- Treffer 1 von 1
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.…
Dokumentart: | Artikel (Wissenschaftlicher) |
---|---|
Verfasserangaben: | Hongwei Guo, Naif Alajlan, Xiaoying Zhuang, Prof. Dr.-Ing. Timon RabczukORCiDGND |
DOI (Zitierlink): | https://doi.org/10.1007/s00466-023-02287-xZitierlink |
URN (Zitierlink): | https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20230517-63666Zitierlink |
URL: | https://link.springer.com/article/10.1007/s00466-023-02287-x |
Titel des übergeordneten Werkes (Englisch): | Computational Mechanics |
Verlag: | Springer |
Verlagsort: | Berlin |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 09.05.2023 |
Datum der Erstveröffentlichung: | 06.04.2023 |
Datum der Freischaltung: | 17.05.2023 |
Veröffentlichende Institution: | Bauhaus-Universität Weimar |
Institute und Partnereinrichtugen: | Fakultät Bauingenieurwesen / Professur Modellierung und Simulation - Mechanik |
Jahrgang: | 2023 |
Seitenzahl: | 12 |
Erste Seite: | 1 |
Letzte Seite: | 12 |
Freies Schlagwort / Tag: | functionally graded materials; heat transfer; physics-informed activation function |
GND-Schlagwort: | Wärmeübergang; Deep Learning; Modellierung |
DDC-Klassifikation: | 600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften |
BKL-Klassifikation: | 31 Mathematik / 31.80 Angewandte Mathematik |
54 Informatik / 54.80 Angewandte Informatik | |
Lizenz (Deutsch): | Creative Commons 4.0 - Namensnennung (CC BY 4.0) |