Open Access

ARTICLE

Artificial Neural Network Model for Thermal Conductivity Estimation of Metal Oxide Water-Based Nanofluids

Nikhil S. Mane¹, Sheetal Kumar Dewangan^2,*, Sayantan Mukherjee³, Pradnyavati Mane⁴, Deepak Kumar Singh¹, Ravindra Singh Saluja⁵

1 School of Engineering, Ajeenkya DY Patil University, Pune, 41210, India
2 Department of Material Science & Engineering, Ajou University, Suwon-si, 16499, Republic of Korea
3 Department of Mechanical Engineering, Gandhi Academy of Technology and Engineering, Brahmapur, 761008, India
4 Department of Engineering Sciences, Ajeenkya D Y Patil School of Engineering, Pune, 412210, India
5 Department of Mechanical Engineering, School of Engineering, OP Jindal University, Punjipathra, Raigarh, 496019, India

* Corresponding Author: Sheetal Kumar Dewangan. Email:

(This article belongs to the Special Issue: Applications of Neural Networks in Materials)

Computers, Materials & Continua 2026, 86(1), 1-16. https://doi.org/10.32604/cmc.2025.072090

Received 19 August 2025; Accepted 29 September 2025; Issue published 10 November 2025

Abstract

The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids. Researchers rely on experimental investigations to explore nanofluid properties, as it is a necessary step before their practical application. As these investigations are time and resource-consuming undertakings, an effective prediction model can significantly improve the efficiency of research operations. In this work, an Artificial Neural Network (ANN) model is developed to predict the thermal conductivity of metal oxide water-based nanofluid. For this, a comprehensive set of 691 data points was collected from the literature. This dataset is split into training (70%), validation (15%), and testing (15%) and used to train the ANN model. The developed model is a backpropagation artificial neural network with a 4–12–1 architecture. The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence. It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.

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Supplementary Material

Supplementary Material File

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