Iterated Boolean random varieties and application to fracture statistics models

Dominique Jeulin

Applications of Mathematics (2016)

  • Volume: 61, Issue: 4, page 363-386
  • ISSN: 0862-7940

Abstract

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Models of random sets and of point processes are introduced to simulate some specific clustering of points, namely on random lines in 2 and 3 and on random planes in 3 . The corresponding point processes are special cases of Cox processes. The generating distribution function of the probability distribution of the number of points in a convex set K and the Choquet capacity T ( K ) are given. A possible application is to model point defects in materials with some degree of alignment. Theoretical results on the probability of fracture of convex specimens in the framework of the weakest link assumption are derived, and used to compare geometrical effects on the sensitivity of materials to fracture.

How to cite

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Jeulin, Dominique. "Iterated Boolean random varieties and application to fracture statistics models." Applications of Mathematics 61.4 (2016): 363-386. <http://eudml.org/doc/283400>.

@article{Jeulin2016,
abstract = {Models of random sets and of point processes are introduced to simulate some specific clustering of points, namely on random lines in $ \mathbb \{R\}^\{2\}$ and $\mathbb \{R\} ^\{3\}$ and on random planes in $ \mathbb \{R\}^\{3\}$. The corresponding point processes are special cases of Cox processes. The generating distribution function of the probability distribution of the number of points in a convex set $K$ and the Choquet capacity $T(K)$ are given. A possible application is to model point defects in materials with some degree of alignment. Theoretical results on the probability of fracture of convex specimens in the framework of the weakest link assumption are derived, and used to compare geometrical effects on the sensitivity of materials to fracture.},
author = {Jeulin, Dominique},
journal = {Applications of Mathematics},
keywords = {Boolean model; Boolean varieties; Cox process; weakest link model; fracture statistics; mathematical morphology; Boolean model; Boolean varieties; Cox process; weakest link model; fracture statistics; mathematical morphology},
language = {eng},
number = {4},
pages = {363-386},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {Iterated Boolean random varieties and application to fracture statistics models},
url = {http://eudml.org/doc/283400},
volume = {61},
year = {2016},
}

TY - JOUR
AU - Jeulin, Dominique
TI - Iterated Boolean random varieties and application to fracture statistics models
JO - Applications of Mathematics
PY - 2016
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 61
IS - 4
SP - 363
EP - 386
AB - Models of random sets and of point processes are introduced to simulate some specific clustering of points, namely on random lines in $ \mathbb {R}^{2}$ and $\mathbb {R} ^{3}$ and on random planes in $ \mathbb {R}^{3}$. The corresponding point processes are special cases of Cox processes. The generating distribution function of the probability distribution of the number of points in a convex set $K$ and the Choquet capacity $T(K)$ are given. A possible application is to model point defects in materials with some degree of alignment. Theoretical results on the probability of fracture of convex specimens in the framework of the weakest link assumption are derived, and used to compare geometrical effects on the sensitivity of materials to fracture.
LA - eng
KW - Boolean model; Boolean varieties; Cox process; weakest link model; fracture statistics; mathematical morphology; Boolean model; Boolean varieties; Cox process; weakest link model; fracture statistics; mathematical morphology
UR - http://eudml.org/doc/283400
ER -

References

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  1. Delisée, Ch., Jeulin, D., Michaud, F., Caractérisation morphologique et porosité en 3D de matériaux fibreux cellulosiques, C.R. Académie des Sciences de Paris, t. 329, Série II b French (2001), 179-185. (2001) 
  2. Dirrenberger, J., Forest, S., Jeulin, D., 10.1016/j.ijsolstr.2013.10.011, Int. J. Solids Struct. 51 (2014), 359-376. (2014) DOI10.1016/j.ijsolstr.2013.10.011
  3. Faessel, M., Jeulin, D., 3D multiscale vectorial simulations of random models, Proceedings of ICS13 (2011), 19-22. (2011) 
  4. Jeulin, D., Modèles Morphologiques de Structures Aléatoires et de Changement d'Echelle, Thèse de Doctorat d'Etat è s Sciences Physiques, Université de Caen (1991). (1991) 
  5. Jeulin, D., Modèles de Fonctions Aléatoires multivariables, Sci. Terre French 30 (1991), 225-256. (1991) 
  6. Jeulin, D., Random structure models for composite media and fracture statistics, Advances in Mathematical Modelling of Composite Materials (1994), 239-289. (1994) 
  7. Jeulin, D., 10.1007/978-3-7091-2780-3_2, Mechanics of Random and Multiscale Microstructures D. Jeulin, M. Ostoja-Starzewski CISM Courses Lect. 430, Springer, Wien (2001), 33-91. (2001) Zbl1010.74004DOI10.1007/978-3-7091-2780-3_2
  8. Jeulin, D., 10.1016/j.crme.2012.02.004, A review, C. R. Mecanique 340 (2012), 219-229. (2012) DOI10.1016/j.crme.2012.02.004
  9. Jeulin, D., Boolean random functions, Stochastic Geometry, Spatial Statistics and Random Fields. Models and Algorithms V. Schmidt Lecture Notes in Mathematics 2120, Springer, Cham (2015), 143-169. (2015) Zbl1366.60013MR3330575
  10. Jeulin, D., 10.1007/s11009-015-9464-5, Methodol. Comput. Appl. Probab. (2015), 1-15, DOI: 10.1007/s11009-015-9464-5. (2015) MR3564853DOI10.1007/s11009-015-9464-5
  11. Maier, R., Schmidt, V., 10.1017/S000186780001226X, Adv. Appl. Probab. 35 (2003), 337-353. (2003) Zbl1041.60012MR1970476DOI10.1017/S000186780001226X
  12. Matheron, G., Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics John Wiley & Sons, New York (1975). (1975) Zbl0321.60009MR0385969
  13. Nagel, W., Weiss, V., 10.1017/S0001867800012118, Adv. Appl. Probab. 35 (2003), 123-138. (2003) Zbl1023.60015MR1975507DOI10.1017/S0001867800012118
  14. Schladitz, K., Peters, S., Reinel-Bitzer, D., Wiegmann, A., Ohser, J., 10.1016/j.commatsci.2006.01.018, Computational Materials Science 38 (2006), 56-66. (2006) DOI10.1016/j.commatsci.2006.01.018

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