전산구조공학 (Computational Structural Engineering)

한국전산구조공학회 (Computational Structural Engineering Institute of Korea)
권호 표지

메타모델링 기반 구조 최적화 기법의 현재 연구 동향

Current Research Trends in Metamodeling Based Structural Optimization

  • 오영택 (울산과학기술원 기계공학과 ) ;
  • 정하영 (울산과학기술원 기계공학과 )
  • 발행 : 2023.09.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

키워드

참고문헌

  1. Du, X., Amrit, A., Thelen, A. S., Leifsson, L. T., Zhang, Y., Han, Z. H., & Koziel, S. (2017). Aerodynamic Design of a Rectangular Wing in Subsonic Inviscid Flow by Direct and Surrogate-based Optimization. In 35th AIAA Applied Aerodynamics Conference (p. 4366).
  2. Dugre, A. (2014). A design process using topology optimization applied to flat pressurized stiffened panels (Doctoral dissertation, Ecole Polytechnique de Montreal).
  3. Sigmund, O. (2011). On the usefulness of non-gradient approaches in topology optimization. Structural and Multidisciplinary Optimization, 43, 589-596. https://doi.org/10.1007/s00158-011-0638-7
  4. 유승화 (2022) 데이터 기반 소재 및 구조 최적화 방법 소개, 전산구조공학회지, 제35권 제1호.
  5. Wang, G. G., & Shan, S. (2006). Review of metamodeling techniques in support of engineering design optimization. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 4255, pp. 415-426).
  6. Liu,J., Song,W. P., Han, Z. H., &Zhang, Y.(2017). Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models. Structural and Multidisciplinary Optimization, 55, 925-943. https://doi.org/10.1007/s00158-016-1546-7
  7. Luo, Y., Xing,J., & Kang, Z. (2020). Topology optimization using material-field series expansion and Kriging-based algorithm: An effective non-gradient method. Computer Methods in Applied Mechanics and Engineering, 364, 112966.
  8. Y. Luo,J. Xing, Z. Kang,J.Zhan, M.Li. (2021). Uncertainty of membrane wrinkling behaviors considering initial thickness imperfections., International Journal of Solids and Structures, 191-192 264-277.
  9. Y. Oh, H. Chung (2023) Design of Bio-inspired Material with High Energy Absorption Capability Using Bayesian Optimization and Voronoi Tessellation., WCSMO-15 2023, Jun 05-09, 2023, Cork, Ireland.
  10. Raponi,E., Bujny, M., Olhofer, M., Aulig, N., Boria, S., & Duddeck, F.(2019). Kriging-assisted topology optimization of crash structures. Computer Methods in Applied Mechanics and Engineering, 348, 730-752. https://doi.org/10.1016/j.cma.2019.02.002
  11. Shin, D., Cupertino, A., de Jong, M. H., Steeneken, P. G., Bessa, M. A., & Norte, R. A. (2022). Spiderweb nanomechanical resonators via bayesian optimization: inspired by nature and guided by machine learning. Advanced Materials, 34(3), 2106248.
  12. Liu, X., Gao, L., Xiao, M., & Zhang, Y. (2022). Kriging-assisted design of functionally graded cellular structures with smoothly-varying lattice unit cells. Computer Methods in Applied Mechanics and Engineering, 390, 114466.
  13. Xiong, F., Ren, C., Mo, B., Li, C., & Hu, X. (2023). A new adaptive multi-fidelity metamodel method using meta-learning and Bayesian deep learning. Structural and Multidisciplinary Optimization, 66(3), 58.
  14. Chung, H., Hwang, J. T., Gray, J. S., & Kim, H. A. (2019). Topology optimization in OpenMDAO. Structural and multidisciplinary optimization, 59, 1385-1400. https://doi.org/10.1007/s00158-019-02209-7
  15. Guibert, A.T., Hyun,J., Neofytou, A.,&Kim, H. A.(2023). Implementation of a plug-and-play reusable level-set topology optimization framework via COMSOL Multiphysics. In AIAA SCITECH 2023 Forum (p. 1675).