Nanofluids as secondary fluid in the refrigeration system: Experimental data, regression, ANFIS, and NN modeling

Li, Zhixiong; Renault, Felipe Lemos; Gómez, Abdul Orlando Cárdenas; Sarafraz, M.M.; Khan, H.; Safaei, Mohammad Reza; Filho, Ênio Pedone Bandarra

doi:10.1016/j.ijheatmasstransfer.2019.118635

Cited by 61 publications

(21 citation statements)

References 27 publications

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“…The coefficient of determination is 0.98. The N S is also calculated by using the formulation N S = S gen,total /(ṁ tot .c p ) [36] and the minimum value (N S,minimum = 0.1532) is obtained corresponding to the optimum number of channels. The plot between N S and N is shown in Figure 5.…”

Section: Resultsmentioning

confidence: 99%

Circular Microchannel Heat Sink Optimization Using Entropy Generation Minimization Method

Kumar

Bharj

2020

Journal of Non-Equilibrium Thermodynamics

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The performance of the microchannel heat sink (MCHS) in electronic applications needs to be optimized corresponding to the number of channels (N). In this study optimization of the number of channels corresponding to the diameter of the microchannel ({D_{N}}) using an entropy generation minimization approach is achieved for the MCHS used in electronic applications. The numerical study is performed for constant total heat flow rate ({\dot{q}_{tot}}) and total mass flow rate ({\dot{m}_{tot}}). The results indicate that the dominance of frictional entropy generation ({S_{gen,Fr}}) increases with the reduction in diameter. However, the entropy generation due to heat transfer ({S_{gen,HT}}) decreases with the reduction in diameter. Therefore, the optimum diameter ({D^{\ast }}) is calculated corresponding to the minimum total entropy generation ({S_{gen,total}}) for the optimum number of channels ({N^{\ast }}). Furthermore, the entropy generation number ({N_{S}}) and Bejan number (Be) are also calculated.

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Section: Resultsmentioning

confidence: 99%

Circular Microchannel Heat Sink Optimization Using Entropy Generation Minimization Method

Kumar

Bharj

2020

Journal of Non-Equilibrium Thermodynamics

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“…Its viscosity is calculated via Equation (6) [45]. Further, Equation 7calculates the thermal conduction of water, while its density and specific heat are evaluated by Equations (8) and (9), respectively [1].…”

Section: Nanofluid Propertiesmentioning

confidence: 99%

“…Furthermore, research has been conducted to predict [7][8][9] and optimize [10][11][12] the desired performance of nanofluids in multiple conditions. It should be noted that a few studies have been conducted on the optimization of configuration to obtain the conditions with the expected thermohydraulic attributes.…”

Section: Introductionmentioning

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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“…The data are used to train by gradient descent and least-squares methods. More details of this combination of systems can be found in [43].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

2020

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A precise determination of the mass diffusion coefficient and the mass Biot number is indispensable for deeper mass transfer analysis that can enable finding optimum conditions for conducting a considered process. The aim of the article is to estimate the mass diffusion coefficient and the mass Biot number by applying nondominated sorting genetic algorithm (NSGA) II genetic algorithms. The method is used in drying. The maximization of coefficient of correlation (R) and simultaneous minimization of mean absolute error (MAE) and root mean square error (RMSE) between the model and experimental data were taken into account. The Biot number and moisture diffusion coefficient can be determined using the following equations: Bi = 0.7647141 + 10.1689977s − 0.003400086T + 948.715758s 2 + 0.000024316T 2 − 0.12478256sT, D = 1.27547936·10 −7 − 2.3808·10 −5 s − 5.08365633·10 −9 T + 0.0030005179s 2 + 4.266495·10 −11 T 2 + 8.33633·10 −7 sT or Bi = 0.764714 + 10.1689091s − 0.003400089T + 948.715738s 2 + 0.000024316T 2 − 0.12478252sT, D = 1.27547948·10 −7 − 2.3806·10 −5 s − 5.08365753·10 −9 T + 0.0030005175s 2 + 4.266493·10 −11 T 2 + 8.336334·10 −7 sT. The results of statistical analysis for the Biot number and moisture diffusion coefficient equations were as follows: R = 0.9905672, MAE = 0.0406375, RMSE = 0.050252 and R = 0.9905611, MAE = 0.0406403 and RMSE = 0.050273, respectively.

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Nanofluids as secondary fluid in the refrigeration system: Experimental data, regression, ANFIS, and NN modeling

Cited by 61 publications

References 27 publications

Circular Microchannel Heat Sink Optimization Using Entropy Generation Minimization Method

Circular Microchannel Heat Sink Optimization Using Entropy Generation Minimization Method

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

Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

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