Multiscale Analysis of Soil-Strap Interactions in Mechanically Stabilized Earth Retaining Walls

A numerical pullout test was built using the discrete element method (DEM) to model and capture the pullout response of steel reinforcements and soil in mechanically stabilized earth (MSE) walls. Through numerical modeling, microscale phenomena showing aggregate behavior in response to the reinforcement can be used to gain insight into the macroscale structure. The general setup of the simulation is a steel specimen encased in a rectangular apparatus filled with particles. A normal pressure is applied to the top layer of particles while the strap is slowly removed from the box until it reaches a prescribed displacement.The simulation was created using YADE, an open-source DEM software, which allows for rapid scene construction via scripting. The numerical model uses an iterative approach to step through time while resolving contacts at each step and translating those contacts into forces to ultimately provide updated positions for each body at every time step. For this research, a non-cohesive, elastic-frictional Cundall-Strack contact model was employed to resolve interactions on an individual body basis. Test parameters were largely based on the experimental setup of pullout tests performed by Weldu. Particle packings for the pullout simulation were calibrated to the aggregate used in Weldu’s experiments by setting up a simple triaxial compression simulation within YADE to derive the correct microscale particle friction angle such that it produced the proper macroscale behavior.Using the numerical model, three sets of experiments from Weldu’s research were reproduced with particle uniformity coefficients of 1, 2, and 3. Simulations sets were run at various normal pressures and included 400,860 particles at the upper end. The numerical tests resulted in an encouraging degree of correlation to the laboratory experiments, with pullout residuals being as close as 2% different and an average of 14% different. In addition, this thesis discusses some of the microscale data extracted from the simulations, such as force chains and rolling characteristics, and how numerical simulations could be used in the future to help guide pullout testing and MSE wall design

Image-based Modeling of Flow through Porous Media: Development of Multiscale Techniques for the Pore Level

Increasingly, imaging technology allows porous media problems to be modeled at microscopic and sub-microscopic levels with finer resolution. However, the physical domain size required to be representative of the media prohibits comprehensive micro-scale simulation. A hybrid or multiscale approach is necessary to overcome this challenge. In this work, a technique was developed for determining the characteristic scales of porous materials, and a multiscale modeling methodology was developed to better understand the interaction/dependence of phenomena occurring at different microscopic scales. The multiscale method couples microscopic simulations at the pore and sub-pore scales. Network modeling is a common pore-scale technique which employs severe assumptions, making it more computationally efficient than direct numerical simulation, enabling simulation over larger length scales. However, microscopic features of the medium are lost in the discretization of a material into a network of interconnected pores and throats. In contrast, detailed microstructure and flow patterns can be captured by modern meshing and direct numerical simulation techniques, but these models are computationally expensive. In this study, a data-driven multiscale technique has been developed that couples the two types of models, taking advantage of the benefits of each. Specifically, an image-based physically-representative pore network model is coupled to an FEM (finite element method) solver that operates on unstructured meshes capable of resolving details orders of magnitude smaller than the pore size. In addition to allowing simulation at multiple scales, the current implementation couples the models using a machine learning approach, where results from the FEM model are used to learn network model parameters. Examples of the model operating on real materials are given that demonstrate improvements in network modeling enabled by the multiscale framework. The framework enables more advanced multiscale and multiphysics modeling – an application to particle straining problems is shown. More realistic network filtration simulations are possible by incorporating information from the sub-pore-scale. New insights into the size exclusion mechanism of particulate filtration were gained in the process of generating data for machine learning of conductivity reduction due to particle trapping. Additional tests are required to validate the multiscale network filtration model, and compare with experimental findings in literature

Pore-to-continuum Multiscale Modeling of Two-phase Flow in Porous Media

Abstract Pore-scale network modeling using 3D X-ray computed tomographic images (digital rock technology) has become integral to both research and commercial simulations in recent years. While this technology provides tremendous insight into pore-scale behavior, computational methods for integrating the results into practical, continuum-scale models remain fairly primitive. The general approach is to run pore-scale models and continuum models sequentially, where macroscopic parameters are simulated using the pore-scale models and then used in the continuum models as if they have been obtained from laboratory experiments. While a sequential coupling approach is appealing in some cases, an inability to run the two models concurrently (exchanging parameters and boundary conditions in real numerical time) will prevent using pore-scale image-based modeling to its full potential. In this work, an algorithm for direct coupling of a dynamic pore-network model for multiphase flow with a traditional continuum-scale simulator is presented. The ability to run the two models concurrently is made possible by a novel dynamic pore-network model that allows simultaneous injection of immiscible fluids under either transient or steady-state conditions. The dynamic network algorithm can simulate both drainage and imbibition. Consequently, the network algorithm can be used to model a complete time-dependent injection process that comprises a steady-state relative permeability test, and also allows for coupling to a continuum model via exchange of information between the two models. Results also include the sensitivity analysis of relative permeability to pore-level physics and simulation algorithms. A concurrent multiscale modeling approach is presented. It allows the pore-scale properties to evolve naturally during the simulated reservoir time step and provide a unique method for reconciling the dramatically different time and length scales across the coupled models. The model is tested for examples associated with oil production and groundwater transport in which relative permeability depends on flowrate, thus demonstrating a situation that cannot be modeled using a traditional approach. This work is significant because it represents a fundamental change in the way we might obtain continuum-scale parameters in a reservoir simulation

Experimental and computational analysis of random cylinder packings with applications

Random cylinder packings are prevalent in chemical engineering applications and they can serve as prototype models of fibrous materials and/or other particulate materials. In this research, comprehensive studies on cylinder packings were carried out by computer simulations and by experiments. The computational studies made use of a collective rearrangement algorithm (based on a Monte Carlo technique) to generate different packing structures. 3D random packing limits were explored, and the packing structures were quantified by their positional ordering, orientational ordering, and the particle-particle contacts. Furthermore, the void space in the packings was expressed as a pore network, which retains topological and geometrical information. The significance of this approach is that any irregular continuous porous space can be approximated as a mathematically tractable pore network, thus allowing for efficient microscale flow simulation. Single-phase flow simulations were conducted, and the results were validated by calculating permeabilities. In the experimental part of the research, a series of densification experiments were conducted on equilateral cylinders. X-ray microtomography was used to image the cylinder packs, and the particle-scale packings were reconstructed from the digital data. This numerical approach makes it possible to study detailed packing structure, packing density, the onset of ordering, and wall effects. Orthogonal ordering and layered structures were found to exist at least two characteristic diameters from the wall in cylinder packings. Important applications for cylinder packings include multiphase flow in catalytic beds, heat transfer, bulk storage and transportation, and manufacturing of fibrous composites

Multiscale modeling of segregation in granular flows

Modeling and simulation of segregation phenomena in granular flows are investigated. Computational models at different scales ranging from particle level (microscale) to continuum level (macroscale) are employed in order to determine the important microscale physics relevant to macroscale modeling. The capability of a multi-fluid model to capture segregation caused by density difference is demonstrated by simulating grain-chaff biomass flows in a laboratory-scale air column and in a combine harvester. The multi-fluid model treats gas and solid phases as interpenetrating continua in an Eulerian frame. This model is further improved by incorporating particle rotation using kinetic theory for rapid granular flow of slightly frictional spheres. A simplified model is implemented without changing the current kinetic theory framework by introducing an effective coefficient of restitution to account for additional energy dissipation due to frictional collisions. The accuracy of predicting segregation rate in a gas-fluidized bed is improved by the implementation. This result indicates that particle rotation is important microscopic physics to be incorporated into the hydrodynamic model. Segregation of a large particle in a dense granular bed of small particles under vertical vibration is studied using molecular dynamics simulations. Wall friction is identified as a necessary condition for the segregation. Large-scale force networks bearing larger-than-average forces are found with the presence of wall friction. The role of force networks in assisting rising of the large particle is analyzed. Single-point force distribution and two-point spatial force correlation are computed. The results show the heterogeneity of forces and a short-range correlation. The short correlation length implies that even dense granular flows may admit local constitutive relations. A modified minimum spanning tree (MST) algorithm is developed to asymptotically recover the force statistics in the force networks. This algorithm provides a possible route to constructing a continuum model with microstructural information supplied from it. Microstructures in gas fluidized beds are also analyzed using a hybrid method, which couples the discrete element method (DEM) for particle dynamics with the averaged two-fluid (TF) equations for the gas phase. Multi-particle contacts are found in defluidized regions away from bubbles in fluidized beds. The multi-particle contacts invalidate the binary-collision assumption made in the kinetic theory of granular flows for the defluidized regions. Large ratios of contact forces to drag forces are found in the same regions, which confirms the relative importance of contact forces in determining particle dynamics in the defluidized regions

Transient Study of the Wetting Films in Porous Media Using 3D X-Ray Computed Micro-Tomography: Effect of Imbibition Rate and Pore Geometry

Imbibition in porous media is governed by the complex interplay between viscous and capillary forces, pore structure and fluid properties. Understanding and predicting imbibition is important in many natural and engineered applications; it affects the efficiency of oil production operations, the moisture and contaminant transport in soil science, and the formation of defects in certain types of composite materials. Majority of the studies published on the transient imbibition behavior in a porous medium were conducted in the simplified 2D transparent micromodels or the 2D projection visualization (X-ray or visible light) of the 3D porous medium. However, the pore level transient imbibition studies have not been reported on real three dimensional porous medium. The main challenge arises from the slowness of the present 3D imaging techniques in comparison with the speed of the pore filling events. To overcome these difficulties, we have developed a novel experimental technique using UV-induced polymerization, which allows the fluid phase distributions to be frozen in place during transient imbibition. Pore-scale structure of the front can then be examined in the 3D microscopic details using the X-ray Computed micro-Tomography (XCT). We have also developed a suite of advanced image segmentation programs to segment the grayscale XCT data. Image-based physically representative pore network generation techniques were unitized to quantify the geometry and topology of pore, wetting and nonwetting phase structure. Using UV initiated polymerization technique and image-based quantitative analysis tools; we have studied the effects of capillary number, pore structure and surface roughness on the structure of the transient imbibition front

Modeling the Microstructural and Micromechanical Influence on Effective Properties of Granular Electrode Structures with regard to Solid Oxide Fuel Cells and Lithium Ion Batteries

The work studies electrode structures and the influence on the performance of electrochemical cells. Porous electrodes structures are modeled as a mixture of electron and ion conducting particles, densified considering manufacturing: sintering of SOFC is approximated geometrically; calendering and intercalation in LIB are modeled by a discrete element approach. A tracking algorithm plus a resistor network approach allow predicting connectivity, conductivity and active area of various structures

Broadband Thin Film Absorber Based on Plasmonic Nanoparticles

Harvesting solar energy presents a formidable challenge, primarily rooted in the need to capture light across a broad spectrum range efficiently. Addressing this challenge, we describe the concept of designing a broadband perfect absorber in the form of a thin-film system with plasmonic nanoparticles as its foundational basis. We study a thin-film absorber made from the scattering responses of an Au144 gold molecule. It turns out that this thin-film absorber absorbs the light in the entire visible light region quite well. As a further aspect, we employ bulk copper nanoparticles as the basis for the nanoparticle layer within the absorber. We inspect on computational grounds the effect of the nanoparticles filling factor and the thin-film thicknesses on the absorber performance. Remarkably, our findings reveal that the thin-film absorber with copper nanoparticles can absorb 90% of light energy across a broad spectrum ranging from ultraviolet to near-infrared wavelengths. To validate the accuracy of our simulations, we translate these optimized absorber layouts into fabrications together with experimental partners from the University of Kiel. The experimental results align remarkably closely with our simulations, confirming the capability of our designed broadband perfect absorber