Back propagation modeling of shear stress and viscosity of aqueous Ionic-MXene nanofluids

Afzal, Asif; Yashawantha, Kyathanahalli Marigowda; Aslfattahi, Navid; Saidur, R.; Razak, R. K. Abdul; Subbiah, Ram

doi:10.1007/s10973-021-10743-0

Cited by 55 publications

(24 citation statements)

References 66 publications

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“…If "Prob > F" falls below 0.05 and above 0.1, then the model terms are supposed to be substantial terms. All responses acquired with these models are closer to standard values representing the models' high accuracy and consistency with the experimental responses [73][74][75][76] 5 and 6. Table 4 lists the validation results of the RSM regression model and experimental values with the percentage of deviation.…”

Section: Resultsmentioning

confidence: 65%

An Experimental Study of ZrO₂‐CeO₂ Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

Vidhya

Balakrishnan

Kumar³

et al. 2022

Journal of Nanomaterials

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This article experimentally and statistically reports the convective heat transfer performance of a cylindrical mesh-type heat pipe apparatus filled with ZrO2-CeO2/water-ethylene glycol nanofluids. In this regard, ZrO2-CeO2 nanoparticles were synthesized and characterized through the Scanning Electron Microscope and Powder X-ray diffraction methods followed by the preparation of hybrid ZrO2-CeO2 nanofluids of various concentrations ranging from 0.025 to 0.1%. The heat transfer features of a tubular heat pipe with a mixture of the ZrO2-CeO2 nanofluid were evaluated. A 5.33% decrease in thermal resistance value and a 41.16% increase in heat transfer ability with increased power input were observed. The potent regression models were proposed to estimate heat transfer features of the heat pipe. The ANOVA statistical method has been employed to determine the P value and the F value of the models towards enhancing the reliability and accuracy of the developed models. The outcome revealed that the proposed models are reliable and have the best fit with the experimental data for 30–60 W power. The correlations’ results were validated against the experimental data and showed high accuracy. Moreover, the accuracy of the developed models was ensured through R -squared and adjusted R -squared values.

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Section: Resultsmentioning

confidence: 65%

An Experimental Study of ZrO₂‐CeO₂ Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

Vidhya

Balakrishnan

Kumar³

et al. 2022

Journal of Nanomaterials

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“…The cost-competitiveness, scalability, and dependability of 2D nanomaterial synthesis, the technological and industrial readiness of value chains, and sociopolitical considerations, legislation, and industrial development policies are three major concerns that are most likely to impact 2D nanomaterial's industrial uptake [128]. There are several other studies related to nanomaterial usage in different fields which can be referred in [134][135][136][137][138][139][140][141][142][143][144][145][146][147][148].…”

Section: Graphene Production Market Trend and Commercialization Obsta...mentioning

confidence: 99%

A Review on Environmental and Economic Impact of 2D Nanomaterials‐Based Heat Transfer Fluids

Shaik

Goyal

et al. 2022

Journal of Nanomaterials

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2D nanomaterials-based heat transfer fluids show excellent thermal properties due to their large specific surface area; hence, they find large-scale applications in automobile industries and cooling processes. Therefore, it is very essential to study the environmental and economic aspects of these 2D nanomaterial-based nanofluids. In this review, we have discussed the environmental impact of 2D nanomaterial-based heat transfer nanofluids under various conditions. The environmental impact analysis of these materials has shown excellent capability in reducing the energy consumption for heat transfer operations. Moreover, the possibility of nanomaterials and base fluid recovery makes it a sustainable alternative. In addition, health risk assessment on humans, cytotoxicity, and life cycle analysis have also been explored. The price-performance index has been successfully used to study the economic impact of 2D nanomaterial-based heat transfer fluids. The overall economic impact of 2D nanomaterial-based heat transfer nanofluids provides an optimistic perspective over conventional heat transfer fluids. Moreover, graphene production, market trend, and commercialization obstacles were also discussed.

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“…e second stage is the backpropagation of errors, and the weight and bias from the hidden layer to the output layer and from the input layer to the hidden layer are adjusted in turn [13].…”

Section: Bpnnmentioning

confidence: 99%

Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

Tian

Bao

Liu

2022

Mathematical Problems in Engineering

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The goal of the research is to increase the accuracy of wind power forecasts while maintaining the power system’s stability and safety. First, the wireless sensor network (WSN) is used to collect the meteorological data of wind power plants in real time. Second, the real-time data collected by WSN are combined with the meteorological forecast data of some meteorological organizations. Then, the fruit fly optimization algorithm (FOA) is improved, and the improved fruit fly optimization algorithm (IFOA) and back propagation neural network (BPNN) are combined to construct the wind power forecast model. Finally, the signal reception of the WSN and the error of wind power forecast under different receiving distances and different antenna heights are tested. The results show that with the increase of receiving and transmitting distance, the signal strength decreases, the packet loss rate increases, and the electromagnetic wave of wind plants will cause some interference to the signal strength. The fly optimization algorithm-back propagation (IFOA-BP) wind power forecast model has a better effect than other models in wind power prediction and can better fit the actual tested wind power. Its root mean square error (RMSE) and mean absolute error (MAE) are 0.16 and 0.11, respectively. The research results provide a reference for improving the forecast accuracy of wind power.

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Back propagation modeling of shear stress and viscosity of aqueous Ionic-MXene nanofluids

Cited by 55 publications

References 66 publications

An Experimental Study of ZrO₂‐CeO₂ Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

An Experimental Study of ZrO₂‐CeO₂ Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

A Review on Environmental and Economic Impact of 2D Nanomaterials‐Based Heat Transfer Fluids

Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

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Back propagation modeling of shear stress and viscosity of aqueous Ionic-MXene nanofluids

Cited by 55 publications

References 66 publications

An Experimental Study of ZrO2‐CeO2 Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

An Experimental Study of ZrO2‐CeO2 Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

A Review on Environmental and Economic Impact of 2D Nanomaterials‐Based Heat Transfer Fluids

Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning

Contact Info

Product

Resources

About

An Experimental Study of ZrO₂‐CeO₂ Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

An Experimental Study of ZrO₂‐CeO₂ Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations