A machine learning approach coupled with polar coordinate based localized collocation method for inner surface identification in heat conduction problem
Abstract
In the present work, we developed the Neural Networks (NNs) for identifying unknown surface shape of inner wall in the two-dimensional pipeline based on the temperature at uniformly distributed measuring points. The steady-state governing equation is transformed into the anisotropic heat conduction equations, and the irregularly shaped inner boundary is identified by the estimation of circumferential distribution of the effective thermal conductivity of the furnace. After the unhomogenized technique for the effective thermal conductivity model, the meshless generalized finite difference method on a radial is derived to effectively solve the direct problem for training data. The NNs are introduced to calculate the effective thermal conductivity for further detection of unknown internal boundary. Several numerical examples are provided to demonstrate the efficiency and accuracy of the proposed solver.
Details
- Organisationseinheit(en)
-
Institut für Photonik
- Externe Organisation(en)
-
Hohai University
Tongji University
- Typ
- Artikel
- Journal
- Computers and Mathematics with Applications
- Band
- 148
- Seiten
- 41-61
- Anzahl der Seiten
- 21
- ISSN
- 0898-1221
- Publikationsdatum
- 15.10.2023
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Modellierung und Simulation, Theoretische Informatik und Mathematik, Computational Mathematics
- Elektronische Version(en)
-
https://doi.org/10.1016/j.camwa.2023.07.031 (Zugang:
Geschlossen
)
