Mixed convection of Al2O3–H2O nanoliquid in a square chamber with complicated fin

Shulepova, Elena V.; Sheremet, Mikhail А.; Öztop, Hakan F.; Abu‐Hamdeh, Nidal H.

doi:10.1016/j.ijmecsci.2019.105192

Cited by 67 publications

(18 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For definition of the stream function magnitude at the inner body boundary the special procedure was used [29]. Namely, we introduce the condition that the pressure p should be single-valued along the internal block surface.…”

Section: Numerical Techniquementioning

confidence: 99%

Thermal Convection of Nanoliquid in a Double-Connected Chamber

2020

Self Cite

View full text Add to dashboard Cite

Thermogravitational convective thermal transmission, inside a square differentially-heated chamber with a nanoliquid, has been examined in the presence of internal adiabatic or a thermally-conducting solid body. A single-phase nanoliquid approach is employed, based on the experimentally-extracted relations for nanofluid heat conductivity and dynamic viscosity. The governing equations have been written using non-primitive parameters such as stream function and vorticity. Such approach allows a decrease in computational time due to a reduction of equation numbers. One of the main challenges in such a technique is a determining the stream function magnitude at the inner body walls. A solution of this problem has been described in detail in this paper. Computational scrutinizing has been performed by employing the finite difference technique. The mesh sensitivity analysis and comparison with theoretical and experimental results of other researchers have been included. An influence of the Rayleigh number, nanoparticles concentration, internal block size, heat conductivity ratio and non-dimensional time on nanofluid motion and energy transport has been studied.

show abstract

Section: Numerical Techniquementioning

confidence: 99%

Thermal Convection of Nanoliquid in a Double-Connected Chamber

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, for the sake of making the most appropriate choice, it is of great necessity to take the comprehensive performance index to appraise the heat transfer and pressure drop. Figure 14 indicates the comprehensive evaluation index computed by Equation (14) to appraise the flow enhancement degree of heat transfer is larger than that of the resistance.…”

Section: Comprehensive Evaluationmentioning

confidence: 99%

“…Bondarenko et al 13 discovered that the heater which is situated near the vertical cold wall is beneficial to strengthening the cooling performance. In addition, a lot of researchers implemented investigations on the free convection in a square chamber with complicated fin, 14 a 3D cavity with an inner T‐shaped adiabatic obstacle, 15 a cavity with a corrugated partition, 16 and an enclosure with V‐shaped fins 17 . The kinds and shapes of nanoparticles also determine the heat transfer capacity of fluids.…”

Section: Introductionmentioning

confidence: 99%

Effects of metal foam on exergy and entropy of nanofluids in a heat sink applied for thermal management of electronic components

Chen

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

To solve the high heat dissipation of the heat sink, a set of experimental device for the flow and heat transfer of the nanofluids through a heat sink filled with metal foam was established in this paper. The impacts of metal foam with diverse pore densities (PPI = 20, 30, 40), nanoparticle mass fractions (from β = 0.0 wt% to β = 0.5 wt%) and Reynolds numbers (Re = 414-1119) on the flow and heat transfer characteristics were investigated. In addition, the thermal and hydraulic properties were evaluated by the exergy efficiency and entropy generation. The conclusions displayed that the metal foam with a pore density (PPI = 40) is instrumental in strengthening the heat transfer under a certain porosity, and the heat sink under the working condition (β = 0.3 wt% and PPI = 40) possesses the best capacity of thermal performance. Nanofluids and metal foam (PPI = 40) can improve Nusselt number by 6.13% and 3.2% at most compared with water and metal foam (PPI = 20 and PPI = 30), respectively. Nanofluids with β = 0.3 wt% exhibit the highest exergy efficiency, and the metal foam with PPI = 40 shows the smallest entropy generation.

show abstract

“…Recently, numerous endeavors have been made in order to enhance the performance of various applications with the help of nanotechnology [1][2][3][4]. As an illustration, it is practicable to reduce system size or enhance the thermal performance of materials [5][6][7][8]. In this way, some investigations have been implemented on the use of nanotechnology in thermal applications [9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

et al. 2020

View full text Add to dashboard Cite

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

show abstract

Mixed convection of Al2O3–H2O nanoliquid in a square chamber with complicated fin

Cited by 67 publications

References 50 publications

Thermal Convection of Nanoliquid in a Double-Connected Chamber

Thermal Convection of Nanoliquid in a Double-Connected Chamber

Effects of metal foam on exergy and entropy of nanofluids in a heat sink applied for thermal management of electronic components

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

Contact Info

Product

Resources

About