Thermal and hydraulic characteristics of a minichannel heat exchanger operated with a non-Newtonian hybrid nanofluid

Bahiraei, Mehdi; Godini, Ali; Shahsavar, Amin

doi:10.1016/j.jtice.2018.01.014

Cited by 57 publications

(11 citation statements)

References 20 publications

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“…A maximum enhancement of ~ 24% for the largest concentration of nanoparticles was reported. Similar findings were also demonstrated by Bahraei et al [55] by doing some experiments on a mini-channel heat exchanging system. They reported 53% enhancement in the HTC of nanofluid.…”

Section: Introductionsupporting

confidence: 88%

Thermal performance of a micro heat exchanger (MHE) working with zirconia-based nanofluids for industrial cooling

Nikkhah

Nakhjavani

2019

Int J Ind Chem

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An experimental investigation was performed with the view to assess the heat transfer characteristics of a water-based nanofluid in a micro heat exchanger employed to quench a high heat flux heater for industrial and microelectronic cooling applications. The experiments were conducted at heat fluxes 10-70 kW/m 2 and for nanofluids at various mass concentrations of 0.1-0.3% and passing flow rates of 0.1-5 l/min. Thermo-physical properties of the nanofluid including thermal conductivity, heat capacity, density and viscosity of nanofluid were experimentally measured at 40 °C close to the temperature of the experiments. Results showed that the heat transfer coefficient and pressure drop were augmented by 40.1% and 67% at wt% = 0.3 compared to the base fluid, respectively. The enhancement in the heat transfer coefficient was associated with the improvement in the thermal conductivity of the base fluid together with the intensification of Brownian motion and thermophoresis effect. The increase in the pressure drop was also attributed to the increase in the viscosity of the working fluid which induces layer-layer frictional forces in the bulk of the coolant in micro heat exchanger.

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Section: Introductionsupporting

confidence: 88%

Thermal performance of a micro heat exchanger (MHE) working with zirconia-based nanofluids for industrial cooling

Nikkhah

Nakhjavani

2019

Int J Ind Chem

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“…Bahiraei et al [18] hybrid nanofluid containing coated Fe 3 O 4 /CNT Double-tube heat exchanger Maximum heat transfer enhancement: 53.8%…”

Section: Researchers Nanofluid Type Configuration Type Main Resultsmentioning

confidence: 99%

Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

et al. 2020

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Nanofluids in minichannels with various configurations are applied as cooling and heating fluids. Therefore, it is essential to have an optimal design of minichannels. For this purpose, a square channel with a cylinder in the center connected to wavy fins at various concentrations of an Al2O3 nanofluid is simulated using the finite volume method (FVM). Moreover, central composite design (CCD) is used as a method of design of experiment (DOE) to study the effects of three input variables, namely the cylinder diameter, channel width, and fin radius on the convective heat transfer and pumping power. The impacts of the linear term, together with those of the square and interactive on the response variables are determined using Pareto and main effects plots by an ANOVA. The non-dominated sorting genetic algorithm-II (NSGA-II), along with the response surface methodology (RSM) is applied to achieve the optimal configuration and nanofluid concentration. The results indicate that the effect of the channel width and cylinder diameter enhances about 21% and 18% by increasing the concentration from 0% to 5%. On the other hand, the pumping power response is not sensitive to the nanofluid concentration. Besides, the channel width has the highest and lowest effect on the heat transfer coefficient (HTC) and pumping power, respectively. The optimization for a concentration of 3% indicates that in Re = 500 when the geometry is optimized, the HTC enhances by almost 9%, while the pumping power increases by about 18%. In contrast, by increasing the concentration from 1% to 3%, merely an 8% enhancement in HTC is obtained, while the pumping power intensifies around 60%.

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“…Nanofluids are defined as fluids that contain nanometer-sized particles (less than 100 nanometers (nm) in size), which are suspended in base fluids to enhance their convectional heat transfer. Nanofluidic problems of the flow and heat transfer characteristics are important from a theoretical as well as a practical point of view, and they have been extensively studied in applied sciences and various engineering applications, such as thermal power generation systems, the cooling of a large metallic plate in a bath, fiber spinning, glass blowing, melt spinning, wire coating dynamics and the extrusion of material through a die [1]. Choi and Eastman [2] were among the first to introduce nanoparticles to the fluid system.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

et al. 2022

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This study investigated the steady two-phase flow of a nanofluid in a permeable duct with thermal radiation, a magnetic field, and external forces. The basic continuity and momentum equations were considered along with the Buongiorno model to formulate the governing mathematical model of the problem. Furthermore, the intelligent computational strength of artificial neural networks (ANNs) was utilized to construct the approximate solution for the problem. The unsupervised objective functions of the governing equations in terms of mean square error were optimized by hybridizing the global search ability of an arithmetic optimization algorithm (AOA) with the local search capability of an interior point algorithm (IPA). The proposed ANN-AOA-IPA technique was implemented to study the effect of variations in the thermophoretic parameter (Nt), Hartmann number (Ha), Brownian (Nb) and radiation (Rd) motion parameters, Eckert number (Ec), Reynolds number (Re) and Schmidt number (Sc) on the velocity profile, thermal profile, Nusselt number and skin friction coefficient of the nanofluid. The results obtained by the designed metaheuristic algorithm were compared with the numerical solutions obtained by the Runge–Kutta method of order 4 (RK-4) and machine learning algorithms based on a nonlinear autoregressive network with exogenous inputs (NARX) and backpropagated Levenberg–Marquardt algorithm. The mean percentage errors in approximate solutions obtained by ANN-AOA-IPA are around 10−6 to 10−7. The graphical analysis illustrates that the velocity, temperature, and concentration profiles of the nanofluid increase with an increase in the suction parameter, Eckert number and Schmidt number, respectively. Solutions and the results of performance indicators such as mean absolute deviation, Theil’s inequality coefficient and error in Nash–Sutcliffe efficiency further validate the proposed algorithm’s utility and efficiency.

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Thermal and hydraulic characteristics of a minichannel heat exchanger operated with a non-Newtonian hybrid nanofluid

Cited by 57 publications

References 20 publications

Thermal performance of a micro heat exchanger (MHE) working with zirconia-based nanofluids for industrial cooling

Thermal performance of a micro heat exchanger (MHE) working with zirconia-based nanofluids for industrial cooling

Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

Analysis of Nanofluid Particles in a Duct with Thermal Radiation by Using an Efficient Metaheuristic-Driven Approach

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