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International Journal on Interactive Design and Manufacturing (IJIDeM)

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10-04-2023 | Original Paper

Prediction of thermophysical properties of hybrid nanofluids using machine learning algorithms

Authors: S. Bhanuteja, V. Srinivas, Ch. V. K. N. S. N. Moorthy, S. Jai Kumar, B. Lakshmipathi Lakshmipathi Raju

Published in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Issue 9/2024

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Abstract

The current research focuses on identifying machine learning algorithms that provide results with high accuracy. The present work is conducted in three phases: conduction of heat transfer experiments, development of correlation, implementation, and comparison of machine learning algorithms with the correlation. Experiments were conducted using hybrid nanofluids with graphene platelets, and carbon nanotubes dispersed in Ethylene glycol-water mixtures. Ethylene glycol percentage in the base fluid varied from 0 to 100%. The nanoparticles are dispersed in concentrations of 0.5, 0.25, 0.125, and 0.0625 weight fractions. The results achieved a 15 to 24% enhancement in thermal conductivity. Results showed viscosity increased in temperatures ranging from 50 to 70 °C but less in higher temperatures. Correlation formulas were developed, and they predicted the thermal conductivity and viscosity values with a maximum deviation of 10%. Machine learning (ML) models have been implemented, and a comparative analysis with correlation results has been conducted. These ML models provided results with a maximum deviation of 4% for viscosity and 3% for thermal conductivity.

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Title: Prediction of thermophysical properties of hybrid nanofluids using machine learning algorithms
Authors: S. Bhanuteja
V. Srinivas
Ch. V. K. N. S. N. Moorthy
S. Jai Kumar
B. Lakshmipathi Lakshmipathi Raju
Publication date: 10-04-2023
Publisher: Springer Paris
Published in: International Journal on Interactive Design and Manufacturing (IJIDeM) / Issue 9/2024
Print ISSN: 1955-2513
Electronic ISSN: 1955-2505
DOI: https://doi.org/10.1007/s12008-023-01293-w

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Prediction of thermophysical properties of hybrid nanofluids using machine learning algorithms

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Other articles of this Issue 9/2024

Artifical intelligence inspired approach to numerically investigate chip morphology in machining AISI630

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Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Artifical intelligence inspired approach to numerically investigate chip morphology in machining AISI630

Effect of different tool pin geometries on force, torque, microstructure and mechanical properties of friction stir welded acrylonitrile butadiene styrene and polycarbonate joints

Box–Behnken based investigation of surface quality and tool wear rate and FEM analysis of tool wear in TiAlN/CrN coated carbide tool

A comparative study of machine learning algorithms in the prediction of bead geometry in wire-arc additive manufacturing

Simplified optimal design of NU202 cylindrical roller bearing and validation through GA

Influence of axial thermal variation on modal behavior of axially functionally graded material beam