Submit Search
Upload
10 n18 ijeset0702917-v7-is2-631-640
•
0 likes
•
310 views
S
Songpol Charuvisit
Follow
DETERMINATION OF BASIC MEAN HOURLY WIND SPEEDS FOR STRUCTURAL DESIGN IN NAIROBI COUNTRY
Read less
Read more
Engineering
Report
Share
Report
Share
1 of 10
Download now
Download to read offline
Recommended
F04823142
F04823142
IOSR-JEN
İstanbul Deprem Erken Uyarı Sistemi
İstanbul Deprem Erken Uyarı Sistemi
Ali Osman Öncel
Iamg2014 likso
Iamg2014 likso
Tanja Likso
Delineation of Mahanadi River Basin by Using GIS and ArcSWAT
Delineation of Mahanadi River Basin by Using GIS and ArcSWAT
inventionjournals
Simulating Weather: Numerical Weather Prediction as Computational Simulation
Simulating Weather: Numerical Weather Prediction as Computational Simulation
Ting-Shuo Yo
TU2.T10.2.ppt
TU2.T10.2.ppt
grssieee
CEPSI 2014 Full paper JKT Alstom WIND TURBINE OPERATION IN TYPHOON CONDITIONS
CEPSI 2014 Full paper JKT Alstom WIND TURBINE OPERATION IN TYPHOON CONDITIONS
Josef Tadich
Contribution to the investigation of wind characteristics and assessment of w...
Contribution to the investigation of wind characteristics and assessment of w...
Université de Dschang
Recommended
F04823142
F04823142
IOSR-JEN
İstanbul Deprem Erken Uyarı Sistemi
İstanbul Deprem Erken Uyarı Sistemi
Ali Osman Öncel
Iamg2014 likso
Iamg2014 likso
Tanja Likso
Delineation of Mahanadi River Basin by Using GIS and ArcSWAT
Delineation of Mahanadi River Basin by Using GIS and ArcSWAT
inventionjournals
Simulating Weather: Numerical Weather Prediction as Computational Simulation
Simulating Weather: Numerical Weather Prediction as Computational Simulation
Ting-Shuo Yo
TU2.T10.2.ppt
TU2.T10.2.ppt
grssieee
CEPSI 2014 Full paper JKT Alstom WIND TURBINE OPERATION IN TYPHOON CONDITIONS
CEPSI 2014 Full paper JKT Alstom WIND TURBINE OPERATION IN TYPHOON CONDITIONS
Josef Tadich
Contribution to the investigation of wind characteristics and assessment of w...
Contribution to the investigation of wind characteristics and assessment of w...
Université de Dschang
phd
phd
Karl Nilsson
Numerical Weather Prediction
Numerical Weather Prediction
Sourav Kumar Mund
Comparison of Solar and Wind Energy Potential at University of Oldenburg, Ger...
Comparison of Solar and Wind Energy Potential at University of Oldenburg, Ger...
Dipta Majumder
J041245863
J041245863
IOSR-JEN
Weather Prediction: History and Math
Weather Prediction: History and Math
Ida Regine
TIME INTEGRATION OF EVAPOTRANSPIRATION USING A TWO SOURCE SURFACE ENERGY BALA...
TIME INTEGRATION OF EVAPOTRANSPIRATION USING A TWO SOURCE SURFACE ENERGY BALA...
Ramesh Dhungel
Time integration of evapotranspiration using a two source surface energy bala...
Time integration of evapotranspiration using a two source surface energy bala...
Ramesh Dhungel
Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...
Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...
BREEZE Software
On the formulation_of_asce7_95_gust_effe
On the formulation_of_asce7_95_gust_effe
vijith vasudevan
Paper id 71201909
Paper id 71201909
IJRAT
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASP
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASP
IJERA Editor
Site-dependent Spectra: Ground Motion Records in Turkey
Site-dependent Spectra: Ground Motion Records in Turkey
Ali Osman Öncel
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...
GIS in the Rockies
ex_abs_framework-2
ex_abs_framework-2
Damian Vigouroux
Climate data management
Climate data management
Prof. A.Balasubramanian
Calculation and analysis of total quantity of solar radiation incident on the...
Calculation and analysis of total quantity of solar radiation incident on the...
Alexander Decker
MS thesis
MS thesis
Bo Luan
Combined ariborne and ground geophysics Flåm Norway
Combined ariborne and ground geophysics Flåm Norway
Bjorn Sture Rosenvold
Cyclone storm prediction using knn
Cyclone storm prediction using knn
priya veeramani
ÖNCEL AKADEMİ: INTRODUCTION TO SEISMOLOGY
ÖNCEL AKADEMİ: INTRODUCTION TO SEISMOLOGY
Ali Osman Öncel
Wind energy forecasting using radial basis function neural networks
Wind energy forecasting using radial basis function neural networks
eSAT Journals
Wind Energy in Pakistan
Wind Energy in Pakistan
Salman Noor
More Related Content
What's hot
phd
phd
Karl Nilsson
Numerical Weather Prediction
Numerical Weather Prediction
Sourav Kumar Mund
Comparison of Solar and Wind Energy Potential at University of Oldenburg, Ger...
Comparison of Solar and Wind Energy Potential at University of Oldenburg, Ger...
Dipta Majumder
J041245863
J041245863
IOSR-JEN
Weather Prediction: History and Math
Weather Prediction: History and Math
Ida Regine
TIME INTEGRATION OF EVAPOTRANSPIRATION USING A TWO SOURCE SURFACE ENERGY BALA...
TIME INTEGRATION OF EVAPOTRANSPIRATION USING A TWO SOURCE SURFACE ENERGY BALA...
Ramesh Dhungel
Time integration of evapotranspiration using a two source surface energy bala...
Time integration of evapotranspiration using a two source surface energy bala...
Ramesh Dhungel
Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...
Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...
BREEZE Software
On the formulation_of_asce7_95_gust_effe
On the formulation_of_asce7_95_gust_effe
vijith vasudevan
Paper id 71201909
Paper id 71201909
IJRAT
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASP
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASP
IJERA Editor
Site-dependent Spectra: Ground Motion Records in Turkey
Site-dependent Spectra: Ground Motion Records in Turkey
Ali Osman Öncel
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...
GIS in the Rockies
ex_abs_framework-2
ex_abs_framework-2
Damian Vigouroux
Climate data management
Climate data management
Prof. A.Balasubramanian
Calculation and analysis of total quantity of solar radiation incident on the...
Calculation and analysis of total quantity of solar radiation incident on the...
Alexander Decker
MS thesis
MS thesis
Bo Luan
Combined ariborne and ground geophysics Flåm Norway
Combined ariborne and ground geophysics Flåm Norway
Bjorn Sture Rosenvold
Cyclone storm prediction using knn
Cyclone storm prediction using knn
priya veeramani
ÖNCEL AKADEMİ: INTRODUCTION TO SEISMOLOGY
ÖNCEL AKADEMİ: INTRODUCTION TO SEISMOLOGY
Ali Osman Öncel
What's hot
(20)
phd
phd
Numerical Weather Prediction
Numerical Weather Prediction
Comparison of Solar and Wind Energy Potential at University of Oldenburg, Ger...
Comparison of Solar and Wind Energy Potential at University of Oldenburg, Ger...
J041245863
J041245863
Weather Prediction: History and Math
Weather Prediction: History and Math
TIME INTEGRATION OF EVAPOTRANSPIRATION USING A TWO SOURCE SURFACE ENERGY BALA...
TIME INTEGRATION OF EVAPOTRANSPIRATION USING A TWO SOURCE SURFACE ENERGY BALA...
Time integration of evapotranspiration using a two source surface energy bala...
Time integration of evapotranspiration using a two source surface energy bala...
Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...
Comparison Of Onsite And Nws Meteorology Data Sets Based On Varying Nearby La...
On the formulation_of_asce7_95_gust_effe
On the formulation_of_asce7_95_gust_effe
Paper id 71201909
Paper id 71201909
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASP
Calculating Wind Farm Production in Al-Shihabi (South Of Iraq) Using WASP
Site-dependent Spectra: Ground Motion Records in Turkey
Site-dependent Spectra: Ground Motion Records in Turkey
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...
ex_abs_framework-2
ex_abs_framework-2
Climate data management
Climate data management
Calculation and analysis of total quantity of solar radiation incident on the...
Calculation and analysis of total quantity of solar radiation incident on the...
MS thesis
MS thesis
Combined ariborne and ground geophysics Flåm Norway
Combined ariborne and ground geophysics Flåm Norway
Cyclone storm prediction using knn
Cyclone storm prediction using knn
ÖNCEL AKADEMİ: INTRODUCTION TO SEISMOLOGY
ÖNCEL AKADEMİ: INTRODUCTION TO SEISMOLOGY
Similar to 10 n18 ijeset0702917-v7-is2-631-640
Wind energy forecasting using radial basis function neural networks
Wind energy forecasting using radial basis function neural networks
eSAT Journals
Wind Energy in Pakistan
Wind Energy in Pakistan
Salman Noor
Experimental investigation, optimization and performance prediction of wind t...
Experimental investigation, optimization and performance prediction of wind t...
eSAT Journals
Experimental investigation, optimization and performance prediction of wind t...
Experimental investigation, optimization and performance prediction of wind t...
eSAT Publishing House
Ht2514031407
Ht2514031407
IJERA Editor
Ht2514031407
Ht2514031407
IJERA Editor
A Stochastic Approach to Determine Along-Wind Response of Tall Buildings
A Stochastic Approach to Determine Along-Wind Response of Tall Buildings
IRJET Journal
Simulation of Wind Power Dynamic for Electricity Production in Nassiriyah Dis...
Simulation of Wind Power Dynamic for Electricity Production in Nassiriyah Dis...
IOSR Journals
Grand Renewable Energy Conference
Grand Renewable Energy Conference
Kaushik Sharma
Suitable Wind Turbine Selection using Evaluation of Wind Energy Potential in ...
Suitable Wind Turbine Selection using Evaluation of Wind Energy Potential in ...
IJCI JOURNAL
C041112247
C041112247
IOSR-JEN
IRJET - Wind Analysis of High Rise Buildings
IRJET - Wind Analysis of High Rise Buildings
IRJET Journal
B04910932
B04910932
IOSR-JEN
Impact of field roughness and power losses, turbulence intensity on electrici...
Impact of field roughness and power losses, turbulence intensity on electrici...
International Journal of Power Electronics and Drive Systems
An Experimental Study of Weibull and Rayleigh Distribution Functions of Wind ...
An Experimental Study of Weibull and Rayleigh Distribution Functions of Wind ...
TELKOMNIKA JOURNAL
A041110112
A041110112
IOSR-JEN
WindResourceAndSiteAssessment.pdf
WindResourceAndSiteAssessment.pdf
SAKTHI NIVASHAN.S
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco
IJECEIAES
Wind resource assessment in large cities
Wind resource assessment in large cities
Stephane Meteodyn
Assessment of wind resource and
Assessment of wind resource and
ijcsa
Similar to 10 n18 ijeset0702917-v7-is2-631-640
(20)
Wind energy forecasting using radial basis function neural networks
Wind energy forecasting using radial basis function neural networks
Wind Energy in Pakistan
Wind Energy in Pakistan
Experimental investigation, optimization and performance prediction of wind t...
Experimental investigation, optimization and performance prediction of wind t...
Experimental investigation, optimization and performance prediction of wind t...
Experimental investigation, optimization and performance prediction of wind t...
Ht2514031407
Ht2514031407
Ht2514031407
Ht2514031407
A Stochastic Approach to Determine Along-Wind Response of Tall Buildings
A Stochastic Approach to Determine Along-Wind Response of Tall Buildings
Simulation of Wind Power Dynamic for Electricity Production in Nassiriyah Dis...
Simulation of Wind Power Dynamic for Electricity Production in Nassiriyah Dis...
Grand Renewable Energy Conference
Grand Renewable Energy Conference
Suitable Wind Turbine Selection using Evaluation of Wind Energy Potential in ...
Suitable Wind Turbine Selection using Evaluation of Wind Energy Potential in ...
C041112247
C041112247
IRJET - Wind Analysis of High Rise Buildings
IRJET - Wind Analysis of High Rise Buildings
B04910932
B04910932
Impact of field roughness and power losses, turbulence intensity on electrici...
Impact of field roughness and power losses, turbulence intensity on electrici...
An Experimental Study of Weibull and Rayleigh Distribution Functions of Wind ...
An Experimental Study of Weibull and Rayleigh Distribution Functions of Wind ...
A041110112
A041110112
WindResourceAndSiteAssessment.pdf
WindResourceAndSiteAssessment.pdf
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco
Evaluation of the Energy Performance of the Amougdoul Wind Farm, Morocco
Wind resource assessment in large cities
Wind resource assessment in large cities
Assessment of wind resource and
Assessment of wind resource and
Recently uploaded
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
wendy cai
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
9953056974 Low Rate Call Girls In Saket, Delhi NCR
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
Call Girls in Nagpur High Profile
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
Asst.prof M.Gokilavani
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
rehmti665
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
ranjana rawat
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZTE
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
hassan khalil
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
KurinjimalarL3
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
VICTOR MAESTRE RAMIREZ
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Biology for Computer Engineers Course Handout.pptx
Biology for Computer Engineers Course Handout.pptx
DeepakSakkari2
Past, Present and Future of Generative AI
Past, Present and Future of Generative AI
abhishek36461
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
João Esperancinha
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
Suhani Kapoor
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Low Rate Call Girls In Saket, Delhi NCR
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
RajaP95
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
ranjana rawat
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
Mark Billinghurst
Recently uploaded
(20)
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
Biology for Computer Engineers Course Handout.pptx
Biology for Computer Engineers Course Handout.pptx
Past, Present and Future of Generative AI
Past, Present and Future of Generative AI
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
10 n18 ijeset0702917-v7-is2-631-640
1.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 631 DETERMINATION OF BASIC MEAN HOURLY WIND SPEEDS FOR STRUCTURAL DESIGN IN NAIROBI COUNTRY E.O. Ong’ayo1 , S.K. Mwea2 , S.O. Abuodha3 1 Post Graduate Student in Master in Civil Engineering (Structural Option) at PAUISTI 2 Professor, Department of Civil Engineering, University of Nairobi, Kenya 3 Senior Lecturer, Department of Civil Engineering, University of Nairobi, Kenya ABSTRACT Structural Engineers should be able to accurately predict all loads affecting structures. Consideration of wind loads is very important in the structural design especially in the design of high rise structures. This, among accurate prediction of other loads affecting a structure, ensures safe and economic design of structures. Kenyan structural engineers depend on basic wind speeds which were determined over 30 years ago. It is important to periodically review the wind speeds. This is because the wind speeds experienced could change due to changes in land use like deforestation to give way for infrastructure among other causes. Most structures are usually designed for a life period of 50 yrs, thus the basic wind speed will have a return period of 50 years. The objective of this paper is to provide the basic wind speed which will be used to design building structures in Nairobi. Wind data was taken from 8 meteorological stations within and around Nairobi County. Terrain and altitude correction factors were also determined for all the meteorological stations. These correction factors were multiplied to wind speeds measured at anemometer sites to obtain the design wind speeds for a standard open country terrain and standard elevation. This data was then analyzed statistically using Gumbel method to determine the basic wind speed, from which wind pressure affecting a typical structure can be calculated. KEYWORDS: Basic wind speed, Gumbel method, Terrain Factor, Altitude factor. I. INTRODUCTION Wind loads need to be considered in the design of structures especially high-rise and light weight structures. Wind loads are usually specified as pressure due to the predicted maximum wind velocity. This necessitates accurate prediction of the maximum wind velocity that can be experienced over the design life of structures so that the design is carried out with these wind speeds in mind. Since structures are generally designed for a design life of 50 yrs, this paper endeavors to give the extreme wind speeds for a 50 year return period in Nairobi County. Structural Engineers in Kenya use basic wind speeds which were determined about 30 years ago. However it is proper to periodically review the wind speeds to be sure that the loads are still accurate. This is because the changing land use in the city and its environs may result in change in wind speeds which would in turn change the wind loads on structures. In Kenya, the Kenya Meteorology Department (KMD) is responsible for wind measurements as well as storage of wind data. For purposes of this research, hourly wind speeds for weather stations within Nairobi County and its environs were obtained from KMD. Wind data was obtained for eight weather stations and analyzed to obtain the extreme wind speeds of 50year return period.
2.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 632 II. METHODOLOGY The general procedure that was followed for analysis of the hourly-mean speeds from each anemometer to derive the design wind speeds was as follows: [1] Data Quality: The quality of measurements from each anemometer was checked and any inconsistent data separated. Exposure Correction: Wind records from each anemometer were transposed with respect to the anemometer elevations and terrains around the anemometers so as to be consistent with open country terrain (Engineering Sciences Data Unit (ESDU)) ZO =0.03m) Extreme Value Analysis: Extreme value statistical analysis was carried out. 2.1 Weather Stations Wind records consisting of hourly mean wind speeds and daily gust speeds were analyzed over a range of five to twelve years. The anemometer effective height was taken as 10m. In most meteorological stations wind speeds were measured at 10m height. However in some (mostly the agro meteorological stations), the wind speeds were measured at 2m height and thus they had to be adjusted to 10m heights using the wind profile power law.[3] The wind profile power law relationship is: u/ur = (z/zr)α ………………………………………………………………(1) Where u is the wind speed (in m/s) at height z (in metres) and ur is the known wind speed at a reference height zr. The exponent (α) is an empirically derived coefficient that varies dependent upon the stability of the atmosphere. For neutral stability conditions, α is approximately 1/7 or 0.143. In order to determine the wind speed at 10m, the relationship was rearranged to: ux = ur(10/2)0.143 ……………………………………………………………………………..(2) 2.2 Exposure Correction Wind speeds measured by anemometers are affected by other factors other than the strength of winds. The surrounding terrain and elevation of the anemometer site have an effect on the anemometer readings. For instance rough upwind terrains result in slow wind speeds. To account for the various elevations and terrains surrounding the anemometer, results in wind speeds measured at an anemometer being transposed to a reference terrain at standard height. Transposition allows for comparison of anemometers in close proximity and provides a basis for the comparison of wind speeds. Wind records were corrected for terrain and altitude. These correction factors were assessed for each anemometer and the factors applied to the wind records to get the design wind speeds. 2.2.1 Terrain Assessments. Terrain assessments were carried out using the atmospheric boundary layer model used in the ESDU analyses developed by Deaves and Harris. It takes account of varying surface roughness with fetch. [2] Surface roughness length is characterized by a typical zo. In this method, the varying terrain roughness with fetch at 300 sectors about the origin is taken account of. A detailed survey of the terrain roughness was carried out for each anemometer from images obtained from GOOGLE EARTH The analysis was carried out for each of the twelve sectors of wind direction to a distance of 50 km from the anemometer and for each sector and patch of the terrain, a typical roughness coefficient ZO assigned as shown in figure 1.
3.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 633 Figure 1. Google Earth Map of Terrain around Dagoretti Meteorological Station. A conservative estimation was made. This considered a rougher upwind terrain thus resulting in higher factors being applied to the wind records. 2.2.2 Altitude Factor The design wind speed is usually defined at the mean sea level. Hence, wind speeds measured at anemometers were corrected to account for the effect of the difference in elevation of the anemometer relative to the mean sea level. Based on these elevations an altitude correction factor was estimated as recommended in international standards, and used in the Eurocode. [2] The altitude factor is of the form: SA = 1 + 0.001(A)……………………………………………………………….(3) Where A is the elevation above sea level in metres. SA is the Altitude Factor. These correction factors (both the terrain and altitude factors) were multiplied to wind speeds measured at anemometer sites to obtain the design wind speeds for a standard open country terrain roughness and standard elevation. 2.3 Extreme Value Analysis Procedure Research has shown that Type 1 Extreme Value Distribution is appropriate for analysis of extreme wind speed measurements in areas which rarely experience hurricanes.[4] Type 1 distribution is of the form: Px = exp[-exp(-y)] ]……………………………………………..………………(5) The reduced variate y, is given by: Y=-ln[-ln(Px)] ]……………………………………………..………………….(6) Px is the probability that an extreme value will be less than a value x within any given year. 2.3.1 Gumbel Method The following procedure was used to fit recorded Independent Storm Maxima to the Type 1 Extreme Value Distribution: The Largest wind speed from each storm was extracted.
4.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 634 The series was ranked in order from the smallest to largest. Each value was assigned a value of non-exceedence according to: P=m/(N+1)……………………………………………………………….(7) The reduced variate was formed from: R=-ln(-lnp)……………………………………………………………….(8) The Independent Storms Maximum wind speeds were plotted against y, and a line of best fit drawn by means of linear regression, u=mode=intercept of the line, 1/a = slope of the line. For the 50 year return period, the following equation was used to determine extreme wind speed. Ur=u+1/a (lnR)………………………………………………………….. (9). The graphs drawn from the analysis was carried out for the meteorological stations are as shown below: Figure 2: Graph of Storm Maximum Wind Speed Versus Reduced variate of Dagoretti Hourly Wind Speeds. Figure 3: Graph of Storm Maximum Wind Speed Versus Reduced variate of Eastleigh Hourly Wind Speeds. y = 0.8491x + 15.305 R² = 0.8413 0.00 5.00 10.00 15.00 20.00 25.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED VARIATE y = 1.089x + 15.942 R² = 0.8831 0.00 5.00 10.00 15.00 20.00 25.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED…
5.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 635 Figure 4: Graph of Storm Maximum Wind Speed Versus Reduced variate of JKIA Hourly Wind Speeds. Figure 5: Graph of Storm Maximum Wind Speed Versus Reduced variate of Kabete Hourly Wind Speeds. y = 0.6891x + 16.249 R² = 0.66 0.00 5.00 10.00 15.00 20.00 25.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED VARIAYE Linear (STORM MAXIMA VERSUS REDUCED VARIAYE) y = 0.7944x + 13.24 R² = 0.7588 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED VARIATE Linear (STORM MAXIMA VERSUS REDUCED VARIATE)
6.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 636 Figure 6: Graph of Storm Maximum Wind Speed Versus Reduced variate of Machakos Hourly Wind Speeds. Figure 7: Graph of Storm Maximum Wind Speed Versus Reduced variate of Narok Hourly Wind Speeds. y = 1.0959x + 14.786 R² = 0.8288 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED VARIATE Linear (STORM MAXIMA VERSUS REDUCED VARIATE) y = 1.3471x + 18.766 R² = 0.7254 0.00 5.00 10.00 15.00 20.00 25.00 30.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED VARIATE Linear (STORM MAXIMA VERSUS REDUCED VARIATE)
7.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 637 Figure 8: Graph of Storm Maximum Wind Speed Versus Reduced variate of Thika Hourly Wind Speeds. Figure 9: Graph of Storm Maximum Wind Speed Versus Reduced variate of Wilson Hourly Wind Speeds. III. RESULTS 3.1 Correction Factors The tables below shows the altitude factors, the terrain factors and the overall correction factors obtained for every meteorological station. y = 2.3963x + 15.209 R² = 0.8699 0.00 5.00 10.00 15.00 20.00 25.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED VARIATE Linear (STORM MAXIMA VERSUS REDUCED VARIATE) y = 0.6722x + 20.002 R² = 0.788 18.50 19.00 19.50 20.00 20.50 21.00 21.50 22.00 22.50 23.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 STORM MAXIMA VERSUS REDUCED VARIATE STORM MAXIMA VERSUS REDUCED VARIATE Linear (STORM MAXIMA VERSUS REDUCED VARIATE)
8.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 638 Table 1. Altitude Correction factors for the meteorological stations ALTITUDE FACTORS METEOROLOGICAL STATIONS KABETE JKIA WILSON DAGORETTI EASTLEIGH MACHAKOS THIKA NAROK ALTITUDE 1820 1624 1679 1798 1640 1750 1549 1890 FACTORS 2.82 2.62 2.68 2.80 2.64 2.75 2.55 2.89 Table 2. Terrain Correction factors for the meteorological stations TERRAIN FACTORS STATIONS DIRECTION 30 60 90 120 150 180 210 240 270 300 330 360 KABETE 0.5 0.5 0.5 0.8 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 JKIA 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.8 0.8 0.6 WILSON 0.8 0.8 0.4 0.3 0.3 0.3 0.3 0.5 0.5 0.5 0.5 0.5 DAGORETTI 0.5 0.5 0.8 0.4 0.3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 EASTLEIGH 0.5 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 MACHAKOS 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 THIKA 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.5 0.5 0.5 NAROK 0.5 0.5 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.5 0.5 Table 3. Combination of Altitude and Terrain Correction factors for the meteorological stations CORRECTION (ALTITUDE AND TERRAIN) FACTORS STATIONS DIRECTION 30 60 90 120 150 180 210 240 270 300 330 360 KABETE 1.41 1.41 1.41 2.26 1.13 1.41 1.41 1.41 1.41 1.41 1.41 1.41 JKIA 1.05 1.05 0.79 0.79 0.79 0.79 0.79 0.79 1.31 2.10 2.10 1.57 WILSON 2.14 2.14 1.07 0.80 0.80 0.80 0.80 1.34 1.34 1.34 1.34 1.34 DAGORETTI 1.40 1.40 2.24 1.12 0.84 1.40 1.40 1.40 1.40 1.40 1.40 1.40 EASTLEIGH 1.32 1.06 1.06 1.06 1.06 1.06 1.32 1.32 1.32 1.32 1.32 1.32 MACHAKOS 1.38 1.38 0.83 0.83 0.83 0.83 0.83 0.83 0.83 0.83 1.38 1.38 THIKA 1.27 1.27 1.27 1.27 1.02 0.76 0.76 0.76 1.27 1.27 1.27 1.27 NAROK 1.45 1.45 1.45 0.87 0.87 0.87 0.87 0.87 0.87 0.87 1.45 1.45 3.2. Wind Speed Equations The following table shows the equations for the Wind Speeds versus Reduced Variate. Table 4 Equations of Wind Speeds versus Reduced Variate STATIONS EQUATION KABETE 0.7944x +13.24 JKIA 0.6891x + 16.249 WILSON 0.6722x + 20.002 DAGORETTI 0.8419x + 15.305
9.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 639 EASTLEIGH 1.089x + 15.942 MACHAKOS 1.0959x + 14.786 THIKA 2.3963x +15.209 NAROK 1.3471x + 18.766 3.3. Predicted Basic Wind Speeds The following wind speeds were recommended after carrying out the analysis: Table 5 Station Basic Hourly Wind Speeds STATIONS Basic Wind Speed(m/s) KABETE 16.34 JKIA 18.94 WILSON 22.62 DAGORETTI 18.62 EASTLEIGH 20.19 MACHAKOS 19.06 THIKA 24.56 NAROK 24.02 IV. CONCLUSION The findings here show the basic wind speeds from different meteorological stations in Nairobi County. The time period involved was a maximum of 12 years which is quite short. This was mainly due to lack of data for longer periods and also for other regions within the country. Better and more accurate basic wind speeds can be obtained if more wind speed data is made available. However, this paper shows the method that should be adopted to determine basic wind speeds for different regions and even for the whole country. Structural Engineers in Kenya have been using a basic wind speed of 28m/s as the mean hourly wind speed. From the analysis above this is higher than the mean hourly speeds at all the wind speeds within Nairobi County. However a more comprehensive wind speed analysis using data collected over a longer period e.g. 20 years should be carried out so as to obtain more accurate results. REFERENCES [1]. Ove Arup & Partners Ltd, Department of the Environment, Heritage and Local Government. Irish National Annex to the Wind Eurocode (EN 1991-1-4), Derivation of the Wind Map, 2009 [2]. ENGINEERING SCIENCES DATA UNIT. Strong winds in the atmospheric boundary layer. Part 1: Mean hourly wind speeds. Engineering Sciences Data Item 82026. London: ESDU International, 1990. [3]. Wind Loading and Wind-Induced Structural Response,Report by the Committee on Wind Effects of the Committee on Dynamic Effects of the Structural Division, American Society of Civil Engineers, New York, N.Y, 1987.
10.
International Journal of
Engineering Sciences & Emerging Technologies, Oct. 2014. ISSN: 22316604 Volume 7, Issue 2, pp: 631-640 ©IJESET 640 [4]. Wind loading and Wind-Induced Structural Response, Report by the Committee on Wind Effects of the Committee on Dynamic Effects of the Structural Division, American Society of Civil Engineers, New York, 1987.
Download now