A Multiscale Simulation Method and Its Application to Determine the Mechanical Behavior of Heterogeneous Geomaterials

To study the micro/mesomechanical behaviors of heterogeneous geomaterials, a multiscale simulation method that combines molecular simulation at the microscale, a mesoscale analysis of polished slices, and finite element numerical simulation is proposed. By processing the mesostructure images obtained from analyzing the polished slices of heterogeneous geomaterials and mapping them onto finite element meshes, a numerical model that more accurately reflects the mesostructures of heterogeneous geomaterials was established by combining the results with the microscale mechanical properties of geomaterials obtained from the molecular simulation. This model was then used to analyze the mechanical behaviors of heterogeneous materials. Because kernstone is a typical heterogeneous material that comprises many types of mineral crystals, it was used for the micro/mesoscale mechanical behavior analysis in this paper using the proposed method. The results suggest that the proposed method can be used to accurately and effectively study the mechanical behaviors of heterogeneous geomaterials at the micro/mesoscales

Bridging Nano and Micro-scale X-ray Tomography for Battery Research by Leveraging Artificial Intelligence

X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials' absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models.Comment: 33 pages, 5 figure

Earth Resources: A continuing bibliography with indexes, issue 13

This bibliography lists 524 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1977 and March 1977. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Simulation of damage mechanisms in weave reinforced materials based on multiscale modeling

A weave reinforced composite material with a thermoplastic matrix is investigated by using a multiscale chain to predict the macroscopic material behavior. A large-strain framework for constitutive modeling with focus on material non-linearities, i.e. plasticity and damage is defined. The ability of the geometric and constitutive models to predict the deformation and failure behavior is demonstrated by means of selected examples

APPLICATIONS OF INNOVATIVE BUILDING MATERIAL AND COMPUTER VISION METHODS IN GEOTECHNICAL ENGINEERING

Ph.D

Simulation of damage mechanisms in weave reinforced materials based on multiscale modeling

A weave reinforced composite material with a thermoplastic matrix is investigated by using a multiscale chain to predict the macroscopic material behavior. A large-strain framework for constitutive modeling with focus on material non-linearities, i.e. plasticity and damage is defined. The ability of the geometric and constitutive models to predict the deformation and failure behavior is demonstrated by means of selected examples

Remote Sensing of Earth Resources: A literature survey with indexes (1970 - 1973 supplement). Section 1: Abstracts

Abstracts of reports, articles, and other documents introduced into the NASA scientific and technical information system between March 1970 and December 1973 are presented in the following areas: agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Evolution, Monitoring and Predicting Models of Rockburst: Precursor Information for Rock Failure

Load/unload response ratio predicting of rockburst; Three-dimensional reconstruction of fissured rock; Nonlinear dynamics evolution pattern of rock cracks; Bayesian model for predicting rockburs

Application of the discrete element method for concrete fracturing.

This project focuses on discrete element modelling of fracturing of concrete material at meso-scale, and particularly on calibration of the particle assembly parameters to reproduce phenomenological properties of concrete, and on applying the discrete element method to analyze the failure mechanisms in a three-point bending test and debonding between the FRP sheet and the concrete. The particle flow code PFC2D and PFC3D are employed to carry out the parametric study but only PFC2D is used in the case studies. The calibration of properties of the numerical samples is conducted to determine the effects of the particle level input parameters on the elastic constants, the uniaxial compressive strengths and failure mode of particle assembly. The input parameters are divided into two groups, model constitutive parameters (e.g., particle and bond stiffness, bond shear and normal strengths and friction coefficient) and geometric and physical parameters (e.g., particle and specimen size, particle distribution and loading velocity.). The analysis is constructed using dimensional analysis and numerical uniaxial tests. A random aggregate generation algorithm is incorporated in the DEM code to reproduce the aggregate structure in real concrete material. The aggregate generation algorithm utilizes polygon and polyhedron as the basic shapes of aggregate and is capable of producing multi-graded concrete specimens with aggregate content up to 80% and 60% for two-dimensional and three-dimensional samples respectively. The mode I fracture behavior of three-phased concrete is then simulated by performing a virtual three-point bending test. The mortar matrix phase is simulated with the linear elastic-pure-brittle and softening bond model to ensure a fair comparison. The dynamic debonding process between the FRP sheet and the concrete is simulated with a particle assembly by a regular hexagonal packing arrangement where the heterogeneity of concrete is taken into account by incorporating the Weibull distribution. Based on the analysis of the modelling results, it is conclude that the fracture behavior of concrete can be satisfactorily captured by meso-scale DEM model and comprehensive parameter study allows more confidently implementation of particle flow code