THERMAL SCIENCE

International Scientific Journal

Andrew Jarrett

,

Ashwin Kodibagkar

,

Dugan Um

,

Denise P. Simmons

,

Tae-Youl Choi

MACHINE-LEARNING BASED THERMAL CONDUCTIVITY PREDICTION OF PROPYLENE GLYCOL SOLUTIONS: REAL TIME HEAT PROPAGATION APPROACH

ABSTRACT
The objective of this paper is to evaluate the capability of an ANN to classify the thermal conductivity of water-glycol mixture in various concentrations. Massive training/validation/test temperature data were created by using a COMSOL model for geometry including a micropipette thermal sensor in an infinite media (i.e., water-glycol mixture) where a 500 μs laser pulse is irradiated at the tip. The randomly generated temporal profile of the temperature dataset was then fed into a trained ANN to classify the thermal conductivity of the mixtures, whose value would be used to distinguish the glycol concentration at a sensitivity of 0.2% concentration with an accuracy of 96.5%. Training of the ANN yielded an overall classification accuracy of 99.99% after 108 epochs.
KEYWORDS
artificial neural networks, classification, Temperature Profiles, thermal conductivity, heat transfer
PAPER SUBMITTED: 2022-03-11
PAPER REVISED: 2022-06-21
PAPER ACCEPTED: 2022-10-12
PUBLISHED ONLINE: 2023-03-11
DOI REFERENCE: https://doi.org/10.2298/TSCI220311039J
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 4, PAGES [2925 - 2933]
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Machine-learning based thermal conductivity prediction of propylene glycol solutions: Real time heat propagation approach

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence