FOR DOMESTIC SCALE WIND TURBINE SITING Govindarajan A Chittaranjan, Member IEEE Abstract— Some of the primary concerns in integrating domestic scale renewable energy systems in city buildings is siting optimization and size scaling of domestic wind turbine models on building roof tops discounting the zone of wake influence. This is a Case study scenario of a regional wind resources distributions and predominant wind directions computation for CFD modeling to identify laminar and turbulent flow regions around building clusters which is crucial in tracing and discounting the wake and non potential zones which are not suitable for wind turbine installations. This study identifies potential locations for installing renewable energy systems for optimized energy conversion in future studies. This study also briefly summarizes the energy yield of a 50 kW wind turbine and evaluates the energy loss if the turbine’s real availability decreased due to improper turbine site location. Keywords—CFD simulation, WasP, Reynold’s Number, Strouhal’s Number, vortice shedding. I. INTRODUCTION This research is aimed at simulating the wind flow to trace the wake around tall and medium sized building clusters of a small section in a commercial zone of a busy downtown in order to study the wake size and less energy potential zones, the aim is to propose at a latter stage an optimized wind turbine layout on the roof tops to capture and convert maximum energy at domestic scale. The CFD1 simulation software used is Yamada Soft’s A2C. Due to limitations2 in the evaluation version of this software certain modifications and assumptions were made to guide the study in a potentially right direction, hence accuracy of the computations should not be considered at this stage. This study is aimed at outlining the basic methodology and the process flow that is required for such a project. The sample size is 41 deg N latitude to 42 degree N latitude and 72 deg W to 73 deg W longitude, with topographic region data of New York State to simulate near reality. The samples are a 1 Computational Fluid Dynamics 2 GIS module not available for Topography map creation Cell size cannot be altered, hence samples cannot be placed next to each other in less than 2 km Different simulations [at different wind speeds, directions, heights cannot be saved in the same project Data Export is not allowed. 50 meter to 20 meter tall building clustered together at a major intersection. The predominant wind is primarily from the west direction. Consider this as a major city in North America and the most reliable source of wind data is recorded at the regions International airport. The study uses an actual 2008 wind data from such a source location for the analysis. Annual mean wind speed recorded at 32 m elevation on a 10 m met tower is estimated at 3.8 m/s using the WasP3 software. The region under study is in the northern hemisphere with a false Northing of 0, and a roughness class of 3 meters at an elevation of 39 meters. The coriolis4 force of the region is 45.9 N with favorable geostrophic5 wind. This is a good zone for wind energy harvest. However the region under study is part of a city location with an estimated wind shear of 0.3 and a high roughness class of 3-56 . The mean annual wind speeds of the sample region which falls in UTM 18 is 3.5 m/s to 4.5 m/s range and hence the measured data was extrapolated horizontally to suit the sample simulation. II. METHODOLOGY The area for simulation is selected using Google Earth and exported to A2C simulation software for mapping the wind flow over the terrain. A wind shear exponent of 0.3 is used for estimating the wind shear at 50 meter height extrapolated from the 10m met data. WasP was used to estimate the mean annual wind speeds and the direction distributions. III. WIND DATA ANALYSIS One year data from the regions airport is obtained the same was verified and adjusted suitably for missing and erroneous wind data. WasP software was used to obtain the wind resource distribution and the mean wind speed [3.9 m/s] from the 10 m Meteorology data monitoring mast 3 WasP – Wind Atlas Analysis and Application Program 4 F = 2ω sin φ 5 Vg = pressure gradient / F x ρ 6 The roughness of the city is replaced by displacement height : H – Z0 / k= Z – Z0 Where, k is the surface drag coefficient.
  2. 2. Figure 1: Wind Distributions and Direction Wind Rose in Figure 1 depicts the annual wind directions indicating the predominant wind from the west with a scale factor of 4.4 m/s. The weibull distribution indicates a perfect fit of the wind speeds in 1 meter bins. Annual average wind speed of 4.4 m/s measured at 10 m Metrological Wind mast is vertically extrapolated to 50m hub heights by the power law equation V2 = V1 x [ Z2 /Z1]α = 4.4 x (50/10)0.3 = 7.1 m/s As we know the Gust factor is Peak wind / Mean wind speed, the maximum gust that this region can experience during the sample period at 50 m = 1.2 x 7.1 = 8.52 m/s [using gust factor = 1.2]. The design life period of wind turbines are 20 years hence assuming a return period of 50 years the probability of not exceeding 8.52 m/s in 20 year design life is almost 100%7 by the equation: 1-(1/R)L 8 The turbulence intensity9 by estimation is 0.43 indicating high turbulence around the clusters at 50m heights. Assuming the along wind direction is perpendicular to the buildings the length L for the 5 collective buildings are taken as 100 meters then the Reynolds Number at an annual average air density of 1.253 is estimated at 5.93 x 1010 10 at 7 This may not be true in actual practice 8 R is the return period and L is Design life period 9 TI = σ V / Vmean; σ V = 3.09; Vmean = 7.1 m/s 10 Indicates High Turbulence at a distance D from the leading edge 7.1 m/s at 50m height using Reynolds equation: Re = (ρ x V x L) / μ where ρ is the air density and μ is the air viscosity. This indicates an air turbulence of 1.6; μ = 1.5x10-5 kg/m s at annual average of 70 C As these buildings are located near a water front the surface drag forces are low indicating low wind shear from the predominant west winds. The Drag force11 on the leeward side are 1.6 x 10-34 Nm this is expected to fluctuate as Re > 2 x 105 Figure 2: Cluster view from South of south east IV. WIND MAP MODEL OF SIMULATION AREA The simulation area is bound by 41 deg N to 42 deg N and 72 deg W to 73 deg W where in the tall towers are of 50 meter high and the medium sized buildings wider on x-axis are around 20 meter high, the sample region is of the size 100 m x 100 m12 . A wind flow simulation is limited to 6 m/s13 in 3 D around the buildings as the simulation outputs are limited by the evaluation software A2C. The vortices could be visualized but not so distinctly although earlier estimates and simulation indicates turbulence. Figure 3 depicts the westerly laminar wind flow simulation and a turbulent wake on the east justifying the earlier estimation of a turbulent Reynolds’s Number. The wake area indicated in blue in Figure 3 is quite large on the leeward side of the 20-metre high buildings, indicating high roughness class influence on wind shear. However at 50 m height the gradient wind drops from 6 m/s to 5 m/s indicating not so significantly wind shear at heights above 50 meters. 11 Drag Force FD = 0.5 x CD x ρ x A x V2 12 Hence the D = 100 for Reynolds’s Number estimation 13 due to the limitations of the A2C evaluation edition
  3. 3. Figure 3: Y-axis wake simulation from west wind Figure 4 depicts a 3-D top view of the wake simulation from the west wind; the marker indicates the spot of simulated wind speed indicated on the scale. Figure 4: 3-D View of wake simulation from west wind Due to the building cluster of an area of 10 km2 and adjacent terrain with smooth hills [Figure 5] in the near vicinity the angle of separation [Figure 4] is quite wide leading to windward wind speed of 4.5 m/s [greenish yellow] dropping down to less than 3 m/s [blue] in the wake region The Wake on the eastern side depicted in Figure 4 is approximately fourteen times the tallest building in the cluster with a length of almost similar size; this abnormal breath is due to the contributions of the terrain topography and is typical in a city centre. Figure 5: 3-D wake simulation from east wind Vortices are possibly in the zone tainted in aquamarine with a shedding frequency14 of 8 x 10-3 Hz at a shedding period15 of 117 seconds with a vortex separation16 of 833 m. This explains the reason as to why the simulation visualizes a wide wake zone and a wind turbine should not be installed here on a building sheltered by a still taller building on the windward side. The time varying drag force [FD (t)]17 acting on the leeward side of the building on the western side with a dimension of 50 x 50 m is 160 kilo Newton. V. ENERGY YIELD AND WIND TURBINES A 50 kW horizontal axis wind turbine18 is chosen to estimate the annual energy yield19 at 50 m hub height. This turbine was chosen on the basis of attaining commercial feasibility at domestic scale for roof top installations. Higher capacity turbines have been installed on city towers20 earlier hence this should not pose a major challenge at least in future structures. Table 1 depicts the annual energy yield of a 50 kW HAWT. Note the 18.64 % decrease from arbitrary 2% turbine non-availability, hence it is absolutely necessary to locate these turbines at an optimum location to increase the “real availabilities” as much as possible, this can be effectively done by avoiding the wake region and less potential zones 14 Ns = Strouhal Number S x U / D [ S = 0.12; D = 100 m] at 7.1 m/s wind speed 15 T = 1/NS 16 S = U x T 17 FD (t) = ½ x ρ x 18 Power Curve is depicted in Figure 6 19 AEP = [½ x ρ x A x V3 x 0.59] x 8760 x .98 [as this is a medium sized turbine a Betz limit of 0.59 is used] 20 World Trade Tower in Bahrain has 3 x 225 kW Norwind turbines installed at 3 levels
  4. 4. Figure 6: Power Curve of 50 kW Turbine The energy yield estimations were based on the monthly average wind data which was used in the AEP equation depicted the in foot note 19. An annual energy yield at 0.98% turbine real availability yields 33547 kWh caused by falling wind speeds due to marginal wake region siting Unfortunately no model exists to predict the energy yield with 100% accuracy. By experience it was observed that a marginal wake region could contribute to 1% to 20% fall in a wind speeds a more accurate study is required at a latter stage. Accurate predictions can be obtained only by measuring the wind data at each turbine’s proposed site. Table 1: Annual Energy Yield of 50 kW wind turbines at 50 meter hub-height Figure 7 is a month-wise graphically representation depicting the energy loss between the 100% availability and 98% availability. The graph infers there could be higher losses during winter when there are higher wind speeds due to increase in air density with fall in seasonal temperature, hence diligent micro siting of these turbines are crucial for commercial feasibility of a domestic scale system. 21 18.64% loss Figure 7: EY of a 50 kW Turbine at 100% Vs 98% Availability VI. INFERENCE AND CONSLUSION • The Extrapolated wind speeds at 50 m height is 7.1 m/s although the simulation was based on these data to the software limitation the simulation display was only up to a maximum of 6 m/s. • The estimation of high Reynolds number and drag forces indicating high turbulence at 50 meter levels and below. High vortex shedding frequency and time period and the wide wake confirms this. • The wake separation angle is wide typical of large building clusters and small hills on the terrain in such cases. • Annual Energy Yield for a 50 kW turbine at 50m high on top of the building was computed to emphasis on significant energy loss if installed without diligently considering the wake and turbulence. It should be noted that there is 18.64 % decrease in energy conversion from 2% turbine non-availability, this non availability could be due to improper siting of these wind turbines hence appropriate placement of the turbines [optimized lay out] is a crucial element. • Wind turbines at 60 meter and above are preferred for optimum energy but permitting issues might pose constraints for city building roof top installation • The wind loads on building structures may not be adequate for embedded turbine technology; the Gross Energy Yields at 50m 0 1000 2000 3000 4000 5000 6000 7000 8000 Jan- 08 Feb- 08 Mar- 08 Apr- 08 May- 08 Jun- 08 Jul-08 Aug- 08 Sep- 08 Oct- 08 Nov- 08 Dec- 08 Month MaximumEnergyYields[kWh] 0 1000 2000 3000 4000 5000 6000 7000 8000 EnergyYieldfor50kWTurbine [kWh] Maximun Energy at 50m at 100% turbine availability 50kW Turbine Description Energy Yield at 50m [kWh] Energy Yield of 50 kW Wind Turbine at 100% Real Availability 41235 Energy Yield of a 50 kW Wind Turbine at 98 % Turbine’s Real Availability 33547 21
  5. 5. same appears as technically unfeasible at the present stage using the existing technology. • However further detailed study on wind loads on the windward side, the drag coefficients on the leeward side, vortices and its shedding frequency in line with emerging turbine technologies including shrouded Windjets and transversal axis turbines are suggested along with embedded wind technologies. VII. REFERENCES BOOKS [1] Emil Simiu and Robert H. Scanlan on “Wind Effects on Structures – Fundamental and Applications to Design” [III rd Edition] [2] John Wabha, David Brinker, Mark Malouf and John Erichson on “New Standards for Broadcast Structures” ANSI/EIA/TIA-222-G by [3] Boundary Layer Theory - pdf exercise study [unidentified] WEB REFERENCES [1] d_601.html [2] [3] [4] [5] [6] [7] VIII. BIOGRAPHIES Govindarajan A Chittaranjan (M80255874) is from Chennai, India graduated from Middlesex University, UK is currently pursuing his PhD program in Canada. He is a wind energy analyst and operations & maintenance professional with detailed and long-term experience in the installation, operation and maintenance of wind farms and industrial instrumentation. He has been in industrial instrumentation sector and Unit Head for Operations and Maintenance of Wind Farms for over 21 years. He has a sound understanding of both the technical as well as commercial side of wind farm and industrial automation. He has adequate knowledge & experiences in current Mega- Watt class wind turbines, balance of plant equipments, Marketing & Sales. He has done detailed study on Wind project resource and risk assessments, wind farm losses, crane investigations and other special projects. .