The shape of the probability density function of short-term concentration fluctuations of plumes in the atmospheric boundary layer

1990 ◽  
Vol 51 (3) ◽  
pp. 269-298 ◽  
Author(s):  
Eugene Yee
Keyword(s):  
Boundary Layer ◽  
Probability Density Function ◽  
Atmospheric Boundary Layer ◽  
Probability Density ◽  
Density Function ◽  
Concentration Fluctuations ◽  
Short Term

Methods of Computing the Probability of Failure Under Extreme Values of Bending Moment

1972 ◽  
Vol 16 (02) ◽  
pp. 113-123
Author(s):  
Alaa Mansour
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Extreme Values ◽  
Bending Moment ◽  
Random Variable ◽  
Probability Of Failure ◽  
Short Term ◽  
Term Analysis

Methods for predicting the probability of failure under extreme values of bending moment (primary loading only) are developed. In order to obtain an accurate estimate of the extreme values of the bending moment, order statistics are used. The wave bending moment amplitude treated as a random variable is considered to follow, in general, Weibull distribution so that the results could be used for short-term as well as long-term analysis. The probability density function of the extreme values of the wave bending moment is obtained and an estimate is made of the most probable value (that is, the mode) and other relevant statistics. The probability of exceeding a given value of wave bending moment in "n" records and during the operational lifetime of the ship is derived. Using this information, the probability of failure is obtained on the basis of an assumed normal probability density function of the resistive strength and deterministic still-water bending moment. Charts showing the relation of the parameters in a nondimensional form are presented. Examples of the use of the charts for long-term and short-term analysis for predicting extreme values of wave bending moment and the corresponding probability of failure are given.

scholarly journals Concentration Fluctuations and Odor Dispersion in Lagrangian Models

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 27 ◽  
Author(s):  
Enrico Ferrero ◽  
Alon Manor ◽  
Luca Mortarini ◽  
Dietmar Oettl
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Stochastic Models ◽  
Scientific Community ◽  
Higher Order ◽  
Concentration Fluctuations ◽  
Lagrangian Stochastic Models ◽  
Odor Annoyance ◽  
Odor Dispersion

In this paper, a review of the Lagrangian stochastic models developed in the last decades for the simulation of the concentration–fluctuation dispersion is presented. The main approaches available in the literature are described and their ability in reproducing the higher order moments of the probability density function is discussed. Then, the Lagrangian approaches for evaluating of the odor annoyance are presented. It is worth to notice that, while Lagrangian stochastic models for mean concentrations are well-known and their ability in correctly reproducing the observation is well assessed, concerning concentration fluctuations the approaches are often new and unknown for most of the scientific community.

scholarly journals First-Order Statistical Characteristics of Macrodiversity System with Three Microdiversity MRC Receivers in the Presence of k-µ Short-Term Fading and Gamma Long-Term Fading

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Branimir Jaksic ◽  
Mihajlo Stefanovic ◽  
Danijela Aleksic ◽  
Dragan Radenkovic ◽  
Sinisa Minic
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Output Signal ◽  
Multipath Fading ◽  
Statistical Characteristics ◽  
Cumulative Density Function ◽  
Short Term ◽  
Maximum Ratio Combining

Macrodiversity system with macrodiversity SC receiver and three microdiversity MRC (maximum ratio combining) receivers is considered. Independent k-μ short-term fading and correlated Gamma long-term fading are present at the inputs of microdiversity MRC receivers. For this model, the probability density function and the cumulative density function of microdiversity MRC receivers and macrodiversity SC receiver output signal envelopes are calculated. Influences of Gamma shadowing severity, k-μ multipath fading severity, Rician factor and correlation coefficient at probability density function, and cumulative density function of macrodiversity SC receiver output signal envelopes are graphically presented.

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