The Influence of the Degree of Heterogeneity on the Elastic Properties of Random Sphere Packings

The macroscopic mechanical properties of colloidal particle gels strongly depend on the local arrangement of the powder particles. Experiments have shown that more heterogeneous microstructures exhibit up to one order of magnitude higher elastic properties than their more homogeneous counterparts at equal volume fraction. In this paper, packings of spherical particles are used as model structures to computationally investigate the elastic properties of coagulated particle gels as a function of their degree of heterogeneity. The discrete element model comprises a linear elastic contact law, particle bonding and damping. The simulation parameters were calibrated using a homogeneous and a heterogeneous microstructure originating from earlier Brownian dynamics simulations. A systematic study of the elastic properties as a function of the degree of heterogeneity was performed using two sets of microstructures obtained from Brownian dynamics simulation and from the void expansion method. Both sets cover a broad and to a large extent overlapping range of degrees of heterogeneity. The simulations have shown that the elastic properties as a function of the degree of heterogeneity are independent of the structure generation algorithm and that the relation between the shear modulus and the degree of heterogeneity can be well described by a power law. This suggests the presence of a critical degree of heterogeneity and, therefore, a phase transition between a phase with finite and one with zero elastic properties.Comment: 8 pages, 6 figures; Granular Matter (published online: 11. February 2012

Corneal Biomechanical Properties in Varying Severities of Myopia

Purpose: To investigate corneal biomechanical response parameters in varying degrees of myopia and their correlation with corneal geometrical parameters and axial length. Methods: In this prospective cross-sectional study, 172 eyes of 172 subjects, the severity degree of myopia was categorized into mild, moderate, severe, and extreme myopia. Cycloplegic refraction, corneal tomography using Pentacam HR, corneal biomechanical assessment using Corvis ST and Ocular Response Analyser (ORA), and ocular biometry using IOLMaster 700 were performed for all subjects. A general linear model was used to compare biomechanical parameters in various degrees of myopia, while central corneal thickness (CCT) and biomechanically corrected intraocular pressure (bIOP) were considered as covariates. Multiple linear regression was used to investigate the relationship between corneal biomechanical parameters with spherical equivalent (SE), axial length (AXL), bIOP, mean keratometry (Mean KR), and CCT. Results: Corneal biomechanical parameters assessed by Corvis ST that showed significant differences among the groups were second applanation length (AL2, p = 0.035), highest concavity radius (HCR, p < 0.001), deformation amplitude (DA, p < 0.001), peak distance (PD, p = 0.022), integrated inverse radius (IR, p < 0.001) and DA ratio (DAR, p = 0.004), while there were no significant differences in the means of pressure-derived parameters of ORA between groups. Multiple regression analysis showed all parameters of Corvis ST have significant relationships with level of myopia (SE, AXL, Mean KR), except AL1 and AL2. Significant biomechanical parameters showed progressive reduction in corneal stiffness with increasing myopia (either with greater negative SE or greater AXL), independent of IOP and CCT. Also, corneal hysteresis (CH) or ability to dissipate energy from the ORA decreased with increasing level of myopia. Conclusions: Dynamic corneal response assessed by Corvis ST shows evidence of biomechanical changes consistent with decreasing stiffness with increasing levels of myopia in multiple parameters. The strongest correlations were with highest concavity parameters where the sclera influence is maximal. © Copyright © 2021 Sedaghat, Momeni-Moghaddam, Azimi, Fakhimi, Ziaei, Danesh, Roberts, Monfared and Jamali

Sensing, measuring and modelling the mechanical properties of sandstone

We present a hybrid framework for simulating the strength and dilation characteristics of sandstone. Where possible, the grain-scale properties of sandstone are evaluated experimentally in detail. Also, using photo-stress analysis, we sense the deviator stress (/strain) distribution at the microscale and its components along the orthogonal directions on the surface of a V-notch sandstone sample under mechanical loading. Based on this measurement and applying a grain-scale model, the optical anisotropy index K0 is inferred at the grain scale. This correlated well with the grain contact stiffness ratio K evaluated using ultrasound sensors independently. Thereafter, in addition to other experimentally characterised structural and grain-scale properties of sandstone, K is fed as an input into the discrete element modelling of fracture strength and dilation of the sandstone samples. Physical bulk scale experiments are also conducted to evaluate the load-displacement relation, dilation and bulk fracture strength characteristics of sandstone samples under compression and shear. A good level of agreement is obtained between the results of the simulations and experiments. The current generic framework could be applied to understand the internal and bulk mechanical properties of such complex opaque and heterogeneous materials more realistically in future

Coupled effect of loading rate and notch length on tensile strength of rock

Rock masses naturally contain joints and fractures and the effect of these fractures needs to be carefully investigated to ensure the stability of rock structures. This is particularly the case when dynamic loading effects due to earthquakes or rock blasting are involved. In this study, Brazilian synthetic specimens, made of gypsum with initial notches, were loaded in the mode-I fracture. The specimens were 50 mm in diameter and 10 mm in thickness. The pre-existing notch length in the specimens varied from 10 to 40 mm. The nominal tensile strength of the specimens was numerically evaluated using a bonded particle model (BPM) for the synthetic rock material. The dynamic tests were performed using the Split Hopkinson Pressure Bar (SHPB) system which was numerically simulated by the CA3 computer program. CA3 is a computer program for static and dynamic simulation of geomaterials in which a hybrid bonded particle and finite element system can be employed. The rock specimen was represented by the bonded particle model, while the incident and transmission bars in the Hopkinson Pressure Bar system were simulated by the finite element model. The bonded particle system was calibrated to ensure that the elastic properties, uniaxial compressive strength, tensile strength, and fracture toughness of the rock were replicated by the numerical model. The combined effect of loading rate and initial fracture length on the rock tensile strength was investigated. The results, as expected, suggest that the static nominal tensile strength of the specimens was reduced as the notch length increased. Under dynamic loading, the material response is more complicated; depending on the applied stress rate, the tensile strength can decrease, remain constant, or increase as the initial notch length increases. It is shown that the speed of the crack tip opening is responsible for this interesting observation of tensile strength changes under dynamic loading as the notch length varies

PERSONALIZED LEARNING IN EDUCATION: A MACHINE LEARNING AND SIMULATION APPROACH

International audiencePurpose: The study aims to explore the untapped potential of personalized learning in blended learning environments. It focuses on addressing the challenges hindering technology adoption in education, such as data privacy concerns, personalization issues, and educators' prior experience with technology. The primary objective is to enhance student performance through a personalized learning approach. Design/Methodology/Approach: This research proposes an integrated framework combining Machine Learning and simulations for a personalized learning system. The framework's goal i aims to improve the relevance and integrity of educational tasks, tailor them to individual student needs, and thereby enhance overall academic performance. The approach involves utilizing simulations to power ML algorithms capable of classifying and predicting student performance. Findings: The findings from this study provide preliminary evidence supporting the effectiveness of the ML and simulation-based personalized learning approach. The results indicate that this system can significantly improve learning outcomes, suggesting its ability to improve educational settings. Originality/Value: The research introduces a novel approach by integrating Machine Learning with simulations to enhance personalized learning. This study offers a significant contribution by addressing the technological barriers in educational settings and providing a theoretical solution to improve student performance. The originality of the adopted approach lies in its potential to offer more personalized and effective educational experiences, advancing the fields of educational technology and analytics