Using perceptron feed-forward Artificial Neural Network (ANN) for predicting the thermal conductivity of graphene oxide-Al2O3/water-ethylene glycol hybrid nanofluid

Tian, Shichao; Arshad, Noreen Izza; Toghraie, Davood; Eftekhari, S. Ali; Hekmatifar, Maboud

doi:10.1016/j.csite.2021.101055

Cited by 90 publications

(11 citation statements)

References 34 publications

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“…The applications of robotic could be extended on various potential works such as environmental, energy, hybrid materials, biomaterials, etc [43][44][45][46][47][48][49][50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Review On: The Service Robot Mathematical Model

Moshayedi

Roy

Sambo

et al. 2022

EAI Endorsed Trans AI Robotics

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After nearly 30 years of development, service robot technology has made important achievements in the interdisciplinary aspects of machinery, information, materials, control, medicine, etc. These robot types have different shapes, and mainly in some are shaped based on application. Till today various structure are proposed which for the better analysis’s need to have the mathematical equation that can model the structure and later the behaviour of them after implementing the controlling strategy. The current paper discusses the various shape and applications of all available service robots and briefly summarizes the research progress of key points such as robot dynamics, robot types, and different dynamic models of the differential types of service robots. The current review study can be helpful as an initial node for all researchers in this topic and help them to have the better simulation and analyses. Besides the current research shows some application that can specify the service robot model over the application.

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“…The applications of robotic could be extended on various potential works such as environmental, energy, hybrid materials, biomaterials, etc [43][44][45][46][47][48][49][50][51][52].…”

Section: Discussionmentioning

confidence: 99%

Review On: The Service Robot Mathematical Model

Moshayedi

Roy

Sambo

et al. 2022

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

show abstract

“…The study revealed that for higher temperature and volume concentration the viscosity was independent of the shear rate. The effect of temperature and volume fraction of rGo‐Al 2 O 3 ‐W/EG nanofluid was studied using an ANN 38 . Functionalized MWCNT in a binary‐based fluid of 80% water and 20% EG was tested for its thermal conductivity.…”

Section: Introductionmentioning

confidence: 99%

“…The effect of temperature and volume fraction of rGo-Al 2 O 3 -W/EG nanofluid was studied using an ANN. 38 Functionalized MWCNT in a binary-based fluid of 80% water and 20% EG was tested for its thermal conductivity. A 27.3% enhancement in thermal conductivity was noted for the lower volume concentration of nanoparticles.…”

Section: Introductionmentioning

confidence: 99%

Thermal conductivity and viscosity of Al ₂ O ₃ ‐ZnO‐MWCNT‐EG ternary nanofluid

Bindu

Muthu²

2022

Intl J of Energy Research

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Summary Hybrid nanofluids exhibit enhanced thermal properties compared to their base fluid which makes them more suitable as heat transfer fluids for various applications including solar thermal energy conversion. In the present study, ethylene glycol‐based ternary nanofluid composed of Al2O3, ZnO and multiwall carbon nanotube nanoparticles is synthesized and analyzed for its thermophysical properties such as viscosity and thermal conductivity for different volume concentrations. Thermal conductivity was determined for a temperature range of 25 to 40°C and viscosity was measured for a temperature range of 40 to 70°C. When the volume concentration of nanoparticles for ternary nanofluid was increased from 0.01% to 0.05%, an enhancement in thermal conductivity along with a reduction in viscosity was observed. For a maximum volume concentration of 0.05%, the thermal conductivity was enhanced by 26.6% and 11.2% at a temperature of 25 and 40°C, respectively. Percentage reduction in viscosity was observed at 19.46% and 13.86% for 40 and 70°C, respectively for the volume concentration. Thus, the study suggests that the ternary nanofluid can be used as heat transfer fluid for medium‐temperature heating and cooling applications.

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“…An artificial neural network (ANN), a subset of machine learning, is a data processing system that mimics the characteristics of the human brain to predict the performances of engineering systems [1,2]. The feed-forward multilayer perceptron ANN model consists of interconnected nodes, called neurons, which form an input layer, hidden layer(s), and an output layer [3,4].…”

Section: Introductionmentioning

confidence: 99%

Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

Randriambololona¹,

Shaeri²,

Sarabi³

2022

Proceedings of the 8th World Congress on Mechanical, Chemical, and Material Engineering

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The present study investigates the dependency of prediction accuracy of an artificial neural network (ANN) on the network architecture using 65 different neural networks from seven architecture patterns. The accuracy of the ANNs is compared based on their capability to predict heat transfer coefficients of air-cooled heat sinks operating in laminar flow. Scattered input data is used for training the networks to make the modelling more realistic and closer to practical applications. The input variables for the neural network are heat sink width, channel height, channel length, number of channels, fin thickness, and Reynolds number. The output is heat transfer coefficient. The training process for all ANNs is performed using ReLU as the activation function. The accuracy of the neural networks is evaluated by the root mean square error. It is found that the prediction accuracy of an ANN is strongly dictated by the optimization of the network architecture, which corresponds to the proper number of hidden layers and the number of neurons at each layer. The most accurate architecture in the present study predicts heat transfer coefficients of 60% and 86% of heat sinks within ±10% and ±20% of the true values, respectively. However, an ANN with an unoptimized architecture results in a substantially reduced accuracy such that it predicts heat transfer coefficients of only 19% and 30% of heat sinks within ±10% and ±20% of the true values, respectively.

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Using perceptron feed-forward Artificial Neural Network (ANN) for predicting the thermal conductivity of graphene oxide-Al2O3/water-ethylene glycol hybrid nanofluid

Cited by 90 publications

References 34 publications

Review On: The Service Robot Mathematical Model

Review On: The Service Robot Mathematical Model

Thermal conductivity and viscosity of Al ₂ O ₃ ‐ZnO‐MWCNT‐EG ternary nanofluid

Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

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Using perceptron feed-forward Artificial Neural Network (ANN) for predicting the thermal conductivity of graphene oxide-Al2O3/water-ethylene glycol hybrid nanofluid

Cited by 90 publications

References 34 publications

Review On: The Service Robot Mathematical Model

Review On: The Service Robot Mathematical Model

Thermal conductivity and viscosity of Al 2 O 3 ‐ZnO‐MWCNT‐EG ternary nanofluid

Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

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Product

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Thermal conductivity and viscosity of Al ₂ O ₃ ‐ZnO‐MWCNT‐EG ternary nanofluid