Fan Control Mac Os X El Capitan; About author. No comments so far. Be first to leave comment below. Your email address will not be published. Required fields are marked. Post comment. Notify me of follow-up comments by email. Notify me of new posts by email.

• Fang In Tropical Valley Mac Os X
• Fang In Tropical Valley Mac Os Catalina

• < Previous
• Next >

• This PDF was prepared from a copy downloaded from the journal s website. The page images (originally 200 dpi) were extracted and the images sharpened and resampled to 300 dpi using PhotoZoom Pro 7 running under Mac OS X 10.11.6. The new page images were.
• Best Mac Fan control app? I used to use SMC Fan Control but it's not updated for the latest MAc OS. The one I'm using now 'Macs Fan Control', i dont like at all. It constantly forgets to turn on, it doesnt give me quick options to just spin up the fans, etc.
• Mac OS X 10.8+ (Mountain Lion) Overview The forest-covered valley surrounded by vast mountains amazes with its scale from a bird’s-eye view and is extremely detailed down to every leaf and flower petal.
• Download Unigine Valley Benchmark - Valley Benchmark is a new GPU stress-testing tool from the developers of the very popular and highly acclaimed Heaven Benchmark.

All Dissertations

Fang In Tropical Valley Mac Os X

Date of Award

December 2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Civil Engineering

Fang In Tropical Valley Mac Os Catalina

Committee Member

Yaojun Ge

Committee Member

Weichiang Pang

Committee Member

Yaojun Ge

Committee Member

Thomas E Cousins

Committee Member

Nigel B Kaye

Abstract

Typhoon or hurricane or tropical cyclone, which is a large-scale air rotating system around a low atmospheric pressure center, frequently causing devastating economic loss and human casualties along coastal regions due to violent winds, heavy rainfall, massive storm surges, flash flooding or even landslides in mountainous areas. The coastal region of China, which is characterized by high population densities and well-developed cities, is always exposed to typhoon threats with 7~8 landfall typhoons every year since Western Pacific Basin is the most active typhoon basin on earth, accounting for almost one-third of global annual storms. With more long-span bridges are being constructed along this coastal area, it is of great importance to perform the risk assessments on these flexible or wind-sensitive structures subjected to typhoon winds.

To reconstruct the mean typhoon wind speed field, a semi-analytical height-resolving typhoon boundary layer wind field model, including a parametric pressure model and an analytical wind model was first developed in Chapter 2 using a scale analysis technique. Some basic characteristics of the inner structure of typhoon wind field, such as the logarithmic vertical wind profile near the ground and super-gradient winds were reproduced. Then, Chapter 3 develops a dataset of two wind field parameters, i.e. the radius to maximum wind speed, R_(max,s) and the Holland pressure profile parameter, B_s in Western Pacific Ocean using the wind data information from best track dataset archived by the Japan Meteorological Agency (JMA) coupled with the present wind field model. The proposed dataset of R_(max,s) and B_s is able to reproduce the JMA wind observations as closely as possible, which allows performing more accurate typhoon wind hazard estimation. On this basis, the maximum wind hazard footprints for over-water, roughness only and roughness and topography combined conditions of 184 observed landed or offshore typhoon-scale storms are generated and archived for risk assessment. Moreover, this supplementary dataset of R_(max,s) and B_s enables the development of recursive models to facilitate both sub-region typhoon simulations and full track simulations.

Since the present wind field model can only generate long-time-duration speed, say 10-min mean wind speed, Chapter 4 develops an algorithm to compute the gust factor curve by taking the non-stationary and non-Gaussian characteristics of typhoon winds into account. The real wind data of nine typhoons captured by the structural health monitoring system (SHMS) installed in Xihoumen Bridge were utilized to validate the proposed model. Then, the probability distributions of gust factor associated with any gust time duration of interest can be readily achieved after introducing the statistical models of skewness and kurtosis of typhoon winds.

To predict the typhoon wind hazard along the coastal region of China, a geographically-weighted-regression (GWR) -based subregion model was proposed in Chapter 5. The storm genesis model was first applied to a circular boundary around the site of interest. Then, the typhoon forward model including the tracking model, intensity model, and wind field parameter model was developed utilizing the GWR method. A series of performance assessments were performed on the present subregion model before it was employed to predict the typhoon wind hazards around the coastal regions of China.

Chapter 6 develops a framework to investigate the probabilistic solutions of flutter instability in terms of critical wind speed accounting for multiple resources of uncertainty to facilitate the development of the fragility curve of flutter issue of long-span bridges. The quantifications of structural uncertainties, as well as aerodynamic uncertainties or the randomness of flutter derivatives, were conducted using both literature survey and experimental methods. A number of probabilistic solutions of flutter critical wind speed for two bridges, say a simply supported beam bridge and the Jiangyin Suspension Bridge were achieved by introducing different sources of uncertainty utilizing both 2D step-by-step analysis and 3D multimode techniques.

To examine the flutter failure probability of long-span bridge due to typhoon winds, a case study of a 1666-m-main-span suspension bridge located in the typhoon-prone region was performed. The fragility curves of this bridge in terms of critical wind speed and the typhoon wind hazards curves of the bridge site as the probability of occurrence with respect to any years of interest were developed, respectively by exploiting the techniques achieved in previous chapters. Then a limit state function accounting for the bridge-specific flutter capacity and the site-specific mean typhoon wind hazard as well as the gust factor effects was employed to determine the flutter failure probabilities utilizing Monte Carlo simulation approach.

Recommended Citation

Fang, Genshen, 'Typhoon Wind Modeling and Flutter Fragility Analysis of Long-Span Bridges in Coastal Regions of China' (2019). All Dissertations. 2529.
https://tigerprints.clemson.edu/all_dissertations/2529

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.