Sharif Digital Repository

This paper deals with the investigation of the laminar flow past an elliptical cylinder confined in a channel. In this paper, the Lattice Boltzmann (LB) method is used to simulate flow in two dimensions. The present LB method with the used boundary conditions is validated in simulations of the incompressible flow past a circular cylinder. The simulations are carried out in a range of condition, 0≤θ≤90 (angle of incidence), 5≤Re≤100, (Reynolds number) for AR=0.25,0.5 (aspect ratio). The effects of those parameters on the drag and lift coefficients and other flow characteristics of the cylinder are examined in detail. The results demonstrate that the drag and lift coefficients increase with... 

This paper deals with the investigation of the laminar flow past an elliptical cylinder confined in a channel. In this paper, the Lattice Boltzmann (LB) method is used to simulate flow in two dimensions. The present LB method with the used boundary conditions is validated in simulations of the incompressible flow past a circular cylinder. The simulations are carried out in a range of condition, 0 90 (angle of incidence), 5 Re 100, (Reynolds number) for AR=0.25,0.5 (aspect ratio). The effects of those parameters on the drag and lift coefficients and other flow characteristics of the cylinder are examined in detail. The results demonstrate that the drag and lift coefficients increase with the... 

River water quality monitoring using traditional water sampling and laboratory analyses is expensive and time-consuming. The application of artificial neural network (ANN) models to simulate water quality parameters is cost-effective, quick, and reliable. This study provides two methods of mathematical and ANN modeling to simulate and forecast five important river water quality indicators (DO, TDS, SAR, BOD5, HCO3) correlated with variables such as EC, temperature, and pH which can be measured easily and almost with no cost. The mathematical method is based on polynomial fitting with least square method and the neural network model was developed using a feed-forward algorithm. The 35 years’... 

The present study focuses on simultaneous influence of graphene nanoplatelets (GNP) and hydroxyapatite (HAp) nanopowder on microstructural, wear, tensile and biofunctional behavior of UHMWPE based nanocomposites used in biomedical applications, with the aim to utilize GNP's mechanical strength and wear resistance, while benefitting from HAp's biocompatibility at the same time. 0.1, 0.5 and 1 wt% GNP with 10 wt% optimized concentration of HAp were added to the UHMWPE matrix through an easy two-step approach consisting of solvent mixing and ultrasonication in ethanol as a liquid media. The dried nanocomposite samples of powder were then hot pressed at an optimized temperature and pressure to...