Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

Abstract This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists...

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Bibliographic Details
Main Authors: Farag M. A. Altalbawy, Ahmad Alkhayyat, Ramdevsinh Jhala, Anupam Yadav, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Vladimir Vladimirovich Sinitsin
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Nanofluid
Heat transfer
K-Nearest neighbors
Gaussian process regression
Multi-layer perceptron
Online Access:https://doi.org/10.1038/s41598-025-03187-1
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https://doi.org/10.1038/s41598-025-03187-1

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