X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT ...technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.
This paper presents a machine learning (ML)‐enabled discrete element method (DEM) for the computational mechanics of irregular‐shaped particles. ML‐enabled DEM, as with most conventional DEMs, ...encompasses four main steps in one typical calculation cycle, namely, (1) the detection and resolution of contacts, (2) the evaluation of contact behavior, (3) the calculation of particle motion, and (4) the updating of particle geometric descriptions. Unlike conventional DEMs, the proposed method constructs and employs neural networks to detect particle contacts and resolve contact geometric features. Neural networks take particle geometric descriptors as inputs and output the contact status and contact geometric features. Using two‐dimensional elliptical particles as an example, the performance of the ML‐enabled DEM is investigated through five numerical experiments and compared with analytical solutions or conventional DEM methods. A sixth numerical experiment involving irregular‐shaped particles is also presented to showcase the potential and applicability of the proposed method for other particle shapes. ML‐enabled DEM can accurately capture the trajectory and energy evolution of individual particles, the fabric characteristics of dense packing, and the mechanical behavior of packing under large loads, while demonstrating computational efficiency over conventional methods.
•Morphologies of coral sand particles are acquired and characterized via X-ray CT.•Random field spherical harmonics approach for generating particles are presented.•SDF-DEM is introduced for modeling ...coral sand with intraparticle voids.•SDF-DEM simulated column collapse results match well with those of laboratory.
Coral sand is the main geomaterial on tropical reefs and its constituent particles are featured with complex shapes and abundant intraparticle voids. This work presents a discrete element method (DEM)-based numerical modeling of coral sand in consideration of both the irregular shape and intraparticle voids of particles. To develop the DEM model for coral sand, the acquisition and characterization of coral sand particles are first introduced. A signed distance field-based DEM applicable to arbitrarily irregular-shaped particles is then presented, in conjunction with two versatile particle models, namely spherical harmonics (SH) and level set (LS). To generate virtual coral sand particles that conform to target morphology characteristics, the combined random field and SH approach and the remedy to elongated particles are also presented. The developed DEM model is calibrated and validated against laboratory column collapse tests, where the agreements between numerical simulations and laboratory experiments demonstrate the good validity and accuracy of DEM model for coral sand. The performance of SH and LS particle models are also compared, providing a useful reference for the selection of appropriate particle models for coral sand in practice.
Granular regolith simulants have been extensively used in the preparation of space missions to test rovers and scientific instruments. In this work, the physical and mechanical properties of the ...JSC-1A Martian regolith simulant (MRS) are characterized using conventional and advanced laboratory techniques. Particle images are obtained using X-ray computed tomography, from which particle shapes are characterized through a series of imaging processing techniques and are further used to generate irregularly-shaped numerical particles. The characterized particle size distribution and irregularly-shaped numerical particles are incorporated into a discrete element model to simulate grading and shape-dependent behavior of the JSC-1A MRS. The developed discrete element model is calibrated and validated against laboratory direct shear tests. Simulations without the consideration of particle shapes and simulations with a rolling resistance contact model are also performed to investigate the effect of particle shapes on the behavior of the JSC-1A MRS.
•The characteristics of various forms of energy are studied using finite difference-based strength reduction method.•Both the spatial distribution and evolution of the energy with increasing strength ...reduction factors are analyzed.•The effects of cross-correlated random field soil properties on the energy characteristics are investigated.•The sudden change in gravitational potential energy, dissipated energy, and kinetic energy serves as a good sign of slope failure.•Two distinct mechanisms in the effects of strength reduction on the elastic strain energy are identified and discussed.
The definition of slope failure is a critical issue for strength reduction method-based slope stability analysis. Conventional slope failure criteria, which rely heavily on the distributions of stress, strain and displacement throughout the slope, are relatively complicated and create ambiguity in practical applications. Recognizing such shortcomings, a new energy-based criterion for defining slope failure was recently developed in the literature. In this work, further analyses are performed to investigate the validity of this energy-based criterion for slope stability analysis in which spatially varying soil properties are considered. With the slope being modeled by the finite difference method, random field theory is adopted to generate cross-correlated soil strength properties for the slope. Then, the slope system is solved and analyzed with a consecutive series of strength reduction factors (SRFs). In particular, various forms of energy in the slope system are calculated with different SRFs, and the characteristics of the energy distribution and evolution with increasing SRFs are analyzed. The results indicate that the slope system exhibits a significant energy change when the SRF increases to a critical value, which is a good indicator of slope failure. The areas in the slope characterized by considerable energy dissipation present a profile that is very close to the profile of the critical slip surface of the slope. These findings regarding the characteristics of the energy distribution and energy evolution could be further utilized to develop more efficient approaches to determine factors of safety and critical slip surfaces.
Fourier series (FS) is an efficient tool for describing irregular geometries and has been employed to develop the FS-based particle model in the discrete element method (DEM). This work is devoted to ...extending the previous FS-based particle model to the Minkowski and Gilbert–Johnson–Keerthi (GJK)-based contact detection and resolution framework for DEM, and thus to improving its computational efficiency and compatibility with other conventional particle models. In the new FS-based particle model, instead of representing particle surface, the FS is proposed to represent the support function of particle surface. Particle surface and support points are then formulated based on the FS support function. As the Minkowski- and GJK-based detection and resolution framework relies heavily on the convexity of particles, the convexity constraint and the approach to generate convexity preserving FS-based particles are also presented. The accuracy of the new FS-based particle model for shape representation is analyzed using a set of irregular shape templates. DEM simulation of random packing and biaxial compression test with various particle models is also performed to demonstrate the computational performance and numerical stability of the new FS-based particle model.
Many natural and engineered granular materials consist mainly of irregular-shaped non-spherical particles. In this work, a novel Fourier series-based Discrete Element Method (FS-DEM) is developed for ...the computational mechanics of irregular-shaped particles. In FS-DEM, Fourier series-based particle geometric description and coordinate representation are introduced, where particle shapes are implicitly determined by FS coefficients, which remain constant and are independent of particle positions or kinematics. Using the FS-based particle representation, contact detection and resolution algorithms are then developed to identify contacts and resolve contact geometric features. The FS-DEM method is completed with recourse to conventional contact behavior, laws of motion, and movement integration. The accuracy and computational efficiency of the FS-DEM framework are evaluated via three numerical examples and compared with the Overlapping Discrete Element Cluster-based DEM method. Results demonstrate the robust and superior performance of the FS-DEM method and its potential for efficient computational modeling of irregular-shaped particle systems.
•Novel FS-DEM framework for computational mechanics of irregular-shaped particles.•FS-based method for particle geometric description and coordinate representation.•New contact detection and resolution algorithms proposed.•Robust and superior performance demonstrated for single and multiple-particle systems.
Neighbor searching is an essential and computationally heavy step in particle‐based numerical methods such as discrete element method (DEM), molecular dynamics, peridynamics, and smooth particle ...hydrodynamics. This article presents a novel approach to accelerate particle‐based simulations by leveraging ray tracing (RT) cores in addition to CUDA cores on RTX GPUs. The neighbor search problem is first numerically converted into a general ray tracing problem so that it can be possible to utilize the hardware acceleration of RT cores. A new, general‐purpose RT‐based neighbor search algorithm is then proposed and benchmarked with a prevailing cell‐based one. As a showcase, both algorithms are implemented into a GPU‐based DEM code for simulating large‐scale granular problems including packing, column collapse and debris flow. The overall simulation performance is examined with varying problem sizes and GPU specs. It demonstrates that the RT‐based simulations are 10%–60% faster than the cell‐based ones, depending on the simulated problems and GPU specs. This study offers a new recipe for next‐generation high‐performance computing of large‐scale engineering problems using particle‐based numerical methods.
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The discrete element method (DEM) has become a prominent tool for modeling granular media, whereas the development of versatile and efficient particle models for the modeling of ...irregular-shaped particles remains a heated topic and challenge. In this work, a new particle model based on polybézier curves to describe particle shapes is proposed for the modeling of smooth and irregular-shaped particles. In particular, cubic bézier curves are adopted because they have a fairly high degree of freedom in modeling curved geometries as well as a closed-form support function. With the particle geometry and support function derived from cubic polybézier curves, the Gilbert-Johnson-Keerthi algorithm is adopted to detect contacts, and the expanding polytope algorithm is adopted to resolve contact geometric features. To generate polybézier-based particle templates from images of particle shapes, a particle swarm optimization-based geometric fitting procedure is also developed. The effectiveness of the proposed particle model for shape representation is validated using a chart of particle shapes with various roundness and sphericity characteristics. DEM simulations of random packing and biaxial compression tests on polydispersed irregular-shaped particles are also presented, and the results show that the proposed model has fairly good computational efficiency and numerical stability.
•A detailed FDEM numerical method to simulate mechanical and fracturing responses of heterogeneous geomaterials with irregular inclusions is systematically developed.•A computational geometry method ...named CWSVM is proposed to control mesh quantity and quality.•A signed-distance-field-based discrete element method (SDF-DEM) is employed to approach the natural allocation and orientation of inclusions.•A combined constitutive model is proposed to consider the shearing hardening behaviour for the cohesive elements.•Effects of the interface strength on the mechanical and fracturing behaviours of inclusion-containing geomaterials are extensively discussed.
In this paper, a detailed FDEM approach to simulate the mechanical and fracturing responses of heterogeneous geomaterials with irregular inclusions is systematically developed. The inclusion surface morphology is first obtained through 3D scanning techniques. A computational geometry method, the curvature-weighted sphere Voronoi method (CWSVM), is adopted to control the mesh quantity and quality and ensure the efficiency and accuracy of the FDEM numerical model. A signed-distance-field-based discrete element method (SDF-DEM) is employed to approximate the natural distribution and orientation of inclusions. Heterogeneous geomaterials with large inclusion contents (such as 60% and 70%) are generated effectively and efficiently through this approach. Next, to model the fracturing process, a finite discrete element method (FDEM) model is developed by integrating cohesive elements into the mesh in a fast and efficient manner. In addition, a combined constitutive model is proposed to consider the shear-hardening behaviour of the cohesive elements. The proposed numerical approach is verified through comparison with experimental results, including the shape of inclusions and mechanical responses of geomaterials. The results demonstrate that both satisfactory precision and low calculation costs can be achieved using the proposed algorithm. The consequent simulation performance is verified through comparisons of observations and numerical results with experimental results for failure patterns and mechanical behaviours. In addition, the effects of the strength of the interfaces between the inclusions and matrix on the mechanical and fracturing characteristics of inclusion-containing geomaterials are analysed quantitatively. The mechanical strength decreases rather than increases with increasing content of inclusions when the interface strength is less than the matrix strength.
