From Experiments to AI: A Comparative Review of Machine Learning Approaches for Predicting Nanofluid Thermophysical Properties.

Salim Al Jadidi, Rekha Moolya, Rajendra Padidhapu, Sivasubramanian Subramanian, Shivananda Moolya

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In one paragraph

Review in Nanomaterials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

5 authors.

Salim Al JadidiDepartment of Engineering, College of Engineering and Technology, University of Technology and Applied Sciences, Muscat 133, Oman.ORCID 0009-0007-2958-4790

Rekha MoolyaVTU(RC)CMR Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi 590018, India.ORCID 0009-0002-9737-2822

Rajendra PadidhapuVTU(RC)CMR Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi 590018, India.

Sivasubramanian SubramanianDepartment of Engineering, College of Engineering and Technology, University of Technology and Applied Sciences, Muscat 133, Oman.

Shivananda MoolyaDepartment of Engineering, College of Engineering and Technology, University of Technology and Applied Sciences, Muscat 133, Oman.ORCID 0000-0002-4714-8569

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The applications of nanofluids are widely beneficial in heat transmission and cooling systems. Nanofluid viscosity and thermal conductivity have a substantial effect on heat transfer applications and on devices such as solar and geothermal systems. Machine learning models enable faster, less expensive modeling of nanofluid thermophysical properties. These models are secure for future studies and in the development of nanotechnology. In this review, shape, size, temperature, and volume concentration are considered as inputs to develop several machine learning methods, such as artificial neural networks, support vector regression, decision trees, and random forests. These models were analyzed by comparing their R

Indexed as

machine learningnanofluidsthermophysical propertyviscosity

Identifiers

PMID41745189

DOI10.3390/nano16040272

PMCPMC12943064

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