Sentence examples for extreme pressure coefficients from inspiring English sources

This study investigates the interference effects on the tail characteristics of extreme pressure coefficients.

After solving these problems, the mean and extreme pressure coefficients were studied for the distribution of wind-induced pressure.

Systematic wind tunnel tests are conducted, from which a large amount of data is generated to describe the tail characteristics of extreme pressure coefficients under interference effects.

Several thousand extreme pressure coefficients from repeated time-history samples, from a wall tap and a roof tap on a model of the Texas Tech University Building in a simulated atmospheric boundary layer, were used to better determine the appropriate probability distributions for the data.

After the variations of mean, standard deviation and non-Gaussian peak factor are considered, an innovative and analytical PDF formula of extreme wind pressure coefficients for multiple samples, which is expressed as a function of the JPDF of mean, standard deviation and the PDF of non-Gaussian peak factor, is presented in this paper.

Its density is 0.85 g/cm3, dynamic shear force is 15.5 Pa, initial gel strength is 4 Pa, terminal gel strength is 5 Pa and extreme pressure lubrication coefficient is 0.054.

Rather than using peak values, which could vary due to the stochastic nature of the data, a probabilistic analysis is performed to characterize extreme values of pressure coefficients and associated wind loads.

Through the identification of shape parameter of Generalized Extreme Value distribution (GEVD) function and the estimation of fluctuation level of extreme pressures, optimal peak pressure coefficients are predicted to compare with the Cook and Mayne coefficients.

The implicit or explicit translation between non-Gaussian and Gaussian histories can also be utilized to estimate the extreme value of non-Gaussian wind pressure coefficients while the randomness of aerodynamic effect is not taken into account.

The extreme value of non-Gaussian wind pressure coefficients is usually estimated by fitting the probability density function (PDF) of maximum or minimum values while a large number of observations except the peak values in the measured samples are discarded.

The widely used moment-based Hermit model approach has limitations in estimating the extreme value distributions and peak factors of strongly non-Gaussian pressure coefficients.