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Simple and efficient volume merging method for triply periodic minimal structures?

Authors
Li, YibaoXia, QingYoon, SunghaLee, ChaeyoungLu, BinghengKim, Junseok
Issue Date
7월-2021
Publisher
ELSEVIER
Keywords
Tissue engineering scaffolds; Volume merging method; Triply periodic minimal surfaces; Allen-Cahn equation; Implicit method
Citation
COMPUTER PHYSICS COMMUNICATIONS, v.264
Indexed
SCIE
SCOPUS
Journal Title
COMPUTER PHYSICS COMMUNICATIONS
Volume
264
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137257
DOI
10.1016/j.cpc.2021.107956
ISSN
0010-4655
Abstract
Triply periodic minimal surfaces (TPMSs), which are periodic in all three directions and are surfaces of zero mean curvature, have been proven experimentally to be highly suitable for tissue scaffolds. However, simply gluing different TPMS units with different porosities and pore sizes could induce discontinuous structures and destroy the physical properties. In this study, we propose a simple and efficient volume merging method for triply periodic minimal structures. The proposed method can be divided into two steps. The first step is a novel merging algorithm for unit triply periodic minimal structures in the implicit function framework. The composite scaffold can be designed by merging different unit structures to satisfy the properties of internal connectivity. The second step is to optimize the designed composite scaffolds to satisfy the properties of TPMSs. A modified Allen- Cahn-type equation with a correction term is proposed. The mean curvature on the surface is constant at all points in the equilibrium state. Typically, the obtained structure is smooth owing to the motion by mean curvature flow. Therefore, the quality of the structure is significantly improved. Based on the operator splitting method, the proposed algorithm consists of two analytical evaluations for the ordinary differential equations and one numerical solution for the implicit Poisson-type equation. The proposed numerical scheme can be applied in a straightforward manner to a GPU-accelerated discrete cosine transform (DCT) implementation, which can be executed multiple times faster than CPU-only alternatives. Computational experiments are presented to demonstrate the efficiency and robustness of the proposed method. (c) 2021 Elsevier B.V. All rights reserved. <comment>Superscript/Subscript Available</comment
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