A computational algorithm for random particle breakage
Random breakage can be defined as the breakage patterns independent from the stressing environment and the nature of the broken particle. However, the relevant literature studies give contrary evidence against random breakage of particles. A simple way to detect random breakage is to evaluate the fr...
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| Published in: | Physica A Vol. 602; p. 127640 |
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| Language: | English |
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Elsevier B.V
15.09.2022
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| ISSN: | 0378-4371, 1873-2119 |
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| Abstract | Random breakage can be defined as the breakage patterns independent from the stressing environment and the nature of the broken particle. However, the relevant literature studies give contrary evidence against random breakage of particles. A simple way to detect random breakage is to evaluate the fragment (progeny) size distributions. Such distributions are estimated analytically or through numerical models. The latter models generally treat random breakage as a geometric statistical problem with prior assumptions on particle/flaw geometry and external stressing environment, which may violate the randomness of the breakage process. This study presents a random-breakage algorithm that does not require such assumptions. The simulated progeny size distributions were compared with the experimental size distributions by impact loading (drop-weight) tests. Random breakage events should yield number-weighted size distributions that is fitted well to the lognormal distribution function. Also, a mass-weighted (sieve) size distribution function is presented for random breakage. Nevertheless, the results refute the random breakage of clinker and other brittle particles after impact loading. Instead, the sieve size distribution of fragments may evolve due to crack branching/merging and Poissonian crack nucleation processes.
•An algorithm is presented to simulate random breakage without any assumption.•Simulation results refute the random breakage of brittle particles.•The results are consistent with Kolmogorov’s proof for lognormal distribution.•A function is derived for sieve size dist. of progenies after random breakage.•Progeny size dist. may be due to crack branching/merging and Poissonian processes. |
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| AbstractList | Random breakage can be defined as the breakage patterns independent from the stressing environment and the nature of the broken particle. However, the relevant literature studies give contrary evidence against random breakage of particles. A simple way to detect random breakage is to evaluate the fragment (progeny) size distributions. Such distributions are estimated analytically or through numerical models. The latter models generally treat random breakage as a geometric statistical problem with prior assumptions on particle/flaw geometry and external stressing environment, which may violate the randomness of the breakage process. This study presents a random-breakage algorithm that does not require such assumptions. The simulated progeny size distributions were compared with the experimental size distributions by impact loading (drop-weight) tests. Random breakage events should yield number-weighted size distributions that is fitted well to the lognormal distribution function. Also, a mass-weighted (sieve) size distribution function is presented for random breakage. Nevertheless, the results refute the random breakage of clinker and other brittle particles after impact loading. Instead, the sieve size distribution of fragments may evolve due to crack branching/merging and Poissonian crack nucleation processes.
•An algorithm is presented to simulate random breakage without any assumption.•Simulation results refute the random breakage of brittle particles.•The results are consistent with Kolmogorov’s proof for lognormal distribution.•A function is derived for sieve size dist. of progenies after random breakage.•Progeny size dist. may be due to crack branching/merging and Poissonian processes. |
| ArticleNumber | 127640 |
| Author | Camalan, Mahmut |
| Author_xml | – sequence: 1 givenname: Mahmut orcidid: 0000-0001-7071-7910 surname: Camalan fullname: Camalan, Mahmut email: camalanmahmut@gmail.com organization: Middle East Technical University, Ankara, Turkey |
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| Cites_doi | 10.1145/272991.272995 10.1063/1.336139 10.6028/jres.073B.001 10.1103/PhysRevE.77.051302 10.1063/1.360073 10.1088/0004-637X/738/1/86 10.1063/1.347188 10.1016/0016-0032(47)90465-1 10.1016/j.powtec.2004.04.004 10.1103/PhysRevE.70.026104 10.1007/s10704-008-9267-6 10.1038/srep09147 10.1016/S0016-0032(36)90309-5 10.1080/01621459.1951.10500769 10.1016/j.mineng.2016.05.005 10.1016/j.minpro.2007.12.001 10.1080/15427951.2004.10129088 10.1016/j.ijmst.2020.03.017 10.1214/aoms/1177731283 10.1016/S0032-5910(01)00276-5 10.7566/JPSJ.83.124005 10.1007/BF01028965 10.1016/j.minpro.2004.01.006 10.1016/j.physa.2006.04.087 10.1016/S0892-6875(98)00102-2 10.1016/S0032-5910(02)00237-1 10.1140/epjst/e2014-02270-3 10.1016/0378-4371(95)00063-D 10.1016/j.wear.2004.09.012 |
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