Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 513-524 |
| Seitenumfang | 12 |
| Fachzeitschrift | Computational mechanics |
| Jahrgang | 72 |
| Ausgabenummer | 3 |
| Frühes Online-Datum | 6 Apr. 2023 |
| Publikationsstatus | Veröffentlicht - Sept. 2023 |
Abstract
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 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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Numerische Mechanik
- Ingenieurwesen (insg.)
- Meerestechnik
- Ingenieurwesen (insg.)
- Maschinenbau
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Mathematik (insg.)
- Computational Mathematics
- Mathematik (insg.)
- Angewandte Mathematik
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in: Computational mechanics, Jahrgang 72, Nr. 3, 09.2023, S. 513-524.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials
AU - Guo, Hongwei
AU - Zhuang, Xiaoying
AU - Fu, Xiaolong
AU - Zhu, Yunzheng
AU - Rabczuk, Timon
N1 - Funding Information: The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saudi University for funding this work.
PY - 2023/9
Y1 - 2023/9
N2 - 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 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.
AB - 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 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.
KW - Activation function
KW - Deep learning
KW - Discontinuous time scheme
KW - Functionally graded materials
KW - Heat transfer
KW - Physics-informed
UR - http://www.scopus.com/inward/record.url?scp=85152061460&partnerID=8YFLogxK
U2 - 10.1007/s00466-023-02287-x
DO - 10.1007/s00466-023-02287-x
M3 - Article
AN - SCOPUS:85152061460
VL - 72
SP - 513
EP - 524
JO - Computational mechanics
JF - Computational mechanics
SN - 0178-7675
IS - 3
ER -