Gaussian curvature analysis allows for automatic block placement in multi-block hexahedral meshing

被引:3
|
作者
Ramme, Austin J. [1 ,2 ,3 ]
Shivanna, Kiran H. [1 ,3 ]
Magnotta, Vincent A. [1 ,3 ,4 ]
Grosland, Nicole M. [1 ,3 ,5 ]
机构
[1] Univ Iowa, Dept Biomed Engn, Iowa City, IA 52242 USA
[2] Univ Iowa, Carver Coll Med, Iowa City, IA 52242 USA
[3] Univ Iowa, Ctr Comp Aided Design, Iowa City, IA 52242 USA
[4] Univ Iowa, Dept Radiol, Iowa City, IA 52242 USA
[5] Univ Iowa, Dept Orthopaed & Rehabil, Iowa City, IA 52242 USA
关键词
finite element; hexahedral meshing; multi-block; Gaussian curvature; orthopaedic modelling; FINITE-ELEMENT MODEL; BONE SEGMENTATION; CT IMAGES; VALIDATION;
D O I
10.1080/10255842.2010.499869
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Musculoskeletal finite element analysis (FEA) has been essential to research in orthopaedic biomechanics. The generation of a volumetric mesh is often the most challenging step in a FEA. Hexahedral meshing tools that are based on a multi-block approach rely on the manual placement of building blocks for their mesh generation scheme. We hypothesise that Gaussian curvature analysis could be used to automatically develop a building block structure for multi-block hexahedral mesh generation. The Automated Building Block Algorithm incorporates principles from differential geometry, combinatorics, statistical analysis and computer science to automatically generate a building block structure to represent a given surface without prior information. We have applied this algorithm to 29 bones of varying geometries and successfully generated a usable mesh in all cases. This work represents a significant advancement in automating the definition of building blocks.
引用
收藏
页码:893 / 904
页数:12
相关论文
共 50 条
  • [31] Multi-Block Models for Multivariate Neuropsychiatric Data
    Thompson, Wesley
    Paulus, Martin
    BIOLOGICAL PSYCHIATRY, 2018, 83 (09) : S87 - S88
  • [32] FREE DYNAMICS OF MULTI-BLOCK ROCKING ASSEMBLIES
    Wiebe, R.
    Li, T.
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2017, VOL 6, 2017,
  • [33] Selective linearization for multi-block statistical learning
    Du, Yu
    Lin, Xiaodong
    Pham, Minh
    Ruszczynski, Andrzej
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2021, 293 (01) : 219 - 228
  • [34] Application of multi-block methods in cement production
    Svinning, Ketil
    Hoskuldsson, Agnar
    JOURNAL OF CHEMOMETRICS, 2008, 22 (11-12) : 587 - 593
  • [35] New multi-block mesh generation method
    Tan, Xinxing
    Xi, Guang
    Ying Yong Li Xue Xue Bao/Chinese Journal of Applied Mechanics, 2000, 17 (02): : 94 - 99
  • [36] POLYSTYRENE-POLYDIMETHYLSILOXANE MULTI-BLOCK COPOLYMERS
    SAAM, JC
    WARD, AH
    FEARON, FWG
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 1972, : 61 - &
  • [37] Indoor localization system in a multi-block workspace
    Park, JaeHyun
    Choi, MunGyu
    Zu, YunFei
    Lee, JangMyung
    ROBOTICA, 2010, 28 : 397 - 403
  • [38] Optimal multi-block mesh generation for CFD
    Ali, Zaib
    Dhanasekaran, P. Caleb
    Tucker, Paul G.
    Watson, Rob
    Shahpar, Shahrokh
    INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS, 2017, 31 (4-5) : 195 - 213
  • [39] Development of high-quality hexahedral human brain meshes using feature-based multi-block approach
    Mao, Haojie
    Gao, Haitao
    Cao, Libo
    Genthikatti, Vinay Veeranna
    Yang, King H.
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, 2013, 16 (03) : 271 - 279
  • [40] Controlling the block sequence of multi-block oligomer ligands for neutralization of a target peptide
    Takimoto, Hinata
    Katakami, Sho
    Miura, Yoshiko
    Hoshino, Yu
    MATERIALS ADVANCES, 2020, 1 (04): : 604 - 608