Abstract: This paper introduces a purely heuristic topology optimization framework to improve specific energy absorption for thin-walled, extruded lattice structures. The framework optimizes the lattice cross section design by iteratively updating the lattice wall thicknesses. The main novelty of the work is the two novel thickness update schemes we proposed. The first update scheme is a direct statement of homogenization of wall-wise specific energy absorption, while the other scheme is based on the homogenization of a wall-wise sensitivity parameter inspired by the bi-directional evolutionary structural optimization method. Both schemes are based on the central idea of homogenization of certain field variables, which has been widely employed in previous optimization frameworks for thin-walled structures. The proposed framework has high potential because it can work directly with commercial finite element packages, and only requires information on the energy absorption of each element. Without the need for the finite element stiffness matrix, this framework can be used with explicit dynamics simulations to treat highly nonlinear problems. Three numerical examples are presented: (1) optimization of a column under axial compression, (2) optimization of a lattice-reinforced beam under dynamic three-point bending, and (3) optimization of a lattice-filled sandwich panel under blast loading. The results show that the framework can effectively increase specific energy absorption with as few as 25 nonlinear finite element simulations. Graphical abstract: [Figure not available: see fulltext.]

Original languageEnglish (US)
Article number308
JournalStructural and Multidisciplinary Optimization
Issue number11
StatePublished - Nov 2022


  • Blast loading
  • High strain rate loading
  • Thin-walled extruded lattice
  • Topology optimization

ASJC Scopus subject areas

  • Software
  • Control and Optimization
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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