Component-wise reduced order model lattice-type structure design
Sean McBane, Youngsoo Choi

TL;DR
This paper introduces a component-wise reduced order modeling approach for topology optimization of lattice structures, achieving significant speedups with minimal accuracy loss and enabling efficient design of lightweight, stiff structures.
Contribution
It proposes a novel component-wise reduced order model for topology optimization that drastically reduces computation time and allows reuse of training data across multiple design problems.
Findings
1000x speedup over full FEM models
Less than 1% relative error in solutions
Effective for designing lightweight, stiff lattice structures
Abstract
Lattice-type structures can provide a combination of stiffness with light weight that is desirable in a variety of applications. Design optimization of these structures must rely on approximations of the governing physics to render solution of a mathematical model feasible. In this paper, we propose a topology optimization (TO) formulation that approximates the governing physics using component-wise reduced order modeling, which can reduce solution time by multiple orders of magnitude over a full-order finite element model while providing a relative error in the solution of less than one percent. In addition, the offline training data set from such component-wise models is reusable, allowing its application to many design problems for only the cost of a single offline training phase, and the component-wise method is nearly embarrassingly parallel. We also show how the parameterization…
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