Neural Network Predictions of the Thermophysical Properties of ZnO+Fe3O4/water Hybrid Nanofluids
Lingala Syam SUNDAR
Abstract. The thermophysical properties were estimated experimentally at veracious weight loadings, and temperatures of ZnO+Fe3O4/water hybrid nanofluids. Later the experimental data has been used for artificial neural network (ANN) to train the algorithm. In this study, the temperatures range, and weight concentration range used are varies from 20 oC to 80 oC, and 0% to 0.5%. Levenberg- Marquardt model was considered to train the ANN algorithm. Experimental outcome shows thermal conductivity is enhanced by 4.153%, and 8.689%, moreover, the viscosity is enhanced by 48.762%, and 15.806% at 0.5 wt%, and at 20 oC, and 80 oC, over the base fluid. The density is enhanced by 4.687%, and specific heat is decreased by 1.052% at 0.5 wt%, and at 20 oC, over base liquid. The ANN result shows that, for the trained data, the mean square error for thermal conductivity is 6.7951e-7, for viscosity is 0.00001, for density is 1.9428, and for specific heat is 2.7528 with a correlation coefficient of 0.9997, 0.9998, 0.9960, and 0.9944. Finally, from the ANN, the polynomial regression equation has been developed.
Keywords
Hybrid Nanofluids, Properties, Enhancement, Temperatures, Artificial Neural Network
Published online 4/25/2026, 7 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Lingala Syam SUNDAR, Neural Network Predictions of the Thermophysical Properties of ZnO+Fe3O4/water Hybrid Nanofluids, Materials Research Proceedings, Vol. 64, pp 722-728, 2026
DOI: https://doi.org/10.21741/9781644904091-90
The article was published as article 90 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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