In this article, we consider the effects of double diffusion on magnetohydrodynamics (MHD) Carreau fluid flow through a porous medium along a stretching sheet. Variable thermal conductivity and suction/injection parameter effects are also taken into the consideration. Similarity transformations are utilized to transform the equations governing the Carreau fluid flow model to dimensionless non-linear ordinary differential equations. Maple software is utilized for the numerical solution. These solutions are then presented through graphs. The velocity, concentration, temperature profile, skin friction coefficient, and the Nusselt and Sherwood numbers under the impact of different parameters are studied. The fluid flow is analyzed for both suction and injection cases. From the analysis carried out, it is observed that the velocity profile reduces by increasing the porosity parameter while it enhances both the temperature and concentration profile. The temperature field enhances with increasing the variable thermal conductivity and the Nusselt number exhibits opposite behavior.

In this paper, the process of natural convection heat transfer (NCHT) and production of entropy (POF) in a portion of a tube has been modeled two-dimensionally. The examined problem is a quarter-tubular enclosure (quarter of a tube) which is filled with water-alumina nanofluid and subjected to a magnetic field (MF) of strength B0 at angle relative to horizon. Lattice Boltzmann Method (LBM) is used to simulate this problem. The ranges of parameters used in this investigation are: 0 < ω < 90, 0 < Ha < 60, and 0.1 < L, H < 0.5, and the obtained results include the Nusselt number (Nu), generated entropy, and Bejan number (Be). The results of thermal and dynamic analyses indicate that by growing the Hartmann number (Ha), the NCHT and POF values go up and the Bediminishes. Heat transfer is also improved by increasing the length of the enclosure’s hot walls. The highest amount of heat transfer occurs at the MF angle of 60º, and it is 10.3% greater than the amount of heat transfer occurring at horizontal MF. Finally, an artificial neural network (ANN) was used to simulate the cavity performance based on these parameters. An optimization is performed on the parameters of heat source length and Ha. The optimization is aimed at finding suitable parameter values that lead to the highest heat transfer rate and lowest POF. A table listing a number of optimal points has been presented at the end of the paper.

No abstract

<abstract><p>In this paper, the notion of fuzzy AG-subgroups is further extended to introduce fuzzy cosets in AG-groups. It is worth mentioning that if $ A $ is any fuzzy AG-subgroup of $ G $, then $ \mu_{A}(xy) = \mu_{A}(yx) $ for all $ x, \, y\in G $, i.e. in AG-groups each fuzzy left coset is a fuzzy right coset and vice versa. Also, fuzzy coset in AG-groups could be empty contrary to fuzzy coset in group theory. However, the order of the nonempty fuzzy coset is the same as the index number $ [G:A] $. Moreover, the notions of fuzzy quotient AG-subgroup, fuzzy AG-subgroup of the quotient (factor) AG-subgroup, fuzzy homomorphism of AG-group and fuzzy Lagrange's theorem of finite AG-group is also introduced.</p></abstract>