Truncated hierarchical B-spline–based topology optimization

This work presents a truncated hierarchical B-spline–based topology optimization (THB-TO) to address topology optimization (TO) for both minimum compliance and compliant mechanism. The sensitivity and density filters with a lower bound are adaptively consistent with the hierarchical levels of active elements to remove the checkboard pattern and reduce the gray transition area. By means of the maximum variation of design variables on two consecutive iterative steps and the density differences of adjacent active elements, a mark strategy is established, which triggers the hierarchical local refinement and identifies the elements to be refined during the course of THB-TO. Besides, a locally refined design space is constructed in terms of the parent–child relationship of the cells on consecutive hierarchical levels. Numerical examples are used to verify the effectiveness of the proposed THB-TO, where the resolution around the boundary of the optimized designs can be effectively improved by THB-TO. Compared with global refinement, the number of degree of freedoms (DOFs) and design variables are largely decreased for 2D and 3D cases by THB-TO, which demonstrates that the proposed THB-TO is a promising approach to solving 2D and 3D TO problems.

[1]  J. Petersson,et al.  Numerical instabilities in topology optimization: A survey on procedures dealing with checkerboards, mesh-dependencies and local minima , 1998 .

[2]  Rafael Vázquez,et al.  Algorithms for the implementation of adaptive isogeometric methods using hierarchical splines , 2016 .

[3]  Mei Yulin,et al.  A level set method for structural topology optimization and its applications , 2004 .

[4]  Alessandro Reali,et al.  Suitably graded THB-spline refinement and coarsening: Towards an adaptive isogeometric analysis of additive manufacturing processes , 2018, Computer Methods in Applied Mechanics and Engineering.

[5]  Ning Jiang,et al.  A hierarchical spline based isogeometric topology optimization using moving morphable components , 2020 .

[6]  Hendrik Speleers,et al.  THB-splines: The truncated basis for hierarchical splines , 2012, Comput. Aided Geom. Des..

[7]  H. R. Atri,et al.  Meshfree truncated hierarchical refinement for isogeometric analysis , 2018 .

[8]  Xuan Wang,et al.  Explicit Isogeometric Topology Optimization Using Moving Morphable Components (MMC) , 2017 .

[9]  Liang Gao,et al.  A level-set-based topology and shape optimization method for continuum structure under geometric constraints , 2014 .

[10]  C. D. Boor,et al.  On Calculating B-splines , 1972 .

[11]  B. Simeon,et al.  A hierarchical approach to adaptive local refinement in isogeometric analysis , 2011 .

[12]  M. Bendsøe,et al.  Generating optimal topologies in structural design using a homogenization method , 1988 .

[13]  Daniel A. Tortorelli,et al.  Adaptive mesh refinement in stress-constrained topology optimization , 2018, Structural and Multidisciplinary Optimization.

[14]  Yi Min Xie,et al.  Optimal design of periodic structures using evolutionary topology optimization , 2008 .

[15]  Hendrik Speleers,et al.  Adaptive isogeometric analysis with hierarchical box splines , 2017, 1805.01624.

[16]  Shuting Wang,et al.  An isogeometric approach to topology optimization of spatially graded hierarchical structures , 2019, Composite Structures.

[17]  T. Hughes,et al.  Local refinement of analysis-suitable T-splines , 2012 .

[18]  Y. Xie,et al.  A simple evolutionary procedure for structural optimization , 1993 .

[19]  Xianda Xie,et al.  A new isogeometric topology optimization using moving morphable components based on R-functions and collocation schemes , 2018, Computer Methods in Applied Mechanics and Engineering.

[20]  K. Maute,et al.  Conceptual design of aeroelastic structures by topology optimization , 2004 .

[21]  Chao Mei,et al.  GPU parallel strategy for parameterized LSM-based topology optimization using isogeometric analysis , 2017 .

[22]  T. Shi,et al.  Topology optimization with pressure load through a level set method , 2015 .

[23]  Xiaoping Qian,et al.  Topology optimization in B-spline space , 2013 .

[24]  Carlotta Giannelli,et al.  Adaptive isogeometric methods with hierarchical splines: Optimality and convergence rates , 2017 .

[25]  Trond Kvamsdal,et al.  Isogeometric analysis using LR B-splines , 2014 .

[26]  M. Bruggi,et al.  A fully adaptive topology optimization algorithm with goal-oriented error control , 2011 .

[27]  D. Benson,et al.  Geometrically constrained isogeometric parameterized level-set based topology optimization via trimmed elements , 2016 .

[28]  Markus Kästner,et al.  Bézier extraction and adaptive refinement of truncated hierarchical NURBS , 2016 .

[29]  Andres Tovar,et al.  An efficient 3D topology optimization code written in Matlab , 2014 .

[30]  Liang Gao,et al.  Eigenvalue topology optimization of structures using a parameterized level set method , 2014 .

[31]  Marcelo Krajnc Alves,et al.  Layout optimization with h‐adaptivity of structures , 2003 .

[32]  Wei Chen,et al.  Concurrent topology optimization of multiscale structures with multiple porous materials under random field loading uncertainty , 2017, Structural and Multidisciplinary Optimization.

[33]  Chen Yu,et al.  A CAD/CAE incorporate software framework using a unified representation architecture , 2015, Adv. Eng. Softw..

[34]  Ashok V. Kumar,et al.  Topology optimization using B-spline finite elements , 2011 .

[35]  Jian Zhang,et al.  A new topology optimization approach based on Moving Morphable Components (MMC) and the ersatz material model , 2016 .

[36]  R. Stainko An adaptive multilevel approach to the minimal compliance problem in topology optimization , 2005 .

[37]  Anders Clausen,et al.  Efficient topology optimization in MATLAB using 88 lines of code , 2011 .

[38]  Hendrik Speleers,et al.  Strongly stable bases for adaptively refined multilevel spline spaces , 2014, Adv. Comput. Math..

[39]  Lei Li,et al.  Uniform thickness control without pre-specifying the length scale target under the level set topology optimization framework , 2018, Adv. Eng. Softw..

[40]  Peng Hao,et al.  Isogeometric analysis based topology optimization design with global stress constraint , 2018, Computer Methods in Applied Mechanics and Engineering.

[41]  D. Benson,et al.  Isogeometric analysis for parameterized LSM-based structural topology optimization , 2016 .

[42]  Yingjun Wang,et al.  A triple acceleration method for topology optimization , 2019, Structural and Multidisciplinary Optimization.

[43]  Liang Gao,et al.  Topology optimization for multiscale design of porous composites with multi-domain microstructures , 2019, Computer Methods in Applied Mechanics and Engineering.

[44]  T. E. Bruns,et al.  Topology optimization of non-linear elastic structures and compliant mechanisms , 2001 .

[45]  D. Tortorelli,et al.  A geometry projection method for continuum-based topology optimization with discrete elements , 2015 .

[46]  M. Bendsøe Optimal shape design as a material distribution problem , 1989 .

[47]  Zhen-Pei Wang,et al.  Optimal form and size characterization of planar isotropic petal-shaped auxetics with tunable effective properties using IGA , 2018, Composite Structures.

[48]  E. Ramm,et al.  Adaptive topology optimization , 1995 .

[49]  T. Hughes,et al.  Isogeometric analysis : CAD, finite elements, NURBS, exact geometry and mesh refinement , 2005 .

[50]  Yilin Zhu,et al.  Systematic design of tetra-petals auxetic structures with stiffness constraint , 2019, Materials & Design.

[51]  James K. Guest,et al.  Achieving minimum length scale in topology optimization using nodal design variables and projection functions , 2004 .

[52]  Sung-Kie Youn,et al.  Isogeometric topology optimization using trimmed spline surfaces , 2010 .

[53]  Ole Sigmund,et al.  A 99 line topology optimization code written in Matlab , 2001 .

[54]  Hua Xu,et al.  Graphics processing unit (GPU) accelerated fast multipole BEM with level-skip M2L for 3D elasticity problems , 2015, Adv. Eng. Softw..

[55]  John A. Evans,et al.  An Isogeometric design-through-analysis methodology based on adaptive hierarchical refinement of NURBS, immersed boundary methods, and T-spline CAD surfaces , 2012 .

[56]  O. Sigmund,et al.  Topology optimization approaches , 2013, Structural and Multidisciplinary Optimization.

[57]  G. Allaire,et al.  Structural optimization using sensitivity analysis and a level-set method , 2004 .

[58]  H. Nguyen-Xuan A polytree‐based adaptive polygonal finite element method for topology optimization , 2017 .

[59]  Shuting Wang,et al.  Level set-based isogeometric topology optimization for maximizing fundamental eigenfrequency , 2019, Frontiers of Mechanical Engineering.

[60]  Jihong Zhu,et al.  A comprehensive study of feature definitions with solids and voids for topology optimization , 2017 .

[61]  K. Svanberg The method of moving asymptotes—a new method for structural optimization , 1987 .

[62]  Khanh N. Chau,et al.  A polytree-based adaptive polygonal finite element method for multi-material topology optimization , 2017 .

[63]  C.-Y. Lin,et al.  A two-stage approach for structural topology optimization , 1999 .

[64]  Xu Guo,et al.  Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach , 2017, 1704.06060.

[65]  Xiaoping Qian,et al.  Full analytical sensitivities in NURBS based isogeometric shape optimization , 2010 .

[66]  Jie Gao,et al.  Topology optimization for auxetic metamaterials based on isogeometric analysis , 2019, Computer Methods in Applied Mechanics and Engineering.

[67]  Damiano Pasini,et al.  Hip Implant Design With Three-Dimensional Porous Architecture of Optimized Graded Density , 2018, Journal of Mechanical Design.

[68]  Z. Kang,et al.  An adaptive refinement approach for topology optimization based on separated density field description , 2013 .

[69]  Qui X. Lieu,et al.  A multi-resolution approach for multi-material topology optimization based on isogeometric analysis , 2017 .

[70]  Weihong Zhang,et al.  Feature-driven topology optimization method with signed distance function , 2016 .