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Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

  • We present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge–Kutta discrete time scheme. Firstly, the governing equation, associated boundary conditions and the initial condition for transient heat transfer analysis of FGMs with exponential material variations are presented. Then, the deepWe present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge–Kutta discrete time scheme. Firstly, the governing equation, associated boundary conditions and the initial condition for transient heat transfer analysis of FGMs with exponential material variations are presented. Then, the deep collocation method with the Runge–Kutta integration scheme for transient analysis is introduced. The prior physics that helps to generalize the physics-informed deep learning model is introduced by constraining the temperature variable with discrete time schemes and initial/boundary conditions. Further the fitted activation functions suitable for dynamic analysis are presented. Finally, we validate our approach through several numerical examples on FGMs with irregular shapes and a variety of boundary conditions. From numerical experiments, the predicted results with PIDL demonstrate well agreement with analytical solutions and other numerical methods in predicting of both temperature and flux distributions and can be adaptive to transient analysis of FGMs with different shapes, which can be the promising surrogate model in transient dynamic analysis.show moreshow less

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Metadaten
Document Type:Article
Author: Hongwei Guo, Naif Alajlan, Xiaoying Zhuang, Prof. Dr.-Ing. Timon RabczukORCiDGND
DOI (Cite-Link):https://doi.org/10.1007/s00466-023-02287-xCite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20230517-63666Cite-Link
URL:https://link.springer.com/article/10.1007/s00466-023-02287-x
Parent Title (English):Computational Mechanics
Publisher:Springer
Place of publication:Berlin
Language:English
Date of Publication (online):2023/05/09
Date of first Publication:2023/04/06
Release Date:2023/05/17
Publishing Institution:Bauhaus-Universität Weimar
Institutes and partner institutions:Fakultät Bauingenieurwesen / Professur Modellierung und Simulation - Mechanik
Volume:2023
Pagenumber:12
First Page:1
Last Page:12
Tag:functionally graded materials; heat transfer; physics-informed activation function
GND Keyword:Wärmeübergang; Deep Learning; Modellierung
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften
BKL-Classification:31 Mathematik / 31.80 Angewandte Mathematik
54 Informatik / 54.80 Angewandte Informatik
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)