International Scientific Journal
Thermal Science - Online First
online first only
Machine-learning based thermal conductivity prediction of propylene glycol solutions: Real time heat propagation approach
The objective of this paper is to evaluate the capability of an Artificial Neural Network 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.
PAPER SUBMITTED: 2022-03-11
PAPER REVISED: 2022-06-21
PAPER ACCEPTED: 2022-10-12
PUBLISHED ONLINE: 2023-03-11
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