Development of the ANN–KIM composed model to predict the nanofluid energetic thermal conductivity via various types of nano-powders dispersed in oil

Mei, Xueqing; Li, Zhixiong; Bagherzadeh, Seyed Amin; Karimipour, Aliakbar; Bahrami, Maryam; Karimipour, Arash

doi:10.1007/s10973-020-10212-0

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Consequently, the selection of suitable engine oil may lead to higher performance and low fuel consumption. To advance the heat transfer rate of engine oils or other operational fluids, many researchers investigated nanofluids, composed of nanoparticles and operational fluids 1 – 6 . A review of previous researches indicates Al 2 O 3 nanoparticles commonly are used to prepare the nanofluids 7 – 11 .…”

Section: Introductionmentioning

confidence: 99%

An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al2O3-engine oil nanofluid

Esfe

Toghraie

2021

Sci Rep

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This study presents the design of an artificial neural network (ANN) to evaluate and predict the viscosity behavior of Al2O3/10W40 nanofluid at different temperatures, shear rates, and volume fraction of nanoparticles. Nanofluid viscosity ($${\mu }_{nf}$$ μ nf ) is evaluated at volume fractions ($$\varphi$$ φ =0.25% to 2%) and temperature range of 5 to 55 °C. For modeling by ANN, a multilayer perceptron (MLP) network with the Levenberg–Marquardt algorithm (LMA) is used. The main purpose of this study is to model and predict the $${\mu }_{nf}$$ μ nf of Al2O3/10W40 nanofluid through ANN, select the best ANN structure from the set of predicted structures and manage time and cost by predicting the ANN with the least error. To model the ANN, $$\varphi$$ φ , temperature, and shear rate are considered as input variables, and $${\mu }_{nf}$$ μ nf is considered as output variable. From 400 different ANN structures for Al2O3/10W40 nanofluid, the optimal structure consisting of two hidden layers with the optimal structure of 6 neurons in the first layer and 4 neurons in the second layer is selected. Finally, the R regression coefficient and the MSE are 0.995838 and 4.14469E−08 for the optimal structure, respectively. According to all data, the margin of deviation (MOD) is in the range of less than 2% < MOD < + 2%. Comparison of the three data sets, namely laboratory data, correlation output, and ANN output, shows that the ANN estimates laboratory data more accurately.

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Section: Introductionmentioning

confidence: 99%

An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al2O3-engine oil nanofluid

Esfe

Toghraie

2021

Sci Rep

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“…Over the last decade, artificial neural networks (e.g. Mei et al [ 3 ] and Wu et al [ 4 ]) and advanced methods of mathematical statistics (e.g. Xu et al [ 5 ]) have been used extensively in data analysis and to predict the properties of substances and materials.…”

Section: Introductionmentioning

confidence: 99%

The recognition of selected burning liquids by convolutional neural networks under laboratory conditions

Martinka

Nečas

Rantuch

2021

J Therm Anal Calorim

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This paper deals with the recognition of selected burning liquids by convolutional neural networks (CNNs). Three CNNs (AlexNet, GoogLeNet and ResNet-50) were trained, validated and tested (in the MATLAB 2020b software) for the recognition of selected liquids (ethanol, propanol and pentane) using photographs of the flames they produce. For training, validation and test photographs of the liquids under investigation burning in a 106-mm-diameter vessel were used. The accuracy of all the CNNs under investigation during the tests was above 99%. In addition the trained CNNs were tested using photographs of the flames generated by the liquids under investigation burning in a vessel with a diameter of 75 mm. The accuracy of the trained CNNs in this additional test ranged from 37 to 42% (GoogLeNet) through 62–73% (ResNet-50) up to 51–80% (AlexNet) – the results varied dependent upon the relative size of the flame in the photograph under analysis (in most cases an increase in the relative size caused an increase in accuracy). The accuracy of the AlexNet can be improved from 80% to almost 96% using an algorithm. The principle of the algorithm is the analysis of 10 photographs of the same liquid in the same vessel (taken over a few seconds) followed by the recognition based on an identical classification for at least 6 out of 10 photographs. An accuracy of 96% is sufficient for the rapid recognition of burning liquids in practical applications. Supplementary Information The online version contains supplementary material available at 10.1007/s10973-021-10903-2.

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Estimation of mechanical properties of LDPE/LLDPE/SEBS nanocomposite reinforced with calcium carbonate nanoparticles by Ch mathematical model

Shahrajabian

Bagherzadeh

Moghri

et al. 2021

J Therm Anal Calorim

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Development of the ANN–KIM composed model to predict the nanofluid energetic thermal conductivity via various types of nano-powders dispersed in oil

Cited by 5 publications

References 36 publications

An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al2O3-engine oil nanofluid

An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al2O3-engine oil nanofluid

The recognition of selected burning liquids by convolutional neural networks under laboratory conditions

Estimation of mechanical properties of LDPE/LLDPE/SEBS nanocomposite reinforced with calcium carbonate nanoparticles by Ch mathematical model

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