Numerical Simulation of MHD Fluid Flow inside Constricted Channels using Lattice Boltzmann Method

In this study, the electrically conducting fluid flow inside a channel with local symmetric constrictions, in the presence of a uniform transverse magnetic field is investigated using Lattice Boltzmann Method (LBM). To simulate Magnetohydrodynamics (MHD) flow, the extended model of D2Q9 for MHD has been used. In this model, the magnetic induction equation is solved in a similar manner to hydrodynamic flow field which is easy for programming. This extended model has a capability of simultaneously solving both magnetic and hydrodynamic fields; so that, it is possible to simulate MHD flow for various magnetic Reynolds number (Rem). Moreover, the effects of Rem on the flow characteristics are investigated. It is observed that, an increase in Rem, while keeping the Hartman number (Ha) constant, can control the separation zone; furthermore, comparing to increasing Ha, it doesn't result in a significant pressure drop along the channel

Forced convection around horizontal tubes bundles of a heat exchanger using a two-phase mixture model: Effects of nanofluid and tubes Configuration

In this paper, numerical simulation of laminar flow and heat transfer of nanofluid on a group of heat exchanger tubes is described. For better prediction of the behavior of the nanofluid flow on the tube arrays, two-phase mixture model was used. To achieve this aim, heat transfer and laminar flow of two-phase nanofluid as cooling fluid at volume fraction of 0, 2, 4, and 6% solid nanoparticles of silver and Reynolds numbers of 100 to1800 were investigated for different Configurations of tube arrays. The results indicated when the nanofluid collides with the tube arrays, the growth of heat boundary layer and gradients increase. The increase in the growth of boundary layer in the area behind the tubes was very remarkable, such that at the Reynolds number of 100, due to diffusion of the effect of wall temperature in the cooling fluid close to the wall, it had a considerable growth. Further, from the second row onwards, the slope of pressure drop coefficient diagrams was descending. Among the different Configuration s of tubes and across all the investigated Reynolds numbers, square Configuration had the maximum pressure drop coefficient as well as the highest extent of fluid momentum depreciatio

Effects of graphene oxide‑silicon oxide hybrid nanomaterials on rheological behavior of water at various time durations and temperatures: Synthesis, preparation and stability

The present empirical study investigates the synthesis of graphene oxide nanoparticles, preparation of water/graphene oxide‑silicon oxide hybrid nanofluid, and parameters affecting viscosity of the nanofluid. Graphene oxide nanoparticles are synthesized using the modified Hummer's method. Surface structure and atomic structure of the nanoparticles were studied using SEM and XRD tests. The nanofluid was then prepared using the two step method. DLS tests with various patterns were used, in addition to sedimentation photograph capturing method, to measure stability of the nanofluid. Results suggested that the nanofluid has a fairly suitable nanostructure. Viscosity of the nanofluid was measured and studied using Brookfield DV2EXTRA-Pro Viscometer, in the temperature range of 20–60 °C with volume concentrations of 0, 0.5, 0.1, 0.2, 0.4, 0.6, 0.8, and 1%. Furthermore, effects of parameters such as shear rate, and period of applying constant shear rate on viscosity of the nanofluid were investigated. The test results showed that viscosity behavior of the nanofluid is independent of the shear rate and time of shearing. Numerical viscosity measurement results show that viscosity of the nanofluid with volume concentration of φ = 1%, in temperature of 20 °C, increased considerably to μ = 2.42 mPa·s. Viscosity changes ratio increases intensively in higher concentrations. Comparing empirical results of water/graphene oxide nanofluid viscosity to results of the present study shows that, due to the modification of surface structure in nanoparticles, the viscosity values have improved considerably. An empirical equation is provided to measure the viscosity of the nanofluid using this data, which can be used to calculate viscosity of the base fluid under effect of temperature, and viscosity of the nanofluid under effect of temperature and volume concentration variables

A new experimental correlation for non-Newtonian behavior of COOH-DWCNTs/antifreeze nanofluid

In this paper, the rheological behavior of nano-antifreeze consisting of 50%vol. water, 50%vol. ethylene glycol and different quantities of functionalized double walled carbon nanotubes has been investigated experimentally. Initially, nano-antifreeze samples were prepared with solid volume fractions of 0.05, 0.1, 0.2, 0.4, 0.6, 0.8 and 1% using two-step method. Then, the dynamic viscosity of the nano-antifreeze samples was measured at different shear rates and temperatures. At this stage, the results showed that base fluid had the Newtonian behavior, while the behavior of all nano-antifreeze samples was non-Newtonian. Since the behavior of the samples was similar to power law model, it was attempted to find the constants of this model including consistency index and power law index. Therefore, using the measured viscosity and shear rates, consistency index and power law index were obtained by curve-fitting method. The obtained values showed that consistency index amplified with increasing volume fraction, while reduced with enhancing temperature. Besides, the obtained values for power law index were less than 1 for all samples which means shear thinning behavior. Lastly, new correlations were suggested to estimate the consistency index and power law index using curve-fitting

Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data

Regarding the viscosity of the fluids which is an imperative parameter for calculating the required pumping power and convective heat transfer, based on experimental data, an optimal artificial neural network was designed to predict the relative viscosity of multi-walled carbon nanotubes/water nanofluid. Solid volume fraction and temperature were used as input variables and relative viscosity was employed as output variable. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer. The dataset was divided into training and test sets which contained 80 and 20% of data points respectively. The results obtained from the optimal artificial neural network exhibited a maximum deviation margin of 0.28%. Eventually, the ANN outputs were compared with results obtained from the previous empirical correlation and experimental data. It was found that the optimal artificial neural network model is more accurate compared to the previous empirical correlation

Effect of a novel clay/silica nanocomposite on water-based drilling fluids: Improvements in rheological and filtration properties

Drilling fluid is one of the most important components of drilling operation in oil and gas, mining and geothermal industries. Nanotechnology can be used to develop drilling fluid additives that can improve the drilling fluid properties. In this work, the feasibility of using two types of nanoparticle additives in water-based drilling fluid has been investigated. Clay/SiO2 nanocomposite was synthesized (by effective hydrothermal method) and successfully characterised. A series of experiments are performed to evaluate the effect of SiO2 and clay nanoparticles on the rheological and filtration properties of water-base drilling fluids. The experiments are conducted at different concentrations of Clay/SiO2 and SiO2 nanoparticles, and also at a range of temperatures. The results showed that the addition of clay and SiO2 nanoparticles improved the rheological and fluid loss properties. It was also noticed that the nanoparticles provide thermal stability to the drilling fluid. The experimental results suggest that the Clay/SiO2 nanoparticles have a more significant impact on the rheological and fluid loss properties of the drilling fluid comparing to SiO2 nanoparticles, particularly at higher temperatures