Thermal Performance Improvement of Tractor Radiator Using CuO/Water Nanofluid

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 effective density, which is a parameter of concentration of the nanofluid, is given by

ρ_{n f} = (1 - φ) ρ_{b f} + φ ρ_{p} .

…”

Section: Physical Properties/mathematical Modelmentioning

confidence: 99%

“…The specific heat of the nanofluid is a function of concentration of the nanofluid and specific heat of the base fluid

{(C_{p} ρ)}_{n f} = {((), ((), 1 - φ) C_{p})}_{b f} + φ (C_{p} ρ_{p})

…”

Section: Physical Properties/mathematical Modelmentioning

confidence: 99%

Effect of carboxyl graphene nanofluid on automobile radiator performance

Sumanth

Rao

Krishna

et al. 2018

Nanofluid is a promising solution for the improvement of the radiator performance. In the current research work, the effect of the nanofluid carboxyl graphene on the performance of a radiator is studied. Carboxyl graphene nanoplatelets are added to 50:50 ethylene glycol–distilled water at three concentrations of 0.02, 0.03, and 0.04 vol%. The liquid flow rate is varied from 3 liters per minute (LPM) to 6 LPM, and the inlet liquid to the radiator has been maintained at constant temperatures of 40 °C and 50 °C. The inlet air Reynolds number is varied between 1200 and 2500. The effects of these on performance parameters such as Nusselt number, effectiveness, and friction factor are investigated. It is observed that addition of carboxyl graphene nanoplatelets increases the Nusselt number and effectiveness of radiator while friction factor is unaltered. The effectiveness of radiator increases by 27.38% and 23.41% for inlet temperatures of 40 °C and 50 °C respectively at 0.02 vol% and 5 LPM flow rate.

“…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.