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An integrated topology and shape optimization framework for stiffened curved shells by mesh deformation

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Abstract

Topology and shape optimization (TSO) is a powerful technique for achieving high-stiffness configurations of stiffened curved shells. However, it presents a challenge to obtain stiffener layouts that satisfy manufacturing constraints using topology optimization (TO) while also considering the changes in the curved shell shape through shape optimization (SO). There are three main contributions in this paper: (1) A shape-dependent TO method is developed for the design of stiffener layouts on different curved surfaces. (2) An integrated TSO framework is established by combining SO design variables and TO stiffener constraints through a mesh deformation approach. (3) A surrogate model of SO variables to TO result is constructed by a deep neural network (DNN) to accelerate the proposed framework. First, an actual optimization model including shape modeling and shape-dependent TO is established. The shape-dependent TO method is based on anisotropic filters to achieve stiffener constraints. Based on shape variables, mesh deformation is used to move the nodes to achieve the shape modeling without changing the mesh topology, and also to move the anisotropic filter direction to ensure the stiffener constraints. Then, sample points are generated in the shape design space as the input, and the TO results are obtained by the actual optimization model as the output. A DNN surrogate model is constructed and its optimal point is found. Finally, the DNN surrogate model is updated by filling in the optimal points to achieve the integrated TSO. Three engineering examples are carried out to illustrate the effectiveness of the proposed framework, including a spherical shell subjected to external pressure, a conical shell subjected to concentrated forces, and an S-shaped variable cross-sectional curved shell subjected to internal pressure. The results verify the effectiveness and outstanding optimization ability of the integrated TSO framework compared to the conventional methods such as single TO without shape change.

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Data availability

The data that support the findings of this study are available from the corresponding author, [KT], upon reasonable request.

References

  1. Tian K, Huang L, Sun Y, Zhao L, Gao T, Wang B (2022) Combined approximation based numerical vibration correlation technique for axially loaded cylindrical shells. Eur J Mech-A/Solids 93:104553

    Article  MathSciNet  MATH  Google Scholar 

  2. Li Z, Gao T, Tian K, Wang B (2023) Elite-driven surrogate-assisted CMA-ES algorithm by improved lower confidence bound method. Eng Comput 39(4):2543–2563

    Article  Google Scholar 

  3. Li Z, Zhang S, Li H, Tian K, Cheng Z, Chen Y, Wang B (2022) On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks. Adv Eng Inform 53:101689

    Article  Google Scholar 

  4. Tian K, Lai P, Sun Y, Sun W, Cheng Z, Wang B (2023) Efficient buckling analysis and optimization method for rotationally periodic stiffened shells accelerated by Bloch wave method. Eng Struct 276:115395

    Article  Google Scholar 

  5. Tian K, Li Z, Ma X, Zhao H, Zhang J, Wang B (2020) Toward the robust establishment of variable-fidelity surrogate models for hierarchical stiffened shells by two-step adaptive updating approach. Struct Multidiscip Optim 61(4):1515–1528

    Article  Google Scholar 

  6. Tian K, Li Z, Zhang J, Huang L, Wang B (2021) Transfer learning based variable-fidelity surrogate model for shell buckling prediction. Compos Struct 273:114285

    Article  Google Scholar 

  7. Tian K, Wang B, Zhang K, Zhang J, Hao P, Wu Y (2018) Tailoring the optimal load-carrying efficiency of hierarchical stiffened shells by competitive sampling. Thin-Walled Struct 133:216–225

    Article  Google Scholar 

  8. Shi S, Sun Z, Ren M, Chen H, Hu X (2013) Buckling resistance of grid-stiffened carbon-fiber thin-shell structures. Compos B Eng 45(1):888–896

    Article  Google Scholar 

  9. Feng S, Zhang W, Meng L, Xu Z, Chen L (2021) Stiffener layout optimization of shell structures with B-spline parameterization method. Struct Multidiscip Optim 63(6):2637–2651

    Article  MathSciNet  Google Scholar 

  10. Aage N, Andreassen E, Lazarov BS, Sigmund O (2017) Giga-voxel computational morphogenesis for structural design. Nature 550(7674):84–86

    Article  Google Scholar 

  11. Zhou Y, Tian K, Xu S, Wang B (2020) Two-scale buckling topology optimization for grid-stiffened cylindrical shells. Thin-Walled Struct 151:106725

    Article  Google Scholar 

  12. Ding X, Yamazaki K (2005) Adaptive growth technique of stiffener layout pattern for plate and shell structures to achieve minimum compliance. Eng Optim 37(3):259–276

    Article  MathSciNet  Google Scholar 

  13. Savine F, Irisarri FX, Julien C, Vincenti A, Guerin Y (2021) A component-based method for the optimization of stiffener layout on large cylindrical rib-stiffened shell structures. Struct Multidiscip Optim 64(4):1843–1861

    Article  Google Scholar 

  14. Sun Y, Zhou Y, Ke Z, Tian K, Wang B (2022) Stiffener layout optimization framework by isogeometric analysis-based stiffness spreading method. Comput Methods Appl Mech Eng 390:114348

    Article  MathSciNet  MATH  Google Scholar 

  15. Liu S, Li Q, Chen W, Hu R, Tong L (2015) H-DGTP—a Heaviside-function based directional growth topology parameterization for design optimization of stiffener layout and height of thin-walled structures. Struct Multidiscip Optim 52(5):903–913

    Article  MathSciNet  Google Scholar 

  16. Xia Q, Shi T, Wang MY, Liu S (2010) A level set based method for the optimization of cast part. Struct Multidiscip Optim 41(5):735–747

    Article  Google Scholar 

  17. Guest JK, Zhu M (2012) Casting and milling restrictions in topology optimization via projection-based algorithms. In: International design engineering technical conferences and computers and information in engineering conference. Chicago, Illinois, USA. American Society of Mechanical Engineers, vol 45028, pp 913–920

  18. Zhang W, Zhou L (2018) Topology optimization of self-supporting structures with polygon features for additive manufacturing. Comput Methods Appl Mech Eng 334:56–78

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang C, Zhang W, Zhou L, Gao T, Zhu J (2021) Topology optimization of self-supporting structures for additive manufacturing with B-spline parameterization. Comput Methods Appl Mech Eng 374:113599

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhou L, Sigmund O, Zhang W (2021) Self-supporting structure design with feature-driven optimization approach for additive manufacturing. Comput Methods Appl Mech Eng 386:114110

    Article  MathSciNet  MATH  Google Scholar 

  21. Francavilla A, Ramakrishnan CV, Zienkiewicz OC (1975) Optimization of shape to minimize stress concentration. J Strain Anal 10(2):63–70

    Article  Google Scholar 

  22. Liu Y, Shimoda M (2015) Non-parametric shape optimization method for natural vibration design of stiffened shells. Comput Struct 146:20–31

    Article  Google Scholar 

  23. Shi JX, Nagano T, Shimoda M (2017) Fundamental frequency maximization of orthotropic shells using a free-form optimization method. Compos Struct 170:135–145

    Article  Google Scholar 

  24. Haftka RT, Grandhi RV (1986) Structural shape optimization-a survey. Comput Methods Appl Mech Eng 57(1):91–106

    Article  MathSciNet  MATH  Google Scholar 

  25. Wall WA, Frenzel MA, Cyron C (2008) Isogeometric structural shape optimization. Comput Methods Appl Mech Eng 197(33–40):2976–2988

    Article  MathSciNet  MATH  Google Scholar 

  26. Jakobsson S, Amoignon O (2007) Mesh deformation using radial basis functions for gradient-based aerodynamic shape optimization. Comput Fluids 36(6):1119–1136

    Article  MATH  Google Scholar 

  27. Comis Da Ronco C, Ponza R, Benini E (2014) Aerodynamic shape optimization in aeronautics: a fast and effective multi-objective approach. Arch Comput Methods Eng 21(3):189–271

    Article  MathSciNet  MATH  Google Scholar 

  28. López J, Anitescu C, Rabczuk T (2020) CAD-compatible structural shape optimization with a movable Bézier tetrahedral mesh. Comput Methods Appl Mech Eng 367:113066

    Article  MATH  Google Scholar 

  29. Secco NR, Kenway GKW, He P, Mader C, Martins JRRA (2021) Efficient mesh generation and deformation for aerodynamic shape optimization. AIAA J 59(4):1151–1168

    Article  Google Scholar 

  30. Tian K, Li H, Huang L, Huang H, Zhao H, Wang B (2020) Data-driven modelling and optimization of stiffeners on undevelopable curved surfaces. Struct Multidiscip Optim 62(6):3249–3269

    Article  Google Scholar 

  31. Li H, Li Z, Cheng Z, Zhou Z, Wang G, Wang B, Tian K (2022) A data-driven modelling and optimization framework for variable-thickness integrally stiffened shells. Aerosp Sci Technol 129:107839

    Article  Google Scholar 

  32. Huang L, Li H, Zheng K, Tian K, Wang B (2023) Shape optimization method for axisymmetric disks based on mesh deformation and smoothing approaches. Mech Adv Mater Struct 30(12):2532–2555

    Article  Google Scholar 

  33. Huang L, Xia Q, Gao T, Wang B, Tian K (2022) Adaptive step-size numerical vibration correlation technique for buckling prediction of thin-walled shells under axial compression and thermal loads. Multidiscip Model Mater Struct 18(4):635–652

    Article  Google Scholar 

  34. Kumar AV, Gossard DC (1996) Synthesis of optimal shape and topology of structures. J Mech Des 118(1):68–74

    Article  Google Scholar 

  35. Jansen M (2019) Explicit level set and density methods for topology optimization with equivalent minimum length scale constraints. Struct Multidiscip Optim 59(5):1775–1788

    Article  MathSciNet  Google Scholar 

  36. Barrera JL, Geiss MJ, Maute K (2020) Hole seeding in level set topology optimization via density fields. Struct Multidiscip Optim 61(4):1319–1343

    Article  MathSciNet  Google Scholar 

  37. Shimoda M, Nakayama H, Suzaki S, Tsutsumi R (2021) A unified simultaneous shape and topology optimization method for multi-material laminated shell structures. Struct Multidiscip Optim 64(6):3569–3604

    Article  MathSciNet  Google Scholar 

  38. Ho-Nguyen-Tan T, Kim HG (2022) An efficient method for shape and topology optimization of shell structures. Struct Multidiscip Optim 65(4):1–28

    Article  MathSciNet  Google Scholar 

  39. Riehl S, Steinmann P (2015) A staggered approach to shape and topology optimization using the traction method and an evolutionary-type advancing front algorithm. Comput Methods Appl Mech Eng 287:1–30

    Article  MathSciNet  MATH  Google Scholar 

  40. Stankiewicz G, Dev C, Steinmann P (2021) Coupled topology and shape optimization using an embedding domain discretization method. Struct Multidiscip Optim 64(4):2687–2707

    Article  MathSciNet  Google Scholar 

  41. Stankiewicz G, Dev C, Steinmann P (2022) Geometrically nonlinear design of compliant mechanisms: topology and shape optimization with stress and curvature constraints. Comput Methods Appl Mech Eng 397:115161

    Article  MathSciNet  MATH  Google Scholar 

  42. Tang PS, Chang KH (2001) Integration of topology and shape optimization for design of structural components. Struct Multidiscip Optim 22(1):65–82

    Article  Google Scholar 

  43. Chandrasekhar A, Sridhara S, Suresh K (2021) Auto: a framework for automatic differentiation in topology optimization. Struct Multidiscip Optim 64(6):4355–4365

    Article  Google Scholar 

  44. Nørgaard SA, Sagebaum M, Gauger NR, Lazarov BS (2017) Applications of automatic differentiation in topology optimization. Struct Multidiscip Optim 56:1135–1146

    Article  MathSciNet  Google Scholar 

  45. Espath LFR, Linn RV, Awruch AM (2011) Shape optimization of shell structures based on NURBS description using automatic differentiation. Int J Numer Methods Eng 88(7):613–636

    Article  MATH  Google Scholar 

  46. Maute K, Ramm E (1997) Adaptive topology optimization of shell structures. AIAA J 35(11):1767–1773

    Article  MATH  Google Scholar 

  47. Ansola R, Canales J, Tarrago JA, Rasmussen J (2002) An integrated approach for shape and topology optimization of shell structures. Comput Struct 80(5–6):449–458

    Article  Google Scholar 

  48. Ansola R, Canales J, Tarrago JA, Rasmussen J (2004) Combined shape and reinforcement layout optimization of shell structures. Struct Multidiscip Optim 27:219–227

    Article  Google Scholar 

  49. Jiang X, Zhang W, Liu C, Du Z, Guo X (2023) An explicit approach for simultaneous shape and topology optimization of shell structures. Appl Math Model 113:613–639

    Article  Google Scholar 

  50. Bakker C, Zhang L, Higginson K, Keulen FV (2021) Simultaneous optimization of topology and layout of modular stiffeners on shells and plates. Struct Multidiscip Optim 64(5):3147–3161

    Article  MathSciNet  Google Scholar 

  51. Smith HA, Norato JA, Deaton JD (2023) Feature-mapping topology optimization of a wing-box with geometric constraints. In: AIAA SCITECH 2023 forum, National Harbor, MD & Online (p. 1271).

  52. Høghøj LC, Conlan-Smith C, Sigmund O, Andreasen CS (2023) Simultaneous shape and topology optimization of wings. Struct Multidiscip Optim 66(5):116

    Article  Google Scholar 

  53. Mukherjee S, Lu D, Raghavan B, Breitkopf P, Dutta S, Xiao M, Zhang W (2021) Accelerating large-scale topology optimization: state-of-the-art and challenges. Arch Comput Methods Eng 28(7):4549–4571

    Article  MathSciNet  Google Scholar 

  54. White DA, Arrighi WJ, Kudo J, Watts SE (2019) Multiscale topology optimization using neural network surrogate models. Comput Methods Appl Mech Eng 346:1118–1135

    Article  MathSciNet  MATH  Google Scholar 

  55. Singh K, Kapania RK (2021) Accelerated optimization of curvilinearly stiffened panels using deep learning. Thin-Walled Struct 161:107418

    Article  Google Scholar 

  56. Zhang X, Xie F, Ji T, Zhu Z, Zheng Y (2021) Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization. Comput Methods Appl Mech Eng 373:113485

    Article  MathSciNet  MATH  Google Scholar 

  57. Blank J, Deb K (2020) Pymoo: multi-objective optimization in python. IEEE Access 8:89497–89509

    Article  Google Scholar 

  58. Svanberg K (1987) The method of moving asymptotes—a new method for structural optimization. Int J Numer Methods Eng 24(2):359–373

    Article  MathSciNet  MATH  Google Scholar 

  59. Frazier PI (2018) Bayesian optimization. In: Recent advances in optimization and modeling of contemporary problems, published Online, pp 255–278

  60. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: International conference on learning representations (ICLR), San Diego. arXiv preprint. arXiv:1412.6980

  61. Rendall TCS, Allen CB (2009) Efficient mesh motion using radial basis functions with data reduction algorithms. J Comput Phys 228(17):6231–6249

    Article  MATH  Google Scholar 

  62. De Boer A, Van der Schoot MS, Bijl H (2007) Mesh deformation based on radial basis function interpolation. Comput Struct 85(11–14):784–795

    Article  Google Scholar 

  63. Wang G, Mian HH, Ye ZY, Lee JD (2015) Improved point selection method for hybrid-unstructured mesh deformation using radial basis functions. AIAA J 53(4):1016–1025

    Article  Google Scholar 

  64. Heyman J (1977) Equilibrium of shell structures. Oxford University Press, Oxford, pp 134–134

    MATH  Google Scholar 

  65. Systèmes D (2010) Abaqus analysis user’s manual, version 6.10. Dassault Systèmes Simulia Corp., Providence

  66. Tran KL, Douthe C, Sab K, Dallot J, Davaine L (2014) Buckling of stiffened curved panels under uniform axial compression. J Constr Steel Res 103:140–147

    Article  Google Scholar 

  67. Li B, Hong J, Yan S, Liu Z (2017) Topology optimization of stiffened plate/shell structures based on adaptive morphogenesis algorithm. J Manuf Syst 43:375–384

    Article  Google Scholar 

  68. Vatanabe SL, Lippi TN, de Lima CR, Paulino GH, Silva EC (2016) Topology optimization with manufacturing constraints: a unified projection-based approach. Adv Eng Softw 100:97–112

    Article  Google Scholar 

  69. Wang B, Zhou Y, Tian K, Wang G (2020) Novel implementation of extrusion constraint in topology optimization by Helmholtz-type anisotropic filter. Struct Multidiscip Optim 62(4):2091–2100

    Article  Google Scholar 

  70. Lazarov BS, Sigmund O (2011) Filters in topology optimization based on Helmholtz-type differential equations. Int J Numer Methods Eng 86(6):765–781

    Article  MathSciNet  MATH  Google Scholar 

  71. Bendsøe MP (1989) Optimal shape design as a material distribution problem. Struct Optim 1(4):193–202

    Article  Google Scholar 

  72. Rozvany GIN (2001) Aims, scope, methods, history and unified terminology of computer-aided topology optimization in structural mechanics. Struct Multidiscip Optim 21(2):90–108

    Article  MathSciNet  Google Scholar 

  73. Guest JK, Prévost JH, Belytschko T (2004) Achieving minimum length scale in topology optimization using nodal design variables and projection functions. Int J Numer Methods Eng 61(2):238–254

    Article  MathSciNet  MATH  Google Scholar 

  74. Guest JK (2009) Imposing maximum length scale in topology optimization. Struct Multidiscip Optim 37(5):463–473

    Article  MathSciNet  MATH  Google Scholar 

  75. Fernández E, Yang K, Koppen S, Alarcón P, Bauduin S, Duysinx P (2020) Imposing minimum and maximum member size, minimum cavity size, and minimum separation distance between solid members in topology optimization. Comput Methods Appl Mech Eng 368:113157

    Article  MathSciNet  MATH  Google Scholar 

  76. Duysinx P, Sigmund O (1998) New developments in handling stress constraints in optimal material distribution. In: 7th AIAA/USAF/NASA/ISSMO symposium on multidisciplinary analysis and optimization, St. Louis, MO, U.S.A. p 4906

  77. Haykin S (2009) Neural networks and learning machines, 3/E. Prentice Hall, Pearson Education India

  78. Prechelt L (2002) Early stopping-but when? Neural networks: tricks of the trade. Springer, Berlin, pp 55–69

    Google Scholar 

  79. Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21:345–383

    Article  MathSciNet  MATH  Google Scholar 

  80. Cheng W, Wang Z, Zhou L, Sun X, Shi J (2017) Influences of shield ratio on the infrared signature of serpentine nozzle. Aerosp Sci Technol 71:299–311

    Article  Google Scholar 

  81. Lee C, Boedicker C (1985) Subsonic diffuser design and performance for advanced fighter aircraft. In: Aircraft design systems and operations meeting, Colorado Springs, Colorado, p 3073

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Acknowledgements

This work was supported by National Key Research and Development Program project of China (no. 2022YFB3404700), and National Natural Science Foundation of China (no. 11902065, no. 11825202, no. U21A20429).

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Huang, L., Gao, T., Sun, Z. et al. An integrated topology and shape optimization framework for stiffened curved shells by mesh deformation. Engineering with Computers (2023). https://doi.org/10.1007/s00366-023-01887-8

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