TY - JOUR
AU - Kim, Hyunsun Alicia
AB - The aim of this work is to introduce a unified description of topology optimization (TO) methods, which modularizes and generalizes all TO methods, both density based and boundary based. This unified description allows for the implementation of a reusable modular TO software, ParaLeSTO, which specializes in level set TO (LSTO). In addition, we use this software as a means to propose a guideline for research software metadata in the TO community. The proposed guideline for the research software metadata is based on the FAIR principles for research software, which focuses on improving the findability, accessibility, interoperability, and reusability of research software and its metadata. The modularized TO framework separates the analysis, which solves the state equations and does the sensitivity analysis, and the design modification, which represents and modifies the design. Mapping is then used to interface between the two. We demonstrate the interoperability and reusability of this framework through numerical examples.
TI - Avoiding reinventing the wheel: reusable open-source topology optimization software
JF - Structural and Multidisciplinary Optimization
DO - 10.1007/s00158-023-03589-7
DA - 2023-06-01
UR - https://www.deepdyve.com/lp/springer-journals/avoiding-reinventing-the-wheel-reusable-open-source-topology-e7h2EJmUDX
VL - 66
IS - 6
DP - DeepDyve
ER -