Kogan Seminar.pptx

7/31/2019 Kogan Seminar.pptx

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Combining Wind Simulations and Bird-DensitySurveys for Optimization of Green Wind Powergeneration in Urban Limited Space Applications

Gil Bohrer

Department of Civil, Environmental &

Geodetic Engineering

The Ohio State University

The Abraham Kogan Seminar, 6/13/2012

Maximizing wind powerand minimizing bird-collisionhazard


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Predicting power output of a turbine

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Wind speed (m/s)

Wind speed distribution (Weibull)

Power output is a non-linear function ofwind speed.Each turbine has a different curve

The long term distribution of wind speed can beconvolved with the power output per wind speedto get the predicted long-term mean power output

www.solacity.com


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www.windsolarenergy.org

1) The tradeoffs between

height

power

& cost

Locations considerations


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2) Obstruction by vegetation and structures

www.rsiwind.com



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Green-campus initiative:Where should a turbine be placed on OSU campus?

A test case for limited-

space urban application

2 focus study areas:

The Olentangy Wetland

Central campus


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Roads, river, building outlines fromGIS county map

Trees and building height from 3D lidar

Step 1:Develop a 3-D campus obstruction map


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Faculty clubMendenhall

Step 1:Develop a 3-D campus obstruction map

3D rendering and sharpening of edges.Simple rules for building and trees (Garrity et al 2012)


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Step 2:Wind climatology of OSU campus

Category Records North East South West

Summer convective 16190 (17.31%) 30.09% 33.94% 19.83% 16.15%

Summer neutral 30914 (33.06%) 31.93% 32.21% 19.67% 16.20%

Winter 46400 (49.62%) 30.92% 36.88% 18.50% 13.70%

Total 93504

A B C

Summer convective Summer Neutral Winter Neutral

The North American Regional Reanalysis1972 current, 32 km, 3 hours weather data
http://www.ncep.noaa.gov/http://www.doc.gov/http://www.noaa.gov/http://www.nws.noaa.gov/


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Step 3:Integrate wind and obstruction using Large-eddy simulations


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Vegetation representation Leaf Area Density (LAD) (Bohrer et al 2007)

Source of heat and vapor fluxusing a coupled biosphere model

Light attenuation

Drag

Finite volume solver for Navier-Stokes equations~1 m3 resolution, over ~1 km3 domain

Building representation Reduce free volume and apertures in finite volume solver

The RAMS-Based Forest Large Eddy Simulation (RAFLES)presents a new approach to canopy and building representation inhigh resolution 3-D heterogeneous domains

Bohrer et al 2008, 2009


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RAFLES determines the distribution of windspeed and turbulence(also temperature, humidity, CO2 flux) above and inside the canopyand disturbance field


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A. Representation of the topography and buildings

Partial volume

Shaved aperture at the surface interface

z21

22x12x22

11

x0 x1 x2

z2

z1

z0

z22

The Shaved Grid Cell Coordinate System:


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Representation of vegetation

Porous volume Venetian blind aperture.

Brown volume restricts space

Green + Brown volume generate drag

BROWN VOLUME:

branches + stems

GREEN VOLUME:

leaves

The Shaved Grid Cell Coordinate system:


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Sparse Dense

DragOnly

Cont

rol


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Sparse Dense

DragOnly

Cont

rol


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Importance of high resolution dynamics:Location of momentum ejection and sweep events


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Results Wetland wind distribution

Summer season, convective boundary layer conditions


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Results Wetland wind distribution

Winter season, neutral boundary layer conditions


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Results 3D Power distribution potentialCombine all seasons and conditions, convolve with power curve


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Results Central campus


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Unfortunate vulture

3) Potential concerns to be accounted for noise, aesthetics, bird collisions

Campus wide survey for birds

individual density

Species richness

All birds Native species

http://www.youtube.com/embed/na6HxKQQsAMhttp://www.youtube.com/embed/na6HxKQQsAM


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Native bird densityTotal bird individual density



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Native bird species richnessBird individual density



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Combining bird collision-riskwith power potential considerationsfor selection of optimalwind turbine location

Birds do not matter

Central campusWetland

Birds = 50% max power

Birds = 90% max power

T k i ill id b i f i bi d l i


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Track annotation will provide better information on bird locationsMortality data is desperately needed

Acknowledgements


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AcknowledgementsMy research group: Tony Zhu, Robert Jones, Steve Garrity, Kyle Maurer,Thalia Chatliefstratiou, William Kenny, Ashley Matheney, Liel Naor-Azrieli

The Project was funded by the OSU institute for Energy and the

EnvironmentCo-PI: Peter Curtis (Bird observations)

ReferencesBohrer et al. 2007, Tellus B

Bohrer et al. 2008, Journal of EcologyBohrer et al. 2009, Boundary Layer MeteorologyGarrity et al. 2012, Remote Sensing Letters

Additional funding:

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