A computational framework for the indirect estimation of interface thermal resistance of composite materials using XPINNs
International Journal of Heat and Mass Transfer, ISSN: 0017-9310, Vol: 200, Page: 123420
2023
- 16Citations
- 8Captures
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Article Description
The development of Physics-Informed Neural Networks (PINNs) over the recent years has offered a promising avenue for the solution of partial differential equations, as well as for the identification of unknown equation parameters. This work focuses on the application of PINNs, and in particular, their variation called eXtended PINNs (XPINNs) for the determination of the Kapitza thermal resistance at the interface between the different phases in a multiphase composite material. This phenomenological model parameter is almost impossible to measure experimentally, however the proposed framework successfully overcomes this difficulty since it only requires measurements of the temperature at the interior of the composite that are easy to obtain. The task of fine tuning the XPINN related hyperparameters is successfully addressed by employing a Bayesian hyperparameter optimisation scheme based on Gaussian process regression. Benchmark numerical examples are provided that demonstrate the high accuracy, ease of implementation and robustness of the proposed computational framework in capturing the true values of the Kapitza resistance.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0017931022008894; http://dx.doi.org/10.1016/j.ijheatmasstransfer.2022.123420; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85139822301&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0017931022008894; https://dx.doi.org/10.1016/j.ijheatmasstransfer.2022.123420
Elsevier BV
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