Factorial experimental design for the thermal performance of a double pipe heat exchanger using Al2O3-TiO2 hybrid nanofluid

Maddah, Heydar; Aghayari, Reza; Mirzaee, Mojtaba; Ahmadi, Mohammad Hossein; Sadeghzadeh, Milad; Chamkha, Ali J.

doi:10.1016/j.icheatmasstransfer.2018.07.002

Cited by 152 publications

(37 citation statements)

References 36 publications

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“…Solar water heater (SWH) is one of the presented storage technologies, which uses water as a storage medium. 15 In the active method, the presence of a bias force is vital; meanwhile, no external force is needed in the passive approach. 8 The most challenging issue in the utilization of solar water heaters, similar to other energy conversion systems, is the thermal efficiency factor.…”

Section: Introductionmentioning

confidence: 99%

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Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Sadeghzadeh

Ahmadi

Kahani

et al. 2019

Energy Science & Engineering

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112

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In the current study, Multilayer Perceptron Artificial Neural Network (MLP‐ANN) mode, Radial Basis Function Artificial Neural Network (RBF‐ANN), and Elman Back Propagation Neural Network (Elamn BP‐ANN) are developed to predict the thermal efficiency of a flat‐plate solar collector. TiO2 (20 nm)/water nanofluids are prepared using two‐step method and used in the designed solar system. All experiments are done in Mashhad city, Iran (Longitude/Latitude: 36.2605°N, 59.6168°E), according to EUROPEAN STANDARD EN 12975‐2 as a quasi‐dynamic test (QDT) method, and the solar collector is exposed to the south with the tilt angle of 55°. Three levels of inlet temperature (ambient air temperature, 52 and 74°C), 3 levels of volumetric flow rate (36, 72, and 108 L/(m2.h)), and 4 levels of nanofluid concentrations (0, 0.1, 0.2, and 0.3 wt.%) are considered as the input data, and the thermal efficiency of the solar system is calculated. According to the output results of developed models, the best prediction of thermal performance is obtained by MLP‐ANN model, although other generated models are also able to predict the efficiency of the solar collector with appropriated accuracy.

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Section: Introductionmentioning

confidence: 99%

“…[10][11][12][13][14] Heat transfer performance can be enhanced through active and passive approaches. 15 In the active method, the presence of a bias force is vital; meanwhile, no external force is needed in the passive approach. The heat transfer rate can be enhanced by introducing nanofluid as the working fluid of the heat exchangers.…”

Section: Introductionmentioning

confidence: 99%

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Sadeghzadeh

Ahmadi

Kahani

et al. 2019

Energy Science & Engineering

Self Cite

112

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“…Based on the previous published works in the field, as the Re increases, the heat transfer coefficient (h) increases and increasing the h naturally increases the Nu number [56,57], which results in less (DEG) ΔT based on Eq. (7).…”

Section: Thermodynamics Sensitivity Analysismentioning

confidence: 99%

A comprehensive thermo-hydraulic analysis and optimization of turbulent TiO2/W-EG nano-fluid flow inside double-pipe heat exchangers with helical coil inserts

Ebrahimi‐Moghadam

Gohari

Hoseinzade

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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The main aim in current work is to optimize TiO 2 /W-EG nano-fluid (with 60 vol% of water and 40 vol% of ethylene glycol) flow within the inner tube of the double-pipe heat exchangers with helical coil inserts. The entropy generation is selected as the target function and is to be minimized. To reach this goal, at the first step, a comprehensive sensitivity analysis is carried out taking into account all of the nano-fluid flow conditions (including nanoparticle volume fraction , Reynolds number Re and Prandtl number Pr) and geometric parameters of the helical coil inserts (including thepitch-to-diameter ratio of coil Π). Afterward, the genetic algorithm (GA) is utilized in the optimization process. The results of sensitivity analysis reveal that and Π , respectively, have the highest and lowest impact on the thermodynamics characteristics. Also, the addition of up to 0.02 vol% nanoparticles leads to a maximum of 13.93% improvement in dimensionless entropy generation (DEG). The outputs of the GA are opt = 0.0198%, Re opt = 8421.173, Pr opt = 32.833, Π opt = 2.437 and DEG opt = 0.005086. Furthermore, based on the optimization results, a useful correlation is proposed to find the optimum geometry of the helical coil based on the nano-fluid flow conditions.

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“…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]. Additionally, some studies 2 of 14 have focused on the prediction of the thermal conductivity ratio associated with various nanofluids with the help of using experiments and artificial neural networks [20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Factorial experimental design for the thermal performance of a double pipe heat exchanger using Al2O3-TiO2 hybrid nanofluid

Cited by 152 publications

References 36 publications

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

A comprehensive thermo-hydraulic analysis and optimization of turbulent TiO2/W-EG nano-fluid flow inside double-pipe heat exchangers with helical coil inserts

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

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