Giga-voxel computational morphogenesis for structural design

We report on a supercomputer-based morphogenesis tool that allows giga-voxel resolution – more than two orders of magnitude higher than previously reported

Niels Aage; Erik Andreassen; Boyan S. Lazarov; Ole Sigmund

2017

Scholarcy highlights

  • Citing this paper Please note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscript or Proof version this may differ from the final Published version
  • A compelling synthesis approach allowing for unrestricted design freedom, called topology optimization, is showing great promise with regards to weight savings but its applicability has so far been limited to component design or simple structures
  • Current state-of-the-art implementations limit voxel numbers to a few millions and render applications to detailed high-rise building, oil-rig or large airplane design impossible. This provokes the question: “Will giga-voxel resolution computational morphogenesis lead to radical design changes?” If so, significant weight‐savings and reduced environmental impact will follow. We investigate this question using high performance computing and demonstrate the potential of giga-scale morphogenesis on a full-scale wing design problem for a Boeing 777 type aircraft shown in Fig. 1; Extended Data Figs. 2 and 3 as well as Supplementary images 1 and 2
  • Computing time and resources reported in this paper provide a severe bottleneck and will, for the time being, limit the use to early design stages in research intensive companies and academia
  • To demonstrate the iterative procedure of the topology optimization method we present the solution to a simple part-design problem in Extended Data Fig. 1
  • To quantify the performance improvement of the optimized rib/spar design compared to the conventional rib/spar design, we subject both models to the two aerodynamical load scenarios as described and depicted in Extended Data Fig. 2, i.e. the same two load cases used in the initial topology optimization

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