Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid

Akhgar, Alireza; Toghraie, Davood; Sina, Nima; Afrand, Masoud

doi:10.1016/j.powtec.2019.07.086

Cited by 179 publications

(48 citation statements)

References 37 publications

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“…In the experiments of different authors, the aggregation state of composite PCM is also different, so it is improper to compare their experimental results with the same theoretical curve. In our calculation, for the experimental results by Liu et al, 39 Akhgar et al, 38 Hwang et al, 41 Chen et al, 37 and Xie et al, 40 It is seen that the effective thermal conductivity of composite PCM agrees well with the experiments overall. In order to prove the correctness of the theoretical results, the values of R g of the materials prepared by different authors were calculated based on the experimental data of thermal conductivity (k eff ) and volume fraction (ϕ p ).…”

Section: Verification Of Resultssupporting

confidence: 74%

“…To further validate the established model, the theoretical results are compared with experimental results in previous literatures, [37][38][39][40][41] where the PCMs and nanoparticles were selected as EG and MWCNTs, respectively. In the theoretical calculations, the thermal conductivity of EG was set as 0.371 W/(m · K), 42 while the thermal conductivity of MWCNT was equal to 3000 W/(m · K).…”

Section: Verification Of Resultsmentioning

confidence: 99%

“…The comparative results for composite PCM are shown in Figure 10. The comparisons include Liu et al, 39 Akhgar et al, 38 Hwang et al, 41 Chen, 37 and Xie, 40 as shown in Figure 10A-E. However, for a certain fabricated PCM, R g can be approximated as a constant value because the aggregate state of materials is mostly determined by the fabrication procedures.…”

Section: Verification Of Resultsmentioning

confidence: 99%

“…On the basis of different R g , thermal conductivity of composite PCM were predicted and compared with experimental results, respectively. The comparisons include Liu et al, 39 Akhgar et al, 38 Hwang et al, 41 Chen, 37 and Xie, 40 as shown in Figure 10A-E. In our calculation, for the experimental results by Liu et al, 39 Akhgar et al, 38 Hwang et al, 41 Chen et al, 37 and Xie et al, 40 the values of R g are 2.72, 4.93, 5.22, 2.62, and 4.12, respectively.…”

Section: Verification Of Resultsmentioning

confidence: 99%

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Conduction paths of backbones for thermal enhancement of nanocomposite phase change materials

Zhou

Zhao

et al. 2019

Int J Energy Res

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Thermal energy storage (TES) based on phase change materials (PCMs) has become a research hot spot due to its high energy storage density and maintained operating temperature during the phase change. However, as PCM has a poor thermal conductivity that can be as low as 0.2~0.5 W/m·K, the charging/discharging processes of PCM modules are significantly restrained, which severely affects the application of the TES technology in industrial sectors. This study concerns the improvement of the effective thermal conductivity of composite PCM formed by adding nanoparticles with high thermal conductivity into different PCMs. A theoretical model is established to reveal the intrinsic mechanism for the promotion of thermal conductivity of composite PCM consisting of nanoparticles. The results show that aggregation and interfacial thermal resistance are the main reasons for the change of the thermal conductivity. By forming effective conduction paths composed of backbones in the composite PCM, the average thermal conductivity can be improved significantly, which can be as high as 10~50 W/m·K with a wide range of volume fraction of the additives.

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Section: Verification Of Resultssupporting

confidence: 74%

Section: Verification Of Resultsmentioning

confidence: 99%

Section: Verification Of Resultsmentioning

confidence: 99%

Section: Verification Of Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Conduction paths of backbones for thermal enhancement of nanocomposite phase change materials

Zhou

Zhao

et al. 2019

Int J Energy Res

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“…In recent years, various types of nanofluids have been applied in a growing body of researches. Many kinds of nanofluids (e.g., TiO 2 nanofluids, 2 ZnO and MgO nanofluids, 3 ZnO-Ag/water nanofluids, 4 ZnO-DWCNT/EG hybrid nanofluids, 5 and multiwalled carbon nanotube (MWCNT)-TiO 2 /ethylene glycol hybrid nanofluids 6 ), are prepared, and their physical properties are measured. Results of researches showed that nanofluids have a better performance than the base fluids in heat exchange progress.…”

Section: Introductionmentioning

confidence: 99%

Influence of rotation angle of a triangular tube with a built‐in twisted tape on the thermal‐exergy efficiency and entropy generation of nanofluids in the heat exchange system

Zhao

et al. 2020

Asia-Pacific J Chem Eng

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In order to investigate the thermal-exergy efficiency and entropy generation of nanofluids flowing through a triangular tube with a built-in twisted tape, a heat exchange system is designed and established. Deionized water and three mass concentration nanofluids (ω = 0.1 wt%, 0.3 wt%, and 0.5 wt%) are applied as the heat-transfer mediums flowing through three different rotation angle (α = 0 , 30 , and 60 ) triangular tubes, respectively.Thermal efficiency index (η) and entropy generation (S) are used to characterize the performance of the heat exchange system. It is proved that the increase of nanoparticles can not only effectively promote the heat transfer effect but also augment the pressure loss. Nusselt number of nanofluids is increased by 20.2% on average, whereas the flow resistance coefficient is increased by 10.1% on average. Based on the thermal efficiency index, the data demonstrate that ω = 0.5 wt% nanofluids in the triangular tube at α = 0 can make the most effective use of energy, whose value could reach 1.32 at most. Exergy efficiency of nanofluids in the heat exchange system can be evidently heightened under the same pumping power and flow rate. Entropy production of 0.1 wt% nanofluids is the smallest. K E Y W O R D Snanofluids, forced convection, exergy efficiency, entropy generation

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Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

Shafiq

Çolak

Swarup

et al. 2022

Advcd Theory and Sims

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The study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi‐layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.

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Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid

Cited by 179 publications

References 37 publications

Conduction paths of backbones for thermal enhancement of nanocomposite phase change materials

Conduction paths of backbones for thermal enhancement of nanocomposite phase change materials

Influence of rotation angle of a triangular tube with a built‐in twisted tape on the thermal‐exergy efficiency and entropy generation of nanofluids in the heat exchange system

Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

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