Transcript of Menon_Rahul_SURF_Presentation_V4
- 1. Advisor Dr. Dat Duthinh Materials & Structural Division
Engineering Laboratory Rahul Menon Analyzing Wind Pressure on
Building Enclosures
- 2. Background Project Description Contour Plots Introduction to
Extreme Value Distributions Comparing Different Distributions
Outline
- 3. Hurricane Wilma
- 4. Wind is chaotic and random Its speed fluctuates in time and
space Need to design for peaks of pressure Background
- 5. Different building scale models were tested Variables
Changed: Eave height Model Size Roof Slope Width Length Terrain
Type cp: unit less coefficient describing wind pressure Wind Tunnel
Testing
- 6. Fluctuating Wind Pressure
- 7. Plot 2D and 3D contour maps of pressure coefficients at
unevenly distributed taps Analyzes the peak pressure coefficients
and wind direction of chosen tap Alternative method of estimating
peak wind pressure with a given probability of exceedance
Software
- 8. Low-Rise Gable Roofed Buildings s 1 3 2
- 9. Time Series Data
- 10. 3D Contour Maps - 90 Face 3Face 2
- 11. 3D Contour Maps - 315 Face 3Face 2
- 12. What Do We Design For?
- 13. Fit peak values to a distribution function Able to estimate
values with a probability of exceedance Only limit distribution for
large sets of random variables Introduction to Extreme Value
Distributions
- 14. Unbiased The estimator function, which is a random
variable, should equal the value being estimated = = = Minimum
Variance The estimator function becomes more accurate as sample
size increases Conditions for Extreme Value Distributions
- 15. Gumbel (Type I): ; , , 0 = , = 0 Frechet (Type II): ; , , =
( ) 1 , > 0 Reversed Weibull (Type III): ; , , = ( ) 1 , < 0
Types of Extreme Value Distributions
- 16. Type I Distribution (BLUE method) P = 0.78 Min 0 Partitions
-2.2248 8 Partitions -3.0747
- 17. Type III Distribution (GEV) P = 0.78 Min Type III -1.1763
Type I -2.2248
- 18. Made tools that will be used to analyze wind pressure data
from wind tunnels Extended peak analysis to more general extreme
value distributions Conclusions
- 19. Dr. Dat Duthinh Emil Simiu Adam Pintar Joe Main Luke
Amatucci Matt Kovarek SURF Directors SURF Peers
Acknowledgements
- 20. http://fris2.nist.gov/winddata/uwo-data/uwo-data.html
http://fris2.nist.gov/winddata/uwo-data/blwt-ss20-2003.pdf
http://www.itl.nist.gov/div898/winds/homepage.htm
https://en.wikipedia.org/wiki/Generalized_extreme_value_
distribution http://www.palmbeachpost.com/news/weather/hurrican
es/hurricane-wilma-five-years-later-storm-taught- hard/nMByM/
Citations