Mengoni, M orcid.org/0000-0003-0986-2769 (2020) Using inverse Finite Element analysis to identify spinal tissue behaviour in situ. Methods. ISSN 1046-2023
Abstract
In computational modelling of musculoskeletal applications, one of the critical aspects is ensuring that a model can capture intrinsic population variability and not only representative of a “mean” individual. Developing and calibrating models with this aspect in mind is key for the credibility of a modelling methodology. This often requires calibration of complex models with respect to 3D experiments and measurements on a range of specimens or patients. Most Finite Element (FE) software’s do not have such a capacity embedded in their core tools.
This paper presents a versatile interface between Finite Element (FE) software and optimisation tools, enabling calibration of a group of FE models on a range of experimental data. It is provided as a Python toolbox which has been fully tested and verified on Windows platforms. The toolbox is tested in three case studies involving in vitro testing of spinal tissues.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author. Published by Elsevier Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Optimisation; In silico models; Data variability |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/F010575/1 EPSRC (Engineering and Physical Sciences Research Council) EP/K020757/1 Wellcome Trust 088908/Z/09/Z |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Feb 2020 15:18 |
Last Modified: | 13 Dec 2024 10:01 |
Status: | Published online |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ymeth.2020.02.004 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156554 |