Image based simulation of one-dimensional compression tests on carbonate sand

High factors of safety and conservative methods are commonly used on foundation design on shelly carbonate soils. A better understanding of the behavior of this material is, thus, critical for more sustainable approaches for the design of a number of offshore structures and submarine pipelines. In particular, understanding the physical phenomena taking place at the microscale has the potential to spur the development of robust computational methods. In this study, a one-dimensional compression test was performed inside an X-ray scanner to obtain 3D images of the evolving internal structure of a shelly carbonate sand. A preliminary inspection of the images through five loading increments has shown that the grains rearrange under loading and in some cases cracks develop at the contacts. In order to replicate of the experiments in the numerical domain, the 3D image of the soil prior to loading was imported into a micro Finite Element (µFE) framework. This image-based modelling tool enables measurements of the contact force and stress map inside the grains while making use of the real microstructure of the soil. The potential of the µFE model to contribute insights into yield initiation within the grain is demonstrated here. This is of particular interest to better understand the breakage of shelly grains underpinning their highly compressive behavior

A laboratory-based technique for grain size and shape characterisation

The significance of grain shape dependent behaviour is widely reported in the literature. Quantification of grain shape is, however, not part of routine laboratory characterisation and this can be in part attributed to the lack to accessible equipment. In this paper, we discuss the potential of a novel imaging technique to capture the three-dimensional outline of grains. This technique enables the volumetric description of the grain to be obtained by reconstructing the planar projections of the grain acquired at different angles of rotation using a standard camera. The imaging setup is very simple and can be easily implemented in any laboratory. It includes a camera, a lens (optional) and a stepper motor to rotate the object in controllable and precise increments. The greater the number of acquired projections, the better the detail of the reconstructed volume will be. Results from single grain compression tests on Leighton Buzzard sand and a shelly carbonate sand from the Persian Gulf are presented to demonstrate the dependency of the tensile strength on the grain shape. The simplicity and easy access to this laboratory-based technique have the potential to enhance laboratory and physical experiments of geomaterials

A micro finite element model for soil behaviour: Numerical validation

A micro finite-element (μFE) model capable of handling arbitrary shapes and deformable grains has been developed by the authors. The basis of this μFE model is to use a virtualised soil fabric obtained from micro computed tomography (μCT) of real sand to simulate grain-to-grain interaction in a framework of combined discrete–finite-element method. By incorporating grain deformation into the model, the contact response emerges from the interaction of contacting bodies and each irregular contact area will produce a unique response. A detailed numerical description of grain morphology and contact topology of a natural sand and the subsequent simulation are presented in the original paper. The present study focuses on the numerical validation of the constitutive contact behaviour against existent theories, for a single sphere and an assembly of spheres. The ability of the model to simulate elastic–plastic behaviour making use of the deformability of the grains is demonstrated. The unloading–reloading behaviour associated with the geometrical arrangement of the grains for a granular assembly under triaxial compression is examined in terms of energy dissipation quantities

A micro finite-element model for soil behaviour

This paper describes a numerical model that virtualises the fabric of a natural sand obtained from micro computed tomography (μCT) to simulate the mechanical response of the material, termed here a micro finite-element (μFE) model. The grain-to-grain interactions under loading are modelled in a framework of combined discrete–finite-element method. The basis of this approach is that using a true representation of soil fabric and deformable grains will enable a more realistic representation of the physics of granular behaviour. Each individual grain is represented in a numerical mesh and modelled as a continuum body allowed to deform according to a prescribed constitutive model with appropriate friction contact conditions. An important feature of this model is the ability to compute the map of stress distribution inside the grains. A case study of an intact sand subjected to oedometer compression is presented to demonstrate the insights that can be gained into the stress transmission mechanisms and yield initiation within the grains. The displacement field, inertia tensor and active contact number are used to quantify grain kinematics as the virtual fabric deforms. By coupling contact dynamics with contact topology, this approach provides a robust numerical tool to infer important grain scale parameters that link the micro phenomena to the macro response of soil

A Large-scale Distributed Video Parsing and Evaluation Platform

Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem (HDFS). Also, a Web User Interface is designed in the system, to collect users' degrees of satisfaction on the recognition tasks so as to evaluate the performance of the whole system. Furthermore, the highly extensible platform running on the long-term surveillance videos makes it possible to develop more intelligent incremental algorithms to enhance the performance of various visual recognition tasks.Comment: Accepted by Chinese Conference on Intelligent Visual Surveillance 201

Single-Grain Virtualization for Contact Behavior Analysis on Sand

A methodology for virtualizing irregularly shaped grains is described here. The principle, largely inspired by computed tomography, is simple and accessible because only the three-dimensional (3D) outline of the grain is required. The volumetric object is obtained by reconstructing the planar projections of the grain acquired at different angles of rotation using a standard camera. Depending on the lens system, the resolution of the images can be as good as a few microns. A numerical representation of the real grain can be obtained by meshing the 3D image. The influence of grain morphology on the contact behavior of quartz sand is investigated here as an application of this novel technique. Numerical simulations using a finite-element model were carried out to reproduce the experimental data from normal compression single-grain tests. The results show the contribution of the initial grain rearrangement on the normal force-displacement response and its strong dependency on the shape of the grain. This study demonstrates that particle shape is a critical parameter for calibration of the contact behavior of sand

CFD-DEM modelling of particle entrainment in wheel–rail interface: a parametric study on train characteristics

Rail-sanding is employed to improve the train’s wheel–rail traction loss in low adhesion conditions. This can significantly impede trains’ kinematics, operation, and performance by hindering the train’s acceleration and deceleration, resulting in delays and unreliability of transport system as well as causing safety risks and in the worst cases train collisions. Rail-sanding has its own merits in recovering the wheel–rail traction but can result in a sand wastage of more than 80% due to its low sand entrainment efficiency. In this research, computational fluid dynamics is coupled to discrete element modelling to study the behaviour of sand particles during rail-sanding. A parametric study based on the train characteristics, including train velocity, sand flow rate, and the geometry of the sander nozzle, is performed by comparing the entrainment efficiency of the sand particles. It is found that train velocities over 30 m/s result in the entrainment efficiency of almost zero. A moving air layer generated at the wheel–rail interface influences the lower bound of acceptable particle size range. The flow rate and nozzle geometry can be designed to enhance entrainment efficiency. https://doi.org/10.1007/s00707-024-04032-