Applying artificial neural network and curve fitting method to predict the viscosity of SAE50/MWCNTs-TiO2 hybrid nanolubricant

Chen, Zhixiong; Ashkezari, Abbas Zarenezhad; Tlili, Iskander

doi:10.1016/j.physa.2019.123946

Cited by 23 publications

(16 citation statements)

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“…This neural network has a function fitting known as a training process, where inputs are used to produce associated target outputs. This neural network is usually applied to function approximation and time series prediction (Chen et al 2020 ; Moradikazerouni et al 2019 ). Cascade-forward neural network: This neural network is similar to a feedforward network and has connections directly from the input layer to the subsequent layers (An et al 2020 ; Budak et al 2020 ).…”

Section: Proposed Methodsmentioning

confidence: 99%

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RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

et al. 2021

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In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.Keywords Ensemble neural networks Á COVID-19 Á Time series prediction Á Type-2 fuzzy logic Á Firefly algorithm Communicated by V. E. Balas.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

et al. 2021

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“…The experimental results show that the nanofluid behavior in the relationship between shear stress and at all is non-Newtonian. Chen et al [ 15 ] investigated changes in the viscosity of the base oil after the addition of MWCNTs-TiO 2 nanoparticles to the SAE50 base fluid. The results of these two experimental methods show that ANN is more reliable than curve fitting.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Rheological Behavior of MWCNT-Al2O3/SAE50 Hybrid Nanofluid to Provide the Best Nano-lubrication Conditions

et al. 2022

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In this study, MWCNT-Al2O3 hybrid nanoparticles with a composition ratio of 50:50 in SAE50 base oil are used. This paper aims to describe the rheological behavior of hybrid nanofluid based on temperature, shear rate ($$\dot{\gamma })$$ γ ˙ ) and volume fraction of nanoparticles ($$\varphi$$ φ ) to present an experimental correlation model. Flowmetric methods confirm the non-Newtonian behavior of the hybrid nanofluid. The highest increase and decrease in viscosity ($${\mu }_{\rm nf}$$ μ nf ) in the studied conditions are measured as 24% and − 17%, respectively. To predict the experimental data, the five-point-three-variable model is used in the response surface methodology with a coefficient of determination of 0.9979. Margin deviation (MOD) of the data is determined to be within the permissible limit of − 4.66% < MOD < 5.25%. Sensitivity analysis shows that with a 10% increase in $$\varphi$$ φ at $$\varphi =$$ φ = 1%, the highest increase in $${\mu }_{\rm nf}$$ μ nf of 34.92% is obtained.

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“…The procedure of finding optimal ANN is illustrated in Figure 2. Many researchers have used a similar method to find the best network 34‐55 …”

Section: Resultsmentioning

confidence: 99%

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60 C), nanoparticles (CuO, Al 2 O 3 , TiO 2 , and mesoporous SiO 2 ), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm.An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 Â 10 À5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

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Applying artificial neural network and curve fitting method to predict the viscosity of SAE50/MWCNTs-TiO2 hybrid nanolubricant

Cited by 23 publications

References 50 publications

RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

RETRACTED ARTICLE: Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

Experimental Study of Rheological Behavior of MWCNT-Al2O3/SAE50 Hybrid Nanofluid to Provide the Best Nano-lubrication Conditions

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

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