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Topology optimization using the discrete element method. Part 1: Methodology, validation, and geometric nonlinearity

O'Shaughnessy, C., Masoero, E. and Gosling, P.D. 2022. Topology optimization using the discrete element method. Part 1: Methodology, validation, and geometric nonlinearity. Meccanica 57 (6) , pp. 1213-1231. 10.1007/s11012-022-01493-w

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Structural Topology optimization is attracting increasing attention as a complement to additive manufacturing techniques. The optimization algorithms usually employ continuum-based Finite Element analyses, but some important materials and processes are better described by discrete models, for example granular materials, powder-based 3D printing, or structural collapse. To address these systems, we adapt the established framework of SIMP Topology optimization to address a system modelled with the Discrete Element Method. We consider a typical problem of stiffness maximization for which we define objective function and related sensitivity for the Discrete Element framework. The method is validated for simply supported beams discretized as interacting particles, whose predicted optimum solutions match those from a classical continuum-based algorithm. A parametric study then highlights the effects of mesh dependence and filtering. An advantage of the Discrete Element Method is that geometric nonlinearity is captured without additional complexity; this is illustrated when changing the beam supports from rollers to hinges, which indeed generates different optimum structures. The proposed Discrete Element Topology Optimization method enables future incorporation of nonlinear interactions, as well as discontinuous processes such as during fracture or collapse.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher: Springer
ISSN: 0025-6455
Funders: EPSRC
Date of First Compliant Deposit: 17 May 2022
Date of Acceptance: 8 February 2022
Last Modified: 17 May 2023 21:23

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