Experimental determination for viscosity of fly ash nanofluid and fly ash-Cu hybrid nanofluid:Prediction and optimization using artificial intelligent techniques

ABSTRACT

The advanced heat transfer applications of nanofluids make it important to investigate their viscous properties. For various engineering applications, accurate measurement of dynamic viscosity of nanofluid plays a major role. This research aims to provide accurate and reliable soft computational algorithms to predict the viscosity of fly ash nanofluids and fly ash-Cu hybrid nanofluids accurately using measured data. In this work, the dynamic viscosity of water-based stable fly ash nanofluid and fly ash–Cu (80:20% by volume) hybrid nanofluid for the concentration range of 0–4 vol.% determined in the temperature range of 30–60°C experimentally. The nanoparticles are characterized by SEM, TEM, and DLS techniques. The stability of the nanofluids assessed by Zeta potential measurement. Outcomes show that the viscosity of hybrid nanofluid is found to be higher than fly ash nanofluid. Correlations are suggested using multiple linear regression analysis to predict the nanofluid viscosity based on the obtained results. We have adopted a novel soft computing technique, namely ANN and MGGP to optimize the dynamic viscosity data of nanofluids obtained experimentally. The MGGP model predicts the viscosity values for fly ash nanofluid by (R = 0.99988, RMSE = 0.0019, and MAPE = 0.25%). In addition, statical analysis reveals that the MGGP model has excellent performance for fly ash-Cu/Water hybrid nanofluid viscosity (R = 0.9975, RMSE = 0.0063, and MAPE = 0.664%). In contrast to the ANN method and multiple linear regression analysis, the MGGP approach exhibits better results in estimating nanofluid viscosity.

KEYWORDS:

• Fly ash nanofluid
• hybrid nanofluid
• ann
• MGGP
• viscosity
• regression analysis

Additional information

Funding

The authors received no financial support for this research, authorship, and/or publication of this article.

Notes on contributors

Praveen Kanti

Praveen Kumar Kanti working as an assistant professor in the mechanical department at Jyothy institute of technology, Bangalore, Karnataka, India. He is pursuing a doctoral degree from Visvesvaraya Technological University in the field of heat transfer domain. He has numerous research papers to his credit in peer-reviewed journals. His area of interest includes heat transfer, CFD, nanofluids, and thermodynamics. 

Korada Viswanatha Sharma

Viswanatha Sharma Korada is working as a professor at Jawaharlal Nehru Technological University Hyderabad, India. He has published numerous peer-reviewed articles in journals dealing with thermal fluid problems having applications in the cooling of electronic components, condensation in the presence of air, nucleate boiling heat transfer, solar thermal energy conversion, flow and heat transfer in a porous medium, single-phase and two-phase heat transfer under turbulent flow in the process of guiding 20 doctoral students. 

Kyathanahalli Marigowda Yashawantha

Kyathanahalli Marigowda Yashawantha is working as a junior research fellow in the chemical engineering department, NIT, Warangal, India. He has published numerous peer-reviewed articles in journals dealing with thermal fluid properties having applications in the cooling of automobile components. 

Siddeswara Dmk

Siddeswara DMKworking as an assistant professor in the chemistry department at Jyothy institute of technology, Bangalore, Karnataka, India. He has numerous research papers to his credit in peer-reviewed journals. His area of interest includes battery technology, nanoparticle synthesis, and ionic fluids.