doi: 10.17586/2226-1494-2023-23-2-430-435


High performance modeling of the stress-strain state of thin-walled shell structures with the use of deep learning

I. N. Zgoda


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Zgoda Iu.N. High performance modeling of the stress-strain state of thin-walled shell structures with the use of deep learning. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 2, pp. 430–435. doi: 10.17586/2226-1494-2023-23-2-430-435


Abstract
Computer modeling is one of the most common approaches to the analysis of thin-walled shell structures stress-strain state analysis. It requires considerable time costs and high-performance hardware, especially when it is necessary to conduct a comparative analysis of various shell configurations. In this paper, we propose the use of deep learning methods to improve performance of this process. The purpose of work is to develop methods for high-performance computer simulation of thin-walled shell structures using deep neural networks, allowing to take into account geometric and physical properties of the structure as well as the load applied to it. A training approach and deep neural network architecture were developed to perform computer modeling of the stress-strain state of the shell. To form a training dataset, a computational experiment was carried out to simulate 3904 different configurations of doubly curved shallow shells that differ in linear dimensions, curvature radii, and materials used. Based on this dataset, 30 deep neural networks with different architectures were trained. To determine the optimal architecture in terms of modeling accuracy, mean absolute percentage error with clipping near-zero samples was calculated for each of the neural networks based on the test dataset. A network has been developed that allows calculating the stress-strain state of different shell configurations under an arbitrary uniformly distributed load. This is the first solution in the field of shell neural network modeling that allows us to vary the applied load, geometric and physical parameters of the shell and obtain calculation results at an arbitrary point of its middle surface. Performance measurements were carried out which show that the developed neural network allows simulating the stress-strain state of a shell structure 2117 times faster compared to the duration of solving the same problem by classical simulation. The modeling error using the network is at an acceptable level. An original architecture of a neural network for modeling the stress-strain state of shells was proposed which, through minor modifications, can be adapted for high-performance modeling of other building structure types. In accordance with the described architecture, a deep neural network was trained which reduces the computation time by several orders of magnitude. The results obtained are of high practical importance for researchers in the field of thin-walled shells modeling since they allow us to significantly reduce the time costs associated with conducting computational experiments. One of the possible applications for developed solution is prototyping of various shell configurations. Once prototyping is complete, the most efficient shell configurations can be explored in detail using classical computer simulation techniques.

Keywords: thin-walled shell structures, stress-strain state, deep learning, neural networks, computer modeling, Julia programming language

References
  1. Karpov V.V. Models of the shells having ribs, reinforcement plates and cutouts. International Journal of Solids and Structures, 2018, vol. 146, pp. 117–135. https://doi.org/10.1016/j.ijsolstr.2018.03.024
  2. Hu N., Feng P., Dai G.-L. Structural art: Past, present and future. Engineering Structures, 2014, vol. 79, pp. 407–416. https://doi.org/10.1016/j.engstruct.2014.08.040
  3. Chai Y., Song Z., Li F. Investigations on the aerothermoelastic properties of composite laminated cylindrical shells with elastic boundaries in supersonic airflow based on the Rayleigh–Ritz method. Aerospace Science and Technology, 2018, vol. 82–83, pp. 534–544. https://doi.org/10.1016/j.ast.2018.09.040
  4. Liu H.-T., Li N. Reliability analysis of autonomous underwater vehicle aft pressure shell for optimal design and strength. Ocean Engineering, 2022, vol. 249, pp. 110906. https://doi.org/10.1016/j.oceaneng.2022.110906
  5. Bevilacqua A., Ciaburro G., Iannace G., Lombardi I., Trematerra A. Acoustic design of a new shell to be placed in the Roman amphitheater located in Santa Maria Capua Vetere. Applied Acoustics, 2022, vol. 187, pp. 108524. https://doi.org/10.1016/j.apacoust.2021.108524
  6. Lopatin A.V., Morozov E.V., Shatov A.V. Axial deformability of the composite lattice cylindrical shell under compressive loading: Application to a load-carrying spacecraft tubular body. Composite Structures, 2016, vol. 146, pp. 201–206. https://doi.org/10.1016/j.compstruct.2016.03.021
  7. Zhang Y., Song H., Yu X., Yang J. Modeling and analysis of forced vibration of the thin-walled cylindrical shell with arbitrary multi-ring hard coating under elastic constraint. Thin-Walled Structures, 2022, vol. 173, pp. 109037. https://doi.org/10.1016/j.tws.2022.109037
  8. Kostopanagiotis C., Kopanos M., Ioakim D., Perros K., Lagaros N.D. Low cost CPU–GPGPU parallel computing in real-world structural engineering. Journal of Building Engineering, 2015, vol. 4, pp. 209–222. https://doi.org/10.1016/j.jobe.2015.09.011
  9. Bezanson J., Edelman A., Karpinski S., Shah V.B. Julia: a fresh approach to numerical computing. SIAM Review, 2017, vol. 59, no. 1, pp. 65–98. https://doi.org/10.1137/141000671
  10. Thai H.T. Machine learning for structural engineering: A state-of-the-art review. Structures, 2022, vol. 38, pp. 448–491. https://doi.org/10.1016/j.istruc.2022.02.003
  11. Mallela U.K., Upadhyay A. Buckling load prediction of laminated composite stiffened panels subjected to in-plane shear using artificial neural networks. Thin-Walled Structures, 2016, vol. 102, pp. 158–164. https://doi.org/10.1016/j.tws.2016.01.025
  12. Sun Z., Lei Z., Bai R., Jiang H., Zou J., Ma Y., Yan C. Prediction of compression buckling load and buckling mode of hat-stiffened panels using artificial neural network. Engineering Structures, 2021, vol. 242, pp. 112275. https://doi.org/10.1016/j.engstruct.2021.112275
  13. Tahir Z.R., Mandal P., Adil M.T., Naz F. Application of artificial neural network to predict buckling load of thin cylindrical shells under axial compression. Engineering Structures, 2021, vol. 248, pp. 113221. https://doi.org/10.1016/j.engstruct.2021.113221
  14. Ribeiro J.P., Tavares S.M., Parente M. Stress-strain evaluation of structural parts using artificial neural networks. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2021, vol. 235, no. 16, pp. 1271–1286. https://doi.org/10.1177/1464420721992445
  15. Innes M., Saba E., Fischer K., Gandhi D., Rudilosso M.C., Joy N.M., Karmali T., Pal A., Shah V. Fashionable modelling with Flux. arXiv, 2018, arXiv.1811.01457. https://doi.org/10.48550/arXiv.1811.01457


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