Study of Couple Stresses on MHD Poiseuille Flow through Porous Medium in a parallel plate channel
In this paper a mathematical analysis of effect of couple stresses on magneto hydrodynamic (MHD) flow past a parallel plate channel of viscous, incompressible and electrically conducting fluid in the presence of porous medium has been studied. An exact solution of the dimensionless governing equations has been obtained by using method of perturbation technique. The effects on velocity, discharge between the plates and wall shear are studied for different parameters like Hartmann number, Reynolds number, pulsation, time, porosity parameter, pulsation frequency and reported graphically to show the interesting aspect of the solution.
Peristaltic flow of blood under effect of a magnetic field in a non-uniform channels
The effect of a magnetic field on peristaltic transport of blood in a non-uniform two-dimensional channels has been investigated under zero Reynolds number with long wavelength approximation. Blood is represented by a viscous, incompressible and electrically conducting couple-stress fluid (a fluid which its particles size are taken into account, a special case of a non-Newtonian fluid). It is found that the pressure rise decreases as the couple-stress fluid parameter @c increases (i.e. small size fluid particle) and increases as the Hartmann number M increases. So the pressure rise for a couple-stress fluid (as a blood model) is greater than that for a Newtonian fluid and is smaller for a magnetohydrodynamic fluid than for a fluid without an effect of a magnetic field. Finally, the maximum pressure rise (Q=0) increases as M increases and @c decreases, and the effect of the Hartmann number M is more obvious (for the same (@[email protected]?"L)"m"a"x".) as the couple-stress parameter @c increases (Newtonian fluid).
Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation
In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.