Model for MHD viscoelastic nanofluid flow with prominence effects of radiation

Dispersions of oil in water are encountered in a variety of industrial processes leading to a reduction in the performance of the heat exchangers when thermally treating such two phase fluids. This reduction is mainly due to changes in the thermal and hydrodynamical behavior of the two phase fluid. In the present work, an experimental investigation was performed to study the effects of light oil fouling on the heat transfer coefficient in a double-pipe heat exchanger under turbulent flow conditions. The effects of different operating conditions on the fouling rate were investigated including: hot fluid Reynolds number (the dispersion), cold fluid Reynolds number, and time. The oil fouling rate was analyzed by determining the growth of fouling resistance with time and through pressure drop measurements. The influence of copper oxide (CuO) nanofluid on the fouling rate in the dispersion was also determined. It was found that the presence of dispersed oil causes a reduction in heat transfer coefficient by percentages depending on the Reynolds number of both cold and hot fluids and the concentration of oil. In addition, the time history of fouling resistance exhibited different trends with the flow rates of both fluids and its trend was influenced appreciably by the presence of CuO nanofluid. K E Y W O R D S copper oxide, dispersion, double-pipe heat exchanger, fouling, heat transfer, nanofluid, oil Heat Transfer-Asian Res.

Section: Introductionmentioning

confidence: 99%

Oil fouling in double‐pipe heat exchanger under liquid‐liquid dispersion and the influence of copper oxide nanofluid

Majdi

Alabdly

Hasan

et al. 2019

“…The nonlinear coupled differential Equations (8)(9)(10)(11) with boundary conditions given in Equation (12) are solved numerically using the Runge-Kutta fourth order method followed by the shooting technique. First, the differential Equations (8)(9)(10)(11) are transformed into a system of ordinary differential equation of first order and the missing initial conditions are then obtained by usual the Newton shooting approach and are describe as follows:…”

Section: Similarity Transformmentioning

confidence: 99%

“…For the enhancement of thermal performance melting and solidification processes of phase change material has been discussed by Patel and Rathod . Hussain et al investigated the influence of radiation on magnetohydrodynamic (MHD) viscoelastic nanofluid flow. Effect of oscillating magnetic field on the heat and mass transfer of ferrofluid has been studied by Hassan et al Furthermore, Bhatti et al worked on the peristaltic propulsion of two‐phase flow through a Darcy‐Brinkman‐Forchheimer porous medium.…”

Section: Introductionmentioning

confidence: 99%

Free convective MHD micropolar fluid flow with thermal radiation and radiation absorption: A numerical study

Pradhan

Baag

Mishra

et al. 2019

An analysis is carried out for the free convective flow of an electrically conducting micropolar fluid through permeable stretching sheet in the presence of porous medium. Inclusion of thermal radiation to the energy equation enhances the thermal properties of polar fluid. In addition to that the radiation absorption parameter occurs due to the interaction of solutal concentration difference also considered in the heat transfer equation. Suitable similar transformation is used to convert the governing partial differential equations to ordinary differential equations. Furthermore, though analytical solutions of these complex nonlinear coupled equations are more complicated therefore, numerical solution such as Runge‐Kutta fourth order method associated with shooting technique is adopted. Behavior of characterizing parameters for the flow phenomena are presented via graphs and computed values of physical quantities of interest are obtained and shown in tabular form. Present result validates with that of earlier results in particular case which confirms the existence of present solution methodology. However, the main findings of contributing parameters are laid down as; angular velocity profile contributes a dual character from the point of contact at the middle of the channel. Fluid temperature is affected by the inclusion of absorption coefficient.

“…Nanofluids have demonstrated better thermal characteristics; thus they are suitable for electronic cooling systems, car/tractor radiator, heat exchanger liquids, solar systems, and nuclear reactors. Several cases related to the heat transfer of nanofluids have been presented …”

Section: Introductionmentioning

confidence: 99%

“…Several cases related to the heat transfer of nanofluids have been presented. [18][19][20][21][22][23][24][25][26][27][28] According to the previous studies about the natural convection flow inside enclosures, investigating the effect of deviation angle of enclosure on flow field, heat transfer, and entropy generation of turbulent natural convection in a tall enclosure has not been noticed recently. Consequently, this study investigates this issue by using the computational fluid dynamicsartificial neural network (CFD-ANN) hybrid method.…”

mentioning

confidence: 99%

Study of flow field, heat transfer, and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics‐artificial neural network hybrid method

Sepehrnia

Sheikhzadeh

Abaei

et al. 2019

In this study, the turbulent natural convection of Ag‐water nanofluid in a tall, inclined enclosure has been investigated. The main objective of this study is finding the optimized angle of the enclosure with operational boundary condition in cooling from ceiling utilizing the computational fluid dynamics‐artificial neural network (CFD‐ANN) hybrid method, which has not been noticed in previous studies. To achieve this, we proposed two approaches. First, the simulations have been done with a deviation angle of 0 to 90° by using water and Ag‐water nanofluid. And second, a new prediction approach is proposed based on radial basis function artificial neural networks (RBF‐ANN) to predict the mean Nusselt number and entropy generation with the variation of Rayleigh numbers, deviation angles, and volume fractions as inputs. The results from the first approach indicate that the Rayleigh number has a considerable function in the determination of optimized angle. The results from the second approach, which used the first approach simulation results as training data set, could predict the mean Nusselt number and entropy generation with 1.4577e−022 and 1.552e−015 mean square error, respectively. Moreover, a new set of data for Rayleigh numbers, deviation angles, and volume fractions were used to test the performance of the prediction model, which shows promising and superior prospects for RBF‐ANN.