Sentence examples for mean wall shear from inspiring English sources

Velocity fields and spatial variations in mean wall shear stress (WSS) and oscillatory shear index (OSI) are presented.

We measured the abdominal aortic blood flow at rest and during exercise, and quantified mean wall shear stress (MWSS), oscillatory shear index (OSI), and particle residence time (PRT).

It was pointed out that mean wall shear stress and mean flow velocity have an influence on the cleaning progress.

A mass transfer correlation relating limiting UF flux with the mean wall shear stress has been obtained.

Localization of atherosclerotic lesions in the abdominal aorta has been previously correlated to areas of adverse hemodynamic conditions, such as flow recirculation, low mean wall shear stress, and high temporal oscillations in shear.

Compared to the younger subjects, the older subjects also experienced greater increases in mean wall shear stress and greater decreases in wall shear stress oscillations from rest to exercise.

Fig. 2 Biofilm thickness versus time at different wall-shear stresses, COD = 400 mg/L (F means wall-shear stress, Pa).

Under the conditions of this experiment, the thicknesses of the biofilms were 2.4 ± 0.1, 2.7 ± 0.1 and 2.2 ± 0.1 mm at shear stresses of 0.8, 1.29 and 2.0 Pa, respectively Fig. 3 Variation of biomass density at different wall-shear stresses; COD = 400 mg/L (F means wall-shear stress, Pa).

The root mean square error can be calculated by: {text{RMSE}} = sqrt {frac{{sumlimits_{i = 1}^{n} {left( {x_{ip} - x_{im} } right)^{2} } }}{n}}, (20 where x ip is the mean wall or bed shear stress predicted by the models, x im is the value of wall or bed shear stress obtained from the experimental results, and n is the number of observations.

Secondly, mean and fluctuating wall shear stress measurements were made using the electrochemical method.

Two soft computing methods were extended in order to predict the mean wall and bed shear stress in open channels.