1. 1. Advisor Dr. Dat Duthinh Materials & Structural Division Engineering Laboratory Rahul Menon Analyzing Wind Pressure on Building Enclosures
2. 2. Background Project Description Contour Plots Introduction to Extreme Value Distributions Comparing Different Distributions Outline
3. 3. Hurricane Wilma
4. 4. Wind is chaotic and random Its speed fluctuates in time and space Need to design for peaks of pressure Background
5. 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. 6. Fluctuating Wind Pressure
7. 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. 8. Low-Rise Gable Roofed Buildings s 1 3 2
9. 9. Time Series Data
10. 10. 3D Contour Maps - 90 Face 3Face 2
11. 11. 3D Contour Maps - 315 Face 3Face 2
12. 12. What Do We Design For?
13. 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. 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. 15. Gumbel (Type I): ; , , 0 = , = 0 Frechet (Type II): ; , , = ( ) 1 , > 0 Reversed Weibull (Type III): ; , , = ( ) 1 , < 0 Types of Extreme Value Distributions
16. 16. Type I Distribution (BLUE method) P = 0.78 Min 0 Partitions -2.2248 8 Partitions -3.0747
17. 17. Type III Distribution (GEV) P = 0.78 Min Type III -1.1763 Type I -2.2248
18. 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. 19. Dr. Dat Duthinh Emil Simiu Adam Pintar Joe Main Luke Amatucci Matt Kovarek SURF Directors SURF Peers Acknowledgements
20. 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