2021
DOI: 10.1016/j.powtec.2021.04.093
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Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of water/ethylene glycol-based mono, binary and ternary nanofluids containing MWCNTs, titania, and zinc oxide

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Cited by 67 publications
(8 citation statements)
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“…ANN is especially affected by the development of deep neural networks and has a wider range of applications (LeCun, Bengio, and Hinton 2015). The mathematical formula is as follows (Yang et al 2021):…”
Section: Artificial Neural Network (Ann)mentioning
confidence: 99%
“…ANN is especially affected by the development of deep neural networks and has a wider range of applications (LeCun, Bengio, and Hinton 2015). The mathematical formula is as follows (Yang et al 2021):…”
Section: Artificial Neural Network (Ann)mentioning
confidence: 99%
“…The general solution of Equation (22), which satisfies the boundary constraints given in Equation (21), is…”
Section: Solution Of Temperature With Ternary Nanoparticlesmentioning
confidence: 99%
“…Overall, these particles comprise metals, carbon-based essentials, and metal oxides. Many researchers illustrated that the thermal properties of a fluid increase after the addition of nanoparticles to the fluid [22]. Nanoparticles can swap conventional materials and help as a material of support and heterogeneous catalytic systems for unalike catalytic systems [23].…”
Section: Introductionmentioning
confidence: 99%
“…The method is cumbersome and cannot solve complex problems involving highly nonlinear or large-scale combinatorial processes. Because the experiment consumes a significant amount of financial, material, and time resources, it is critical to employ machine learning approaches to forecast the thermophysical properties of nanofluids. The cost method has been gradually applied in many fields such as material discovery, structural analysis, property prediction, reverse design, etc., and has shown amazing potential in materials research.…”
Section: Introductionmentioning
confidence: 99%