Novel Techniques in Wind Engineering Horia HANGAN INTRODUCTION - - PowerPoint PPT Presentation

Novel Techniques in Wind Engineering

Horia HANGAN

INTRODUCTION

Pielke Jr. (1997): minimize Vulnerability = f (Incidence, Exposure)

Incidence = f (Intensity, Occurrence, Frequency) Exposure = f (Population, Property, Preparedness)

SUMMARY

• New Laboratory: Novel WindEEE experiments

– Non-Synoptic Winds: Tornadoes, Downbursts – New Flow and Structural Analysis – Topography and Canopy Effects – Multiscale Experiments: Wind Turbines, Solar Panels – Measurement Techniques: Particle Tracking

• New Numerical: Multiple Space and Time Scales

– Mesoscale: WARF, Reanalysis – Microscale: Urban wind environment

• New Full Scale: Real Space-Time Data

– Mobile Doppler Radar – LiDAR

Climate

PDF, Vg, Vgr

ABL

V(z), Iu(z), S(f)

Aerodynamics

p(x,t)

Structural Response

x,a,M,F

LABORATORY

• WindEEE Dome : new three dimensional

and time-dependent wind chamber

• can simulate various wind systems from

sheared winds and gust fronts to tornadoes and downbursts

• a multi-scale, multi-purpose facility for

wind research

The Wind Engineering Energy and Environment (WindEEE) Dome

www.windeee.ca

WindEEE: Preliminary Design

straight/sheared flow tornado flow downburst flow

WindEEE: Engineering Design

• 106 individually controlled fans
• 2 MW maximum power
• 5 m lift and turntable
• 1600 floor roughness elements
• 1000+ tons of steel
• 1850 m³ of concrete
• LEEDs Silver accreditation

WindEEE: Research Ready

Six Initial Design Specifications:

• Straight Mode Uniform
• Straight Mode Boundary Layer
• Straight Mode Shear
• Tornado
• Downburst
• Reversed Flow Mode

+ HH 7 

WindEEE: Tornado Research

• M. Refan, D. Parvu, H. Hangan – ICWE14

WindEEE: Tornado Research

Data Analysis: Velocity Correlations

Fan angle 10° Fan angle 20° Fan angle 30° Height 0.035 m 0.045 m 0.070 m 0.080 m 0.150 m

  

v u j i j i

v v u u n Correlatio     

, ,

• R. Ashton, M. Refan, H. Hangan, G. V. Iungo

Data Analysis: Independent component analysis

• L. Carassale, M. Refan, H. Hangan

WindEEE: Tornado Research

(a) (b) (c) (d) (e) (f)

• Generic Industrial

and Hospital building shapes

• Tests in BLWTL and

WindEEE

• Determine the

loading differences

• M. Refan, H. Hangan

WindEEE: Downburst Research

• D. Parvu, A. Costache, H. Hangan – ICWE 14

WindEEE: Downburst Research

• Downburst TL Interaction
• H/D < 1; H/D > 1
• Roughness Effects
• ElDamatty, Bitsuamlak,

Savory, Hangan

• A. ElDamatty, A. ElAwady – ICWE14

Wind-Structure: Thunderstorm Response Spectrum

eq

f  

eq d

ˆ f f S n ,   

 

eq d,eq 1

ˆ S n , ,     f f

SDOF system NDOF system

0.002   0.01   0.05  

Solari et al., W&S, 2015 Solari et al., JWEIA, 2015 Solari & De Gaetano, ICWE14

time speed (m/s)

eq,N

f

eq,2

f

eq,1

f

N 2 1

Wind-Structure:Gust-Front Factor (GG-F)

Modeling and analysis of thunderstorm/downburst generated gust-front wind loads effect on structures

Web-enabled module to facilitate the use of the GFF framework http://gff.ce.nd.edu

1) Kwon, D. K., and Kareem, A., "Gust-front factor: New Framework for Wind Load Effects on Structures." Journal of Structural Engineering, ASCE, 135(6), 717-732, 2009. 2) Kwon, D. K., Kareem, A. "Generalized gust-front factor: A computational framework for wind load effects." Engineering Structures, 48, 635-644, 2013.

WindEEE: Solar Panels Research

Full-scale: ground and roof mounted solar array at WindEEE Dome

WindEEE: Solar Panels Research

Pressure + force balance + strain gauge testing

• Z. Samani, G. Bitsuamlak, H. Hangan

• located near Roskilde, Denmark
• 12 m high peninsula with steep

escarpment

• shows topographical similarity to

wind turbine sites in complex terrain

• provides meaningful test case for

model validation

• comprehensive mast data available

WINDEEE : Topography – Bolund Experiment

• 1/25 Scale Model
• Large Scale PIV: 2 x 1.5

meters

• 4 simultaneous cameras
• Window overlapping
• Several exposures

WINDEEE : Topography – Bolund Experiment

WINDEEE : Topography – Bolund Experiment

5 10 15 20 25 30 35 40 5 10 15 20 Full Scale Height (m) U (m/s), TI (%) WindEEE (Mean velocity, U)

2 4 6 8 10 12 14 16 18 20 10 20 30 Full Scale Height (m) s/u* Full Scale Bolund Data WindEEE Data

• Cameras 1 and 2
• Velocity Snapshots

WINDEEE : Topography – Bolund Experiment

WINDEEE : Canopy – PEI Experiment

WINDEEE : Canopy – PEI Experiment

Porosity based Forest Canopy Modeling LAI (Leaf Area Index) measured indirect Satellite data (MODIS)-> obtain LAI estimates LAI distribution mapped over terrain

• D. Parvu, H. Hangan

WINDEEE : Wake Experiment

• Phase-Locked PIV measurements
• 8 azimuthal angles between 0 and 120
• Two axial locations: x/R =1 and x/R =2
• At each azimuthal angle 4 PIV tiles
• P. Hashemi-Tari, K. Siddiqui, H. Hangan – Wind Energy (2015)

WindEEE: Wake Experiment

• Radial profiles of axial deficit velocities at X=R

and X=2R for 8 azimuth angles of blade

WindEEE: Wake Experiment

• Radial profiles of radial velocities at X=R and

X=2R for 8 azimuth angles of blade

WindEEE: Wake Experiment

• Radial profiles of Turbulence Intensity
• Streamlines of Instantaneous Velocity

WindEEE: Wake Experiment

• Scaling Effects

WindEEE: Particle Tracking Techniques

• D. Parvu, A. Costache, M. Refan, H. Hangan – ICWE14

NUMERICAL

• WRF
• Data Reanalysis
• Urban Microscale CFD
• Convective forcing modeling

1 2 3 4

Numerical Modeling: NCEP / NCAR Reanalysis

Kalnay et al (1996) data from 1948 to Dec. 2014 2.5° latitude by 2.5° longitude spatial resolution bilinear interpolation method 4 times a day, daily, and monthly Mean daily values for two wind components 67 years, 24473 data records

Numerical Modeling: Trend Data Analysis

Mean Annual Wind Speed per Direction Mann-Kendall non-parametric test for trend (Mann, 1945; Kendall, 1970) Sen’s slope estimator (Sen, 1968)

𝑇 =

𝑧1=1 𝑜−1 𝑧2=𝑧1+1 𝑜

𝑡𝑕𝑜 𝑦𝑧2 − 𝑦𝑧2 . 𝑍 = 𝑅 𝑧 − 1948 + 𝐶,

• D. Romanic, H. Hangan-Sustainable Cities and Society (2015)

Numerical Modeling: Spectral Analysis

Low frequency wind spectra (blue line) 95% confidence intervals (grey line) Welch method (Welch, 1967) Sunspots: SILSO World Data Center, 1948 13-month moving average applied on mean monthly wind speed data σ.995 confidence level

Numerical Modeling: Urban Environment

 WRF  Nested run (4 domains)  ~30 million cells  Chugach supercomputer

 1024 cores  ~20-30 min

 Kraken supercomputer

 576 cores  ~ 120 min

 CFD and downscaling  ~3 million cells  ~15 min  Total time  ~< 1 hour

Numerical Modeling: Urban Environment

7.6% (9) (4) 5.8% 4.9 % (13) 12.3% (8) 9.6% (4)

Numerical Modeling: Downburst and Tornado

FULL SCALE

• ROTATE Campaign
• Doppler on Wheels
• GBDTV Analysis
• Similarity Analysis
• PEIWEE Campaign
• LiDAR
• Topography
• Canopy
• Wake

Recent Campaigns: ROTATE

Data provided by CSWR ROTATE=Radar Observations of Tornadoes And Thunderstorms Experiment – 2012 Ground-Based Velocity Track Display Single-Doppler radar data of five tornadoes: Kellerville, TX 1995 (F4), Spencer, SD 1998 (F4), Stockton, Oklaunion, TX 2000 (F1), Stratford, TX 2003 (F0), KS 2005 (F1), Clairemont, TX 2005 (F0), Happy, TX 2007 (EF0) and Goshen County, WY 2009 (EF2) Nine tornado volumes: cover wind speeds associated with EF0 to EF3 rated tornadoes

Elie, Manitoba tornado 2007 Bennington Kansas EF-4 tornado 2013

Tornadoes: GBDTV Analysis

DOW 3

Doppler velocity (m/s) contour map of the Happy, TX 2007 tornado at 0203:20 UTC and at 0.3˚ radar beam angle r (m) z (m)

200 400 600 800 200 400 600 800 1000

Vtan (m/s)

40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2

5

Tornadoes: Full Scale Data

Volume Clairemont, volume1 (Clr v1) Happy, volume1 (Hp v1) Happy, volume2 (Hp v2) Goshen , volume1 (GC v1) Goshen , volume2 (GC v2) Goshen, volume3 (GC v3) Stockton, volume1 (Stc v1) Spencer, Volume1 (Sp v1) Spencer, Volume2 (Sp v2) EF EF0 EF1 EF0 EF1 EF1 EF1 EF2 EF3 EF3 zmin (m) 25 71 38 97 75 30 43 51 85 Vtrans (m/s) 1.2 19.4 19.4 9.49 9.49 9.49 10.95 15 15 Vtan,max (m/s) 36.3 39 37.9 41.6 42 42.9 50.2 58.2 62 rc (m) 96 160 160 150 150 100 220 192 208 zmax(m) 200 250 50 42 160 41 40 40 40 Vertical structure VBA 1-cell TD 2-cell VBA 2-cell After TD 2-cell 2-cell

Tornadoes: Scaling

R (km) Vtan (m/s)

0.2 0.4 0.6 0.8 1 10 20 30 40 z= 40 m z= 80 m z= 120 m z= 160 m z= 200 m z= 240 m z= 280 m z= 320 m

Goshen County , WY 2009 (EF2)

• The overall maximum

tangential velocity Vtan,max = Vtan(rc,max, zmax)

• Length scale

rc,max,D/rc,max,S zmax,D/zmax,S λl=

r (km)

λv=

• Velocity scale

Vtan,max,D/Vtan,max,S

Tornadoes: Scaling

Hp v1 GC v2 Sp v2

r (m) Vtan (m/s)

200 400 600 800 1000 5 10 15 20 25 30 z=120m z=136m z=160m z=155m z=200m z=194m

r (m) Vtan (m/s)

200 400 600 800 1000 5 10 15 20 25 30 z=200m z=216m z=250m z=247m

r (m) Vtan (m/s)

200 400 600 800 1000 10 20 30 40 z=80m z=77m z=120m z=120m z=160m z=137m

Tornadoes: Scaling

Swirl ratio Length scale,  l

0.2 0.4 0.6 0.8 1 1.2 1.4 2000 4000 6000

Before touch-down After touch-down Clr v1 Hp v1 Hp v2 Stc v1 GC v1 GC v2 GC v3 Sp v1 Sp v2

Swirl ratio Velocity scale,  v

0.2 0.4 0.6 0.8 1 1.2 1.4 1 2 3 4 5

Hp v1 Clr v1 Hp v2 GC v1 GC v2 GC v3 Sp v1 Sp v2 Stc v1

>F2

• Monte Carlo Simulations for 30,000 years
• Minimum Return Period = 4,000 years/sq.km
• Maximum in Oklahoma, Minimum in Nevada

>F4

• Monte Carlo Simulations for 18 million years
• Minimum Return Period = 16,700/sq.km

95% F0, F1 and F2; Only 0.1% F5

F5=0.1*F4=0.02*F3=0.006*F2

Wind Incidence: Frequency

1921-1995 Data base by Grazulis (1993), Monte Carlo Simulations by Meyer et al. (2005)

• Width increases with F scale
• Median F2 = 100 m
• Median F4 = 600 m
• Length increases with F scale
• Median F2 = 10 km
• Median F4 = 60 km

Wind Incidence: Width and Length

Recent Campaigns: PEIWEE

• Cornell University:

– Two Lidars -> Zephyr and Gallion

• Western University (WindEEE RI and DTU):

– 1 Short Range Lidar – 1 Quadropter

• Wind Energy Institute of Canada (WEICan):

– P.E.I site with 5 wind turbines – Masts 80 m, 60 m, 17 m and 15 m – 1 Zephyr Lidar

• York University:

– 6 masts of 10m

PEIWEE: WindScanner short-range LiDAR

PEIWEE: WindScanner short-range LiDAR

WindEEE WindScanner : short-range LiDAR

• max. wind speed acquisition rate : 500 samples/s

PEIWEE : Wake / Topography

• Line and flower pattern scanning
• Multiple heights : 10, 15, 20 , 40, 80 m
• Multiple tilt angles : 0°, 30°, 60° and 90°
• Multiple wind directions : S to W

PEIWEE : Cliff Measurements

PEIWEE : Forest Edge Measurements

PEIWEE : Wake Measurements

DISCUSSION

Climate

• Meso-scale Models + Full Scale: set proper boundary conditions

Terrain

• Micro-scale Models + Full Scale: run simulations in the surface layer

Stats

• Statistical Analysts: set incidence models

Wind3D

• Wind Fields: 3D and Time-Dependent; Multiscale

Loads

• Aerodynamic Loads: Loads = f(buildings/structures, exposure)

Response •Structural Analysis: Responses to Loads; Collapse modes

CONCLUSIONS

• New Tools for the Wind Engineering Chain
• New Laboratory
• Climate: ABL vs. Non-Synoptic Winds-> Flow Fields
• Topography and Roughness -> Reynolds and 3D effects
• Aerodynamic Loading-> Comparison of ABL vs. Non-Synoptic
• Analysis Techniques-> Spectral vs. Time-Domain vs. Modal
• Statistical Analysis
• New Full Scale

– Doppler Radar + GBDTV; LiDAR

• New Numerical
• Re-Analysis, Meso-Micro coupling, Physical Simulations

REFERENCES

• Pielke Jr., R.A., Refraining the U.S. hurricane problem, Society &Natural Resources: 1997.
• Grazulis, T.P., Significant Tornadoes, 1680-1991. Environmental Films, St. Johnsbury, VT, 1326, 1993.
• Meyer, C. L, Brooks, H. E. and Kay, M. P., A hazard model for tornado occurrence in the United States, 16th

Conference on Probability and Statistics in the Atmospheric Sciences, 2002.

• Xu, Z. and Hangan, H., Scale, boundary and inlet condition effects on impinging jets with application to

downburst simulations, J. of Wind Eng. and Ind. Aerodynamics, 96,2008.

• Hangan, H. and Kim, J.D.*, Numerical characterization of impinging jets with application to downbursts. J.
• f Wind Eng. and Ind. Aerodynamics, 95, Issue 4,2007.
• Refan, M.*, Hangan, H., Wurman, J., “Reproducing Tornadoes in Laboratory Using Proper Scaling”, J. Wind
• Eng. And Ind. Aerodynamics, 2014
• Hashemi-Tari, P*.,Hangan, H., Siddiqui, K., “Flow characterization in the Near-wake region of a Horizontal

Axis Wind Turbine”, Wind Energy (2015)

• Romanic, D.*, Rasouli, A.*, Hangan, H., “Wind resource assessment in complex urban environment”, Wind

Engineering, Vol. 39, Nr. 2, January 2015

• Romanic, D., Hangan, H., “Wind Climatology of Toronto based on NCEP/NCAR reanalysis 1 data set and its

potential relation to solar activity, Sustainable Cities and Society (2015)

Thank You !

www.windeee.ca