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Jones, L. D., Vandeperre, L.J., Haynes, T.A. 
ORCID: https://orcid.org/0000-0003-0058-2939 and Wenman, M.R.
(2020)
Modelling of Weibull distributions in brittle solids using 2-dimensional peridynamics.
In:
Procedia Structural Integrity.
Procedia Structural Integrity, 28
.
Elsevier, pp. 1856-1874.
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Abstract
Peridynamics is a continuum mechanics modelling method, which offers advantages over traditional continuum methods when modelling brittle fracture. Brittle fracture typically follows a Weibull fracture distribution, but this behaviour is not well represented in bond-based peridynamics using a single valued bond failure stretch. In order to recreate specific Weibull-type behaviour in bond-based peridynamics, consideration must be given to scaling the distribution to account for the size of peridynamics bonds. Care must also be taken to avoid (wherever possible) non-physical crack arrest, caused by the variations in fracture toughness in the model, distorting the distributions. In this work a method for recreating a variety of Weibull distributions is outlined, based on applying Weibull-type bond behaviour only to surface bonds, including a transition zone across one horizon. The method is shown to be insensitive to variations in mesh refinement.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Funding Information: Lloyd Jones and Mark Wenman acknowledge funding from the National Nuclear Laboratory. Thomas Haynes acknowledges funding from the Engineering and Physical Sciences Research Council MIDAS programme, Grant Number EP/S01702X/1. Lloyd Jones, Mark Wenman and Luc Vandeperre acknowledge funding from the Engineering and Physical Sciences Research Council Centre for Doctoral Training in Nuclear Energy, Grant Number EP/L015900/1. |
| Uncontrolled Keywords: | brittle fracture,non-local modelling,peridynamics,weibull,mechanical engineering,mechanics of materials,civil and structural engineering,materials science(all) ,/dk/atira/pure/subjectarea/asjc/2200/2210 |
| Faculty \ School: | Faculty of Science > School of Engineering (former - to 2024) |
| UEA Research Groups: | Faculty of Science > Research Groups > Sustainable Energy Faculty of Science > Research Groups > Numerical Simulation, Statistics & Data Science |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 10 Nov 2021 08:26 |
| Last Modified: | 07 Nov 2024 12:49 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/82036 |
| DOI: | 10.1016/j.prostr.2020.11.009 |
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