dEjournals Nyitóoldal
International Journal of Engineering and Management Sciences
Regisztráció Aktuális szám Megjelenés előtti cikkek Archívum Indexáltság Beküldés Kapcsolat Keresés
Regisztráció Bejelentkezés
IJEMS

Megjelenés előtti cikkek
Gépészeti és járműmérnöki tudományok

Thermoelastic Analysis of Functionally Graded Spherical Bodies Using Deep Neural Networks

Megjelent:
2025-10-18
DOI Linkhttps://doi.org/10.21791/IJEMS.2025.19
Szerző
Gönczi Dávid
Megtekintés
PDF (Angol)
Kulcsszavak
FGM Sphere Neural Networks FEM Thermoelasticity
Licenc

Copyright (c) 2025 Dávid Gönczi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Hogyan hivatkozzuk
Kiválasztott formátum: APA
Gönczi, D. (2025). Thermoelastic Analysis of Functionally Graded Spherical Bodies Using Deep Neural Networks. International Journal of Engineering and Management Sciences, 1-10. https://doi.org/10.21791/IJEMS.2025.19
Beküldött 2025-08-19
Elfogadott 2025-10-16
Publikált 2025-10-18
Absztrakt

This paper deals with the numerical analysis of functionally graded spherical bodies subjected to combined thermal and mechanical loads. A method is presented to train deep neural networks to approximate the important solutions. We outline two approaches for generating the training dataset for a deep neural network, followed by a method for creating the neural network itself. Then, through a numerical example, we investigate the axisymmetric problems of radially graded spherical bodies (e.g., ideal spherical pressure vessels). Based on the results obtained, we evaluate the accuracy of solving the outlined problem using the proposed neural network.

Hivatkozások
  1. [1] H-S. Shen, “Functionally Graded Materials: Nonlinear Analysis of Plates and Shells” CRC Press, London, 2009.
  2. [2] R. B. Hetnarski, M. R. Eslami, “Thermal Stresses – Advanced Theory and Applications” Springer, New York, USA, 2010, doi: 10.1007/978-3-030-10436-8
  3. [3] N. Noda, R. B. Hetnarski, Y. Tanigawa, (2000) “Thermal Stresses” Lastran Corporation, Rochester, New York, USA, 2000, doi: 10.1115/1.1349549
  4. [4] L. P. Kiss, “Nonlinear stability analysis of FGM shallow arches under an arbitrary concentrated radial force” International Journal of Mechanics and Materials in Design, vol. 16, no. 1, pp. 91-108., 2020, doi: 10.1007/s10999-019-09460-2
  5. [5] S. Rahman, Md. Afsar Ali, “A novel approach to optimize material distributions of rotating functionally graded circular disk under minimum and prescribed stresses” Materials Today Communications, vol. 36, 2023, doi: 10.1016/j.mtcomm.2023.106620.
  6. [6] M. D. Demirbaş, D. Çakır (sofuoğlu), “Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem” International Scientific and Vocational Studies Journal, vol. 6, no. 2, pp. 103-115., 2022, doi: 10.47897/bilmes.1207256
  7. [7] D. Gönczi, “Thermoelastic Problems of Multilayered Spherical Pressure Vessels Subjected to Axisymmetric Loading”, International Journal of Engineering and Management Sciences, vol. 5, no. 3, pp. 106-115, 2020, doi: 10.21791/IJEMS.2020.3.11.
  8. [8] D. Gönczi, “Thermoelastic analysis of functionally graded anisotropic rotating disks and radially graded spherical pressure vessels” Journal of Computational and Applied Mechanics, vol. 19, no. 2, pp. 85-104., 2024, doi: 10.32973/jcam.2024.004
  9. [9] Yıldırım, D. Yarımpabuç, V. Arikan, M. Eker, K. Celebi, “Nonlinear thermal stress analysis of functionally graded spherical pressure vessels” International Journal of Pressure Vessels and Piping, vol. 200, 2022, doi: 10.1016/j.ijpvp.2022.104830.
  10. [10] M. Raj, P. Kumbhar, R. K. Annabattula, “Physics-informed neural networks for solving thermo-mechanics problems of functionally graded material” arXiv preprint arXiv:2111.10751., 2021
  11. [11] X. Wu, Q. Chen, W. He, A. Almadhor, M. Abbas, “Unraveling thermoelastic performance of the multi-directional functionally graded composite structure by data-driven deep-learning approach” Mechanics of Advanced Materials and Structures, vol. 31, no. 26, pp. 8465-8487, 2024, doi: 10.1080/15376494.2023.2259902
  12. [12] K. P. Afsal, K. Swaminathan, N. Indu, “A novel EFG meshless-ANN approach for static analysis of FGM plates based on the higher-order theory” Mechanics of Advanced Materials and Structures, vol. 31, no. 25, pp. 6501-6517, 2024, doi: 10.1080/15376494.2023.2231459
  13. [13] N. G. Luu, T. T. Banh, “Static, dynamic and stability analysis of multi-dimensional functional graded plate with variable thickness using deep neural network” arXiv preprint, 2023, doi: arXiv:2301.05900.
  14. [14] E. Öner, B. Şengül Şabano, E. Uzun Yaylacı, G. Adıyaman, M. Yaylacı, A. Birinci, “On the plane receding contact between two functionally graded layers using computational, finite element and artificial neural network methods” ZAMM‐Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 102(2), e202100287, 2022, doi: 10.1002/zamm.202100287
  15. [15] Abaqus 6.13 online documentation, Dassault Systems, 2015, Accessed: 5, 5, 2025
  16. [16] Abaqus 2016. Scripting user’s guide, Dassault Systems, 2016, Accessed: 5, 5, 2025
  17. [17] TensorFlow v2.16 online documentation, www.tensorflow.org/api_docs, Accessed: 5, 5, 2025
Adatbázis logók
DOAJ MTMT SHERPA EIRHPLUS EBSCO CROSSREF Ulrichsweb Dimensions AI Scilit BASE miar OpenAire Index Copernicus Vrije Universiteit Amsterdam