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Thermal ablation of materials is a complex phenomenon that involves physical and chemical processes for the thermal protection of systems. However, due to the extreme thermal conditions and moving boundaries, predicting temperature and heat flux at the ablative material is quite challenging. A physics-informed neural network is a promising technique for many such inverse problems, including the prediction of unsteady heat flux. However, traditional physics-informed machine learning algorithms struggle with heat flux predictions in thermal ablation problems due to moving boundary conditions and lack of temperature data in the inaccessible domain. This study presents a hybrid approach, where an artificial neural network (ANN) is used for the accessible domain of the material and a physics-based numerical solution (PNS) technique is used in the inaccessible domain of the material, to find heat flux at the ablative surface. Temperature data at the accessible sensor points are used to train the ANN model. The heat flux at the ablative boundary was iteratively obtained from the numerical solution of the energy equation in the inaccessible domain by matching the ANN-predicted temperature at the last accessible sensor point. Our results indicate that this hybrid methodology significantly outperforms traditional physics-informed machine learning techniques, achieving excellent accuracy in predicting the temperature profiles and heat fluxes under complex conditions for both constant and variable heat flux and properties. By addressing the limitations of conventional physics-informed machine learning methods, our approach provides a robust and reliable solution for modeling the intricate dynamics of ablative processes.
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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 ?s 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.
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- Award ID(s):
- 1906553
- PAR ID:
- 10418390
- Date Published:
- Journal Name:
- Thermal Science
- Issue:
- 00
- ISSN:
- 0354-9836
- Page Range / eLocation ID:
- 39 to 39
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2298/TSCI220311039J
