Stochastic generation of virtual air pores in granular materials

Chiarelli, Andrea; Dawson, Andrew; Garcia, Alvaro

Abstract

A computational method is described for the generation of virtual air pores with randomized features in granular materials. The method is based on the creation of a stack of two dimensional stochastically generated domains of packed virtual aggregate particles that are converted to three dimensions and made to intersect with one another. The three dimensional structure that is created is then sampled with an algorithm that detects the void space left between the intersected particles, which corresponds to the air void volume in real materials. This allows the generation of a map of the previously generated three dimensional model that can be used to analyse the topology of the void channels. The isotropy of the samples is here discussed and analysed. The air void size distribution in all the virtual samples generated in this study is described with the Weibull distribution and the goodness of fit is successfully evaluated with the Kolmogorov–Smirnov test. The specific surface of the virtual samples is also successfully compared to that of real samples. The results show that a stochastic approach to the generation of virtual granular materials based only on geometric principles is feasible and provides realistic results.

Citation

Chiarelli, A., Dawson, A., & Garcia, A. (2015). Stochastic generation of virtual air pores in granular materials. Granular Matter, 17(5), https://doi.org/10.1007/s10035-015-0585-x

• Journal Article Type: Article
• Acceptance Date: Aug 16, 2015
• Online Publication Date: Aug 30, 2015
• Publication Date: Oct 3, 2015
• Deposit Date: Jul 8, 2016
• Publicly Available Date: Jul 8, 2016
• Journal: Granular Matter
• Print ISSN: 1434-5021
• Electronic ISSN: 1434-5021
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 5
• DOI: https://doi.org/10.1007/s10035-015-0585-x
• Keywords: Granular material ; Air void content ; Packing ; Porosity ; Asphalt
• Public URL: https://nottingham-repository.worktribe.com/output/764659
• Publisher URL: http://link.springer.com/article/10.1007%2Fs10035-015-0585-x