Specific Heat Analysis of Water and Ethylene Glycol Based rGO-ND Hybrid Nanofluids: Experimental and Artificial Neural Network Predictions
Source abstract
Abstract
The isobaric specific heat was measured experimentally for two kind of hybrid nanofluids like water and ethylene glycol based reduced graphene oxide-nanodiamond (rGO-ND) hybrid nanofluids at different particle volume loadings of 0.2%, 0.4%, 0.6%, 0.8% and 1.0%, and in the temperature range from 293 K to 333 K, respectively. The obtained experimental specific heat data was used for the artificial neural network (ANN) algorithms of Support Vector Regression (SVR), and Levenberg-Marquardt (LM) models for the predictions. Results indicated that, the specific heat of water, and ethylene glycol-based hybrid nanofluids at 1.0% vol. of hybrid nanofluid is lowered by 1.09% and 1.10% at a temperature of 333 K, compared to their own base fluids. The SVR and LM models for the specific heat of water-based hybrid nanofluids predict accurately with a correlation coefficient of 0.99849, and 0.99957, similarly, the SVR and LM models for the specific heat of ethylene glycol-based hybrid nanofluids predict accurately with a correlation coefficient of 0.99998, and 0.99906, respectively. The obtained data was compared with other kind of nanofluids data. The polynomial regression equation was proposed for the water and ethylene glycol-based hybrid nanofluids through the SVR model.
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Research context
OpenAlex topics: Nanofluid Flow and Heat Transfer · Heat Transfer and Boiling Studies · Heat Transfer and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Prince Mohammad Bin Fahd University
References (48)
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