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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 Laminar Flow And Turbulence Modeling For Domestic Scale Wind Turbine Siting Document Transcript

    • LAMINAR FLOW AND TURBULENCE MODELING TO TRACE WAKE PATTERN AROUND BUILDING CLUSTERS FOR DOMESTIC SCALE WIND TURBINE SITING Govindarajan A Chittaranjan, Member IEEEAbstract— Some of the primary concerns in integrating 50 meter to 20 meter tall building clustered together at adomestic scale renewable energy systems in city major intersection. The predominant wind is primarily frombuildings is siting optimization and size scaling of the west direction. Consider this as a major city in Northdomestic wind turbine models on building roof tops America and the most reliable source of wind data isdiscounting the zone of wake influence. recorded at the regions International airport. The study uses an actual 2008 wind data from such a source location for theThis is a Case study scenario of a regional wind analysis. Annual mean wind speed recorded at 32 mresources distributions and predominant wind directions elevation on a 10 m met tower is estimated at 3.8 m/s using the WasP3 software.computation for CFD modeling to identify laminar andturbulent flow regions around building clusters which is The region under study is in the northern hemisphere with acrucial in tracing and discounting the wake and non false Northing of 0, and a roughness class of 3 meters at anpotential zones which are not suitable for wind turbine elevation of 39 meters. The coriolis 4 force of the region isinstallations. This study identifies potential locations for 45.9 N with favorable geostrophic5 wind. This is a goodinstalling renewable energy systems for optimized zone for wind energy harvest.energy conversion in future studies. This study alsobriefly summarizes the energy yield of a 50 kW wind However the region under study is part of a city locationturbine and evaluates the energy loss if the turbine’s real with an estimated wind shear of 0.3 and a high roughnessavailability decreased due to improper turbine site class of 3-56. The mean annual wind speeds of the samplelocation. 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. Keywords—CFD simulation, WasP, Reynold’s Number,Strouhal’s Number, vortice shedding. II. METHODOLOGY The area for simulation is selected using Google Earth and I. INTRODUCTION exported to A2C simulation software for mapping the wind This research is aimed at simulating the wind flow to flow over the terrain. A wind shear exponent of 0.3 is usedtrace the wake around tall and medium sized building for estimating the wind shear at 50 meter height extrapolatedclusters of a small section in a commercial zone of a busy from the 10m met data. WasP was used to estimate the meandowntown in order to study the wake size and less energy annual wind speeds and the direction distributions.potential zones, the aim is to propose at a latter stage anoptimized wind turbine layout on the roof tops to capture andconvert maximum energy at domestic scale. The CFD1 III. WIND DATA ANALYSISsimulation software used is Yamada Soft’s A2C. Due tolimitations2 in the evaluation version of this software certain One year data from the regions airport is obtained the samemodifications and assumptions were made to guide the study was verified and adjusted suitably for missing andin a potentially right direction, hence accuracy of the erroneous wind data.computations should not be considered at this stage. Thisstudy is aimed at outlining the basic methodology and the WasP software was used to obtain the wind resourceprocess flow that is required for such a project. The sample distribution and the mean wind speed [3.9 m/s] from the 10size is 41 deg N latitude to 42 degree N latitude and 72 deg m Meteorology data monitoring mastW to 73 deg W longitude, with topographic region data ofNew York State to simulate near reality. The samples are a1 Computational Fluid Dynamics2 3 GIS module not available for Topography map creation WasP – Wind Atlas Analysis and Application Program Cell size cannot be altered, hence samples cannot be placed next to each 4 F = 2ω sin φother in less than 2 km 5 Vg = pressure gradient / F x ρ 6 Different simulations [at different wind speeds, directions, heights cannot The roughness of the city is replaced by displacement height :be saved in the same project H – Z0 / k = Z – Z0 Data Export is not allowed. Where, k is the surface drag coefficient.
    • 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 70C 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 1: Wind Distributions and DirectionWind Rose in Figure 1 depicts the annual wind directions Figure 2: Cluster view from South of south eastindicating the predominant wind from the west with a scalefactor of 4.4 m/s. The weibull distribution indicates aperfect fit of the wind speeds in 1 meter bins. IV. WIND MAP MODEL OF SIMULATION AREAAnnual average wind speed of 4.4 m/s measured at 10 m The simulation area is bound by 41 deg N to 42 deg N andMetrological Wind mast is vertically extrapolated to 50m 72 deg W to 73 deg W where in the tall towers are of 50hub heights by the power law equation meter high and the medium sized buildings wider on x-axis are around 20 meter high, the sample region is of the size V2 = V1 x [ Z2 /Z1]α = 4.4 x (50/10)0.3 = 7.1 m/s 100 m x 100 m12. A wind flow simulation is limited to 6 m/s 13 in 3 D aroundAs we know the Gust factor is Peak wind / Mean wind the buildings as the simulation outputs are limited by thespeed, the maximum gust that this region can experience evaluation software A2C. The vortices could be visualizedduring the sample period at 50 m = 1.2 x 7.1 = 8.52 m/s but not so distinctly although earlier estimates and[using gust factor = 1.2]. The design life period of wind simulation indicates turbulence. Figure 3 depicts theturbines are 20 years hence assuming a return period of 50 westerly laminar wind flow simulation and a turbulent wakeyears the probability of not exceeding 8.52 m/s in 20 year on the east justifying the earlier estimation of a turbulentdesign life is almost 100%7 by the equation: 1-(1/R)L 8 Reynolds’s Number.The turbulence intensity9 by estimation is 0.43 indicating The wake area indicated in blue in Figure 3 is quite large onhigh turbulence around the clusters at 50m heights. the leeward side of the 20-metre high buildings, indicatingAssuming the along wind direction is perpendicular to the high roughness class influence on wind shear. However atbuildings the length L for the 5 collective buildings are 50 m height the gradient wind drops from 6 m/s to 5 m/staken as 100 meters then the Reynolds Number at an annual indicating not so significantly wind shear at heights aboveaverage air density of 1.253 is estimated at 5.93 x 1010 10 at 50 meters.7 This may not be true in actual practice8 11 R is the return period and L is Design life period Drag Force FD = 0.5 x CD x ρ x A x V29 12 TI = σ V / Vmean; σ V = 3.09; Vmean = 7.1 m/s Hence the D = 100 for Reynolds’s Number estimation10 13 Indicates High Turbulence at a distance D from the leading edge due to the limitations of the A2C evaluation edition
    • Figure 3: Y-axis wake simulation from west wind Figure 5: 3-D wake simulation from east windFigure 4 depicts a 3-D top view of the wake simulation from Vortices are possibly in the zone tainted in aquamarine withthe west wind; the marker indicates the spot of simulated a shedding frequency14 of 8 x 10-3 Hz at a shedding period15wind speed indicated on the scale. 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 Figure 4: 3-D View of wake simulation from west wind much as possible, this can be effectively done by avoidingDue to the building cluster of an area of 10 km2 and the wake region and less potential zonesadjacent terrain with smooth hills [Figure 5] in the nearvicinity the angle of separation [Figure 4] is quite wideleading to windward wind speed of 4.5 m/s [greenish 14 Ns = Strouhal Number S x U / D [ S = 0.12; D = 100 m] at 7.1 m/s windyellow] dropping down to less than 3 m/s [blue] in the wake speedregion 15 T = 1/NS 16 S=UxT 17The Wake on the eastern side depicted in Figure 4 is FD (t) = ½ x ρ x 18approximately fourteen times the tallest building in the Power Curve is depicted in Figure 6cluster with a length of almost similar size; this abnormal 19 AEP = [½ x ρ x A x V 3 x 0.59] x 8760 x .98 [as this is a medium sized turbine a Betz limit of 0.59 is used]breath is due to the contributions of the terrain topography 20 World Trade Tower in Bahrain has 3 x 225 kW Norwind turbinesand is typical in a city centre. installed at 3 levels
    • Gros s E nerg y Yields at 50m 8000 8000 7000 7000 E n erg y Y ield for 50kW T u rb in e Ma x im um E ne rg y Y ie lds [k Wh] Maximun Energy at 6000 6000 50m at 100% turbine availability 5000 5000 [kWh ] 4000 4000 50kW Turbine 3000 3000 2000 2000 1000 1000 0 0 Jan- Feb- Mar- A pr- May- Jun- Jul-08 A ug- S ep- Oct- Nov- Dec- 08 08 08 08 08 08 08 08 08 08 08 Month Figure 6: Power Curve of 50 kW Turbine Figure 7: EY of a 50 kW Turbine at 100% Vs 98% AvailabilityThe energy yield estimations were based on the monthlyaverage wind data which was used in the AEP equationdepicted the in foot note 19. VI. INFERENCE AND CONSLUSIONAn annual energy yield at 0.98% turbine real availability • The Extrapolated wind speeds at 50 m height is 7.1yields 33547 kWh caused by falling wind speeds due tomarginal wake region siting m/s although the simulation was based on these data to the software limitation the simulationUnfortunately no model exists to predict the energy yield display was only up to a maximum of 6 m/s.with 100% accuracy. By experience it was observed that amarginal wake region could contribute to 1% to 20% fall in • The estimation of high Reynolds number and draga wind speeds a more accurate study is required at a latter forces indicating high turbulence at 50 meter levelsstage. Accurate predictions can be obtained only by and below. High vortex shedding frequency andmeasuring the wind data at each turbine’s proposed site. time period and the wide wake confirms this. Energy Yield at • The wake separation angle is wide typical of large Description 50m [kWh] building clusters and small hills on the terrain inEnergy Yield of 50 kW Wind Turbine at 100% Real such cases. 41235 AvailabilityEnergy Yield of a 50 kW Wind Turbine at 98 % • Annual Energy Yield for a 50 kW turbine at 50m 3354721Turbine’s Real Availability high on top of the building was computed to emphasis on significant energy loss if installedTable 1: Annual Energy Yield of 50 kW wind turbines at without diligently considering the wake and 50 meter hub-height turbulence. It should be noted that there is 18.64 % decrease in energy conversion from 2% turbine non-availability, this non availability could be dueFigure 7 is a month-wise graphically representation to improper siting of these wind turbines hencedepicting the energy loss between the 100% availability and appropriate placement of the turbines [optimized98% availability. The graph infers there could be higher lay out] is a crucial element.losses during winter when there are higher wind speeds dueto increase in air density with fall in seasonal temperature, • Wind turbines at 60 meter and above are preferredhence diligent micro siting of these turbines are crucial for for optimum energy but permitting issues mightcommercial feasibility of a domestic scale system. pose constraints for city building roof top installation • The wind loads on building structures may not be adequate for embedded turbine technology; the21 18.64% loss
    • same appears as technically unfeasible at the [6] www.ec.gc.ca present stage using the existing technology. [7] www.earth.google.com • However further detailed study on wind loads on the windward side, the drag coefficients on the leeward side, vortices and its shedding frequency VIII. BIOGRAPHIES in line with emerging turbine technologies including shrouded Windjets and transversal axis Govindarajan A Chittaranjan (M80255874) is from turbines are suggested along with embedded wind Chennai, India graduated from Middlesex University, UK is technologies. 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 VII. REFERENCES industrial instrumentation. He has been in industrialBOOKS 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[1] Emil Simiu and Robert H. Scanlan on “Wind Effects on Structures – Fundamental and Applications to Design” [III rd Edition] commercial side of wind farm and industrial automation. He[2] John Wabha, David Brinker, Mark Malouf and John Erichson on has adequate knowledge & experiences in current Mega- “New Standards for Broadcast Structures” ANSI/EIA/TIA-222-G by Watt class wind turbines, balance of plant equipments,[3] Boundary Layer Theory - pdf exercise study [unidentified] Marketing & Sales. He has done detailed study on Wind project resource and risk assessments, wind farm losses,WEB REFERENCES crane investigations and other special projects.[1] http://www.engineeringtoolbox.com/air-absolute-kinematic-viscosity- d_601.html[2] http://www.efunda.com/formulae/fluids/calc_reynolds.cfm#calc[3] www.wasp.dk .[4] www.ysasoft.com[5] www.atlas.nrcan.gc.ca