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

Yang, Xiaowei; Boroomandpour, Ahmadreza; Wen, Shiwei; Toghraie, Davood; Soltani, Farid

doi:10.1016/j.powtec.2021.04.093

Cited by 56 publications

(5 citation statements)

References 35 publications

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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%

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Huang,

Chen,

Kanga

et al. 2024

Preprint

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In the past, most of traditional master craftsmen always adopted the acoustic actions to recognition the situation of machine. Along with the development of time and technology, the mode of industry has changed with the Fourth Industrial Revolution (Industry 4.0). The long been known for the mother of industry, mold industry, has been inevitably impacted by Industry 4.0. This research stems from the structure of the six-level IoT model, through Internet connecting sensors, data collection, and the appropriate implementation of human and machine interface to intellectualize the injection molding machine. This research has collected 130 times of audio frequency, and there were 53 effective data sets, in sum there were 34,030,640 datasets. There were 5 manufacturing actions of petroleum molding machines that were successfully identified. Due to the low accuracy of one of the manufacturing actions, the training of audio frequency is based on the other four. In the end, there are 93.64% of accurate AI intelligent identifying models. Concurrently, through labeling the audio characteristics of different manufacturing parameters, the model recognizing audio characteristics from injection molding machines under different injection speed and rotation speed parameters is successfully trained. It is expected that in the future, other researchers can use this research as a reference to further strengthen the correlation between audio characteristics and injection molding machines to engage a more in-depth and diverse application of this topic. JEL Classification: C80, C88, C90.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Huang,

Chen,

Kanga

et al. 2024

Preprint

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“…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%

New Solutions of Fractional Jeffrey Fluid with Ternary Nanoparticles Approach

et al. 2022

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The existing work deals with the Jeffrey fluid having an unsteady flow, which is moving along a vertical plate. A fractional model with ternary, hybrid, and nanoparticles is obtained. Using suitable dimensionless parameters, the equations for energy, momentum, and Fourier’s law were converted into non-dimensional equations. In order to obtain a fractional model, a fractional operator known as the Prabhakar operator is used. To find a generalized solution for temperature as well as a velocity field, the Laplace transform is used. With the help of graphs, the impact of various parameters on velocity as well as temperature distribution is obtained. As a result, it is noted that ternary nanoparticles approach can be used to increase the temperature than the results obtained in the recent existing literature. The obtained solutions are also useful in the sense of choosing base fluids (water, kerosene and engine oil) for nanoparticles to achieved the desired results. Further, by finding the specific value of fractional parameters, the thermal and boundary layers can be controlled for different times. Such a fractional approach is very helpful in handling the experimental data by using theoretical information. Moreover, the rate of heat transfer for ternary nanoparticles is greater in comparison to hybrid and mono nanoparticles. For large values of fractional parameters, the rate of heat transfer decreases while skin friction increases. Finally, the present results are the improvement of the results that have already been published recently in the existing literature. Fractional calculus enables us to control the boundary layers as well as rate of heat transfer and skin friction for finding suitable values of fractional parameters. This approach can be very helpful in electronic devices and industrial heat management system.

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“…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%

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

Wang

Chen

2022

Ind. Eng. Chem. Res.

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Nanofluids are often used as heat transfer fluids due to their good thermal and flow properties. Nanofluids are widely used in energy systems such as solar collectors, heat exchangers, and heat pipes. The thermophysical properties of nanofluids can significantly affect their performance in engineering systems. According to current research in this field, machine learning is a very attractive method to predict the thermophysical properties of nanofluids. We believe that, compared with experiments, machine learning methods are more efficient, rapid, accurate, and practical. The accuracy of the model is affected by factors such as the input variables, the data set selected during the modeling process, and the applied algorithm. This is a comprehensive review that describes the use of different machine learning methods to predict the thermophysical properties of nanofluids (conventional and hybrid nanofluids). This is very useful for researchers, scientists, and engineers in the field of nanofluids.

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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

Cited by 56 publications

References 35 publications

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

New Solutions of Fractional Jeffrey Fluid with Ternary Nanoparticles Approach

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

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