A grain based modeling study of mineralogical factors affecting strength, elastic behavior and micro fracture development during compression tests in granites

Hofmann, Hannes; Babadagli, Tayfun; Yoon, Jeoung Seok; Zang, Arno; Zimmermann, Günter

doi:10.1016/j.engfracmech.2015.09.008

Cited by 139 publications

(37 citation statements)

References 31 publications

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“…Table 2 shows the good agreement between strengths and elastic parameters of the laboratory tests and the numerical model. Photographs of Aue granite samples and the GBM model representation are shown in Hofmann et al [16]. Note that the physical model size was 10 Â 5 cm and the numerical model size is only 5 Â 2.5 cm to reduce computational time while no significant difference in the model results were observed [16].…”

Section: Calibrated Pfc2d Grain Based Model Of Aue Granitementioning

confidence: 97%

“…Photographs of Aue granite samples and the GBM model representation are shown in Hofmann et al [16]. Note that the physical model size was 10 Â 5 cm and the numerical model size is only 5 Â 2.5 cm to reduce computational time while no significant difference in the model results were observed [16]. The mineral content and sizes of Aue granite are as follows: 30% quartz (d ¼1.45 7 0.35 mm), 40% plagioclase (d ¼ 1.35 7 0.45 mm), 20% orthoclase (d ¼ 1.35 7 0.45 mm) and 10% mica (d ¼ 1.20 7 0.30 mm) [20][21][22].…”

Section: Calibrated Pfc2d Grain Based Model Of Aue Granitementioning

confidence: 99%

“…First, the results of the sensitivity analysis in the previous study [16] were re-investigated to evaluate the influence of mineral size and distribution of minerals and particles on the resulting fracture patterns. The mineral micro properties (parallel-bond and particle properties) and the mineral boundary micro properties (smooth joint properties) of the calibrated model, which are given in Table 2, were then varied separately between 750% of the values of the calibrated Aue granite model to investigate their sensitivity on the fracture patterns.…”

Section: Modeling Proceduresmentioning

confidence: 99%

“…More details such as the calibration procedure can be found in Hofmann et al [16]. Details about the discrete element Particle Flow Code 2D (PFC2D) are given elsewhere [17].…”

Section: Calibrated Pfc2d Grain Based Model Of Aue Granitementioning

confidence: 99%

“…In an earlier study, a GBM for Aue granites was calibrated and investigated the influence of mineral size and random distribution of minerals and particles, which makes up the minerals on elastic properties, strength, and amounts and types of micro cracks during compression tests with 0.1-40 MPa confining pressure [16]. The same calibrated model is used in this study.…”

Section: Introductionmentioning

confidence: 99%

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A grain based modeling study of fracture branching during compression tests in granites

Hofmann

Babadagli

Zimmermann

2015

International Journal of Rock Mechanics and Mining Sciences

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Section: Calibrated Pfc2d Grain Based Model Of Aue Granitementioning

confidence: 97%

Section: Calibrated Pfc2d Grain Based Model Of Aue Granitementioning

confidence: 99%

Section: Modeling Proceduresmentioning

confidence: 99%

“…More details such as the calibration procedure can be found in Hofmann et al [16]. Details about the discrete element Particle Flow Code 2D (PFC2D) are given elsewhere [17].…”

Section: Calibrated Pfc2d Grain Based Model Of Aue Granitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A grain based modeling study of fracture branching during compression tests in granites

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International Journal of Rock Mechanics and Mining Sciences

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Influence of grain size heterogeneity on strength and microcracking behavior of crystalline rocks

2017

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This study numerically investigates the influence of material heterogeneity on the strength and deformation behavior and the associated microcracking process of a felsic crystalline rock using a grain‐based modeling approach in two‐dimensional Particle Flow Code. By using a heterogeneity index defined in this study, the heterogeneity induced by variation of grain size distribution can be explicitly incorporated into the numerical specimen models quantitatively. Under compressive loading, the peak strength and the elastic modulus are found to increase as the numerical model gradually changes from heterogeneous to homogeneous, i.e., a decrease of heterogeneity index. Meanwhile, the number of grain boundary tensile cracks gradually decreases and the number of intragrain cracks increases at the moment of failure. However, the total number of generated microcracks seems not to be significantly influenced by heterogeneity. The orientation of grain boundary microcracks is mainly controlled by the geometry of assembled grain structure of the numerical specimen model, while the orientation of intragrain microcracks is to a large degree influenced by the confinement. In addition, the development of intragrain cracks (both tensile and shear) is much more favored in quartz than in other minerals. Under direct tensile loading, heterogeneity is found to have no significant influence on the simulated stress‐strain responses and rock strength. Only grain boundary tensile cracks are generated when the numerical models are loaded in direct tension, and the position of generated macroscopic fracture developed upon failure of the specimen is largely affected by heterogeneity.

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An Extended Grain‐Based Model for Characterizing Crystalline Materials: An Example of Marble

Wong

Zhang

2018

Advcd Theory and Sims

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An extended grain-based model (EGBM) is proposed with the purpose of explicitly incorporating morphological and metric characteristics of the crystalline structure for realistic simulation of crystalline materials. The objectives of this paper are twofold: (a) to improve the Voronoi grain-based model (VGBM), and (b) to discuss several key challenges associated with model calibration. The EGBM is realized using the software Particle Flow Code (PFC) based on the principle of VGBM. To determine a representative EGBM, a simple calibration procedure taking the microcracking process into account is suggested. Using a crystalline rock as an example, three EGBMs with different rock structures as well as a conventional bonded-particle model (BPM) have been examined to study the effect of geometric heterogeneity on physico-mechanical behavior. The geometric heterogeneity of the three EGBMs is quantified by the geometric deviation index, which measures the standard deviation of fractal dimensions of the constitutive mineral grains.

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A grain based modeling study of mineralogical factors affecting strength, elastic behavior and micro fracture development during compression tests in granites

Cited by 139 publications

References 31 publications

A grain based modeling study of fracture branching during compression tests in granites

A grain based modeling study of fracture branching during compression tests in granites

Influence of grain size heterogeneity on strength and microcracking behavior of crystalline rocks

An Extended Grain‐Based Model for Characterizing Crystalline Materials: An Example of Marble

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