Prediction of thermal conductivity of various nanofluids using artificial neural network

Ahmadloo, Ebrahim; Azizi, Sadra

doi:10.1016/j.icheatmasstransfer.2016.03.008

Cited by 144 publications

(50 citation statements)

References 51 publications

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“…It similarly definite that the developed model yielded more successful outcomes than the model correlations in the literature. Ahmadloo et al 26 and R-value is 0.99988, it is proof that the ANN can ideally foresee the viscosity of TiO 2 /water nanofluid. In the study conducted by Ghazvini et al, 31 an ANN was As a result of the studies conducted, it has been proved that the ANN has a very well align with the tentative data and the proposed new correlation has a very low margin of error.…”

Section: Introductionmentioning

confidence: 79%

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Çolak

2020

Int J Energy Res

107

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In this study, the effect of the amount of data used in the design of artificial neural networks (ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were designed using the experimentally measured specific heat data of the Al 2 O 3 /water nanofluid prepared at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1 and 0.2 using the Al 2 O 3 nanoparticle. The developed ANN is a multi-layer perceptron, feedforward and backpropagation model. In each ANN with 15 neurons in the hidden layer, the volumetric concentration (φ) and temperature (T) values were nominated as input layer factors and the specific heat value was estimated as the output value. With the aim of survey the effect of the amount of data on the predicted results of the ANN, a different amount of datasets were used in each developed ANN. In this context, in total 260 data were used in the Model 1 ANN. Subsequently, the total amount of data was reduced by 20% in each developed neural network and 55 data were used in the ANN named Model#5. The results obtained show that ANNs are highly talented of predicting the specific heat values of Al 2 O 3 /water nanofluid. However, in the comparisons, it was evaluated that the amount of data used had a share on the prediction performance of the ANN and that the decrease in the amount of data with the prediction performance of the ANN decreased.

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Section: Introductionmentioning

confidence: 79%

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Çolak

2020

Int J Energy Res

107

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show abstract

“…During the cross-validation process, the number of folds was varied from 1 to 5, and the closest fold to the mean was taken as the final fold [36,37]. The training is based on the backpropagation technique to set the optimum weights of the feed-forward neural network [38,39]. Deciding the ANN topology is another critical step to avoid over and underfitting the data.…”

Section: Mrr= Vcmentioning

confidence: 99%

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

et al. 2020

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The continuous pursue of sustainable manufacturing is motivating the utilization of new advanced technology, especially for hard to cut materials. In this study, an adaptive approach for optimization of machining process of AISI 4340 using wiper inserts is proposed. This approach is based on advance yet intuitive modeling and optimization techniques. The approach is based on Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), as well as Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), for modeling, optimization and multicriteria decision making respectively. This integrated approach, to best of the authors' knowledge, has been deployed for the first time to adaptively serve different designs of manufacturing processes. Such designs have different orientations, namely cost, quality, productivity, and balanced orientation. The capability of the proposed approach to serving such diverse requirements answers one of the most accelerating demands in the manufacturing community due to the dynamics of the uprising smart production lines. Besides, the proposed approach is presented in a straightforward manner that can be extended easily to other design orientations as well as other engineering applications. Based on the proposed design, a balanced general setting of 197.4 m/min, 0.95 mm, and 0.168 mm/rev was recommended along with other settings for more sophisticated requirements. Confirmatory experiments showed a good agreement (i.e., no more than 7% deviation) with the predicted optimum responses. This shows the validity of the proposed approach as a viable tool for designers to promote holistic and sustainable process design. INDEX TERMS Adaptive design, Artificial neural networks, Genetic Algorithm, Modeling, Wiper inserts, Turning, NOMENCLATURE ANN Artificial Neural Network B Balanced design DOC Depth of Cut EC Intensive cost-oriented design EP Intensive productivity-oriented design EQ Intensive quality-oriented design f Feed GRA Gray Relation Analysis IC Intensive cost-oriented design IP Intensive productivity-oriented design IQ Intensive quality-oriented design LINMAP

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“…In the present work, we have employed a multilayer perceptron (MLP) neural network model for the prediction of the viscosity of EG based nanofluids. Due to their ability to handle non-linearly dependent complex data, MLP-ANN are the most commonly used type of ANN models [35]. The algorithm used to train the MLP network was back-propagation which when used in feed forward network, is also known as the feed forward back propagation algorithm [36].…”

Section: Development Of the Annmentioning

confidence: 99%

Development of an artificial neural network for the prediction of relative viscosity of ethylene glycol based nanofluids

Parashar¹,

Seraj

Yahya³

et al. 2020

SN Appl. Sci.

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Prediction of thermal conductivity of various nanofluids using artificial neural network

Cited by 144 publications

References 51 publications

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

Development of an artificial neural network for the prediction of relative viscosity of ethylene glycol based nanofluids

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