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On the stiffness of surfaces with non-Gaussian height distribution

In this work, the stiffness, i.e., the derivative of the load-separation curve, is studied for self-affine fractal surfaces with non-Gaussian height distribution. In particular, the heights of the surfaces are assumed to follow a Weibull distribution. We find that a linear relation between stiffness...

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Autores principales: Pérez-Ràfols, Francesc, Almqvist, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820461/
https://www.ncbi.nlm.nih.gov/pubmed/33479254
http://dx.doi.org/10.1038/s41598-021-81259-8
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author Pérez-Ràfols, Francesc
Almqvist, Andreas
author_facet Pérez-Ràfols, Francesc
Almqvist, Andreas
author_sort Pérez-Ràfols, Francesc
collection PubMed
description In this work, the stiffness, i.e., the derivative of the load-separation curve, is studied for self-affine fractal surfaces with non-Gaussian height distribution. In particular, the heights of the surfaces are assumed to follow a Weibull distribution. We find that a linear relation between stiffness and load, well established for Gaussian surfaces, is not obtained in this case. Instead, a power law, which can be motivated by dimensionality analysis, is a better descriptor. Also unlike Gaussian surfaces, we find that the stiffness curve is no longer independent of the Hurst exponent in this case. We carefully asses the possible convergence errors to ensure that our conclusions are not affected by them.
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spelling pubmed-78204612021-01-26 On the stiffness of surfaces with non-Gaussian height distribution Pérez-Ràfols, Francesc Almqvist, Andreas Sci Rep Article In this work, the stiffness, i.e., the derivative of the load-separation curve, is studied for self-affine fractal surfaces with non-Gaussian height distribution. In particular, the heights of the surfaces are assumed to follow a Weibull distribution. We find that a linear relation between stiffness and load, well established for Gaussian surfaces, is not obtained in this case. Instead, a power law, which can be motivated by dimensionality analysis, is a better descriptor. Also unlike Gaussian surfaces, we find that the stiffness curve is no longer independent of the Hurst exponent in this case. We carefully asses the possible convergence errors to ensure that our conclusions are not affected by them. Nature Publishing Group UK 2021-01-21 /pmc/articles/PMC7820461/ /pubmed/33479254 http://dx.doi.org/10.1038/s41598-021-81259-8 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Pérez-Ràfols, Francesc
Almqvist, Andreas
On the stiffness of surfaces with non-Gaussian height distribution
title On the stiffness of surfaces with non-Gaussian height distribution
title_full On the stiffness of surfaces with non-Gaussian height distribution
title_fullStr On the stiffness of surfaces with non-Gaussian height distribution
title_full_unstemmed On the stiffness of surfaces with non-Gaussian height distribution
title_short On the stiffness of surfaces with non-Gaussian height distribution
title_sort on the stiffness of surfaces with non-gaussian height distribution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820461/
https://www.ncbi.nlm.nih.gov/pubmed/33479254
http://dx.doi.org/10.1038/s41598-021-81259-8
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