Predicting and Controlling the Response of Particulate Systems through Grain-Scale Engineering
Abstract
Research Problem. The objective of the proposed research is to establish a framework to predict the continuum behavior of particulate systems by understanding and engineering a set of grain-scale features, termed here dynamic network attributes (DNA). The proposed work will develop the hypothesis that continuum behavior is encoded at the scale where neighboring grains interact. Technical Approaches. This proposal takes a radically different approach from the state-of-the-art by directly embracing the interconnection between the different spatial scales that interact in granular assemblies: grain, meso, continuum scale. The proposed MURI builds on multiple areas of knowledge including Physics, Mechanics, Mathematics, and Engineering. The multidisciplinary approach affords the proposal the great advantage of transforming the state-of-the-knowledge across disciplinary boundaries that, historically, have been silos of specialized knowledge. Likewise, we will use and develop the most advanced experimental techniques (e.g., x-ray, force measurements) and connect these to continuum building blocks such as effective stress and constitutive models. Each of the areas is led by world-experts in the field. We also strike a balance between theoretical, experimental, and computational approaches covering more than 6 orders of magnitude. Anticipated Outcome. This MURI will deliver a wealth of experimental data at an unprecedented level of resolution, including grain kinematics, and kinetics at critical state. In addition, the effective stress will be measured for the first time ever and connected to predictive, physics-based constitutive models and mathematical frameworks that are needed to solve boundary-value-problems at the field scale. This MURI will help establish a new framework to predict the continuum behavior of granular systems by understanding the grain-scale DNA and its role. This will have a transformative impact on DoD applications, such as GO/NOGO mobility maps that are powered by modeling and simulation techniques relying on constitutive models and computational techniques. Impact to DoD. Of particular interest--and motivation to our proposal--is the problem of mobility of military vehicles. (e.g., FED Alpha). Mobility is the foundation to defense operations, where terrain can severely restrict possibilities; thus, resulting in predictable paths that can be exploited by the adversary. At the heart of GO/NOGO mobility maps are models and simulations that need to reliably predict the interconnections between the vehicle and terrain. This is the reason for recent efforts such as the Next-Generation NATO Reference Mobility Model (NG-NRMM), where physics-based models at the grain and continuum scale are expected to be deployed. To make the vision of NG-NRMM, the connection between grain-scale DNA and continuum behavior needs to be understood and engineered, as we are proposing to do in this MURI. In practical terms, the Ouroboros between DNA, physical properties and model parameters is missing in action, as a result, the multiscale nature of granular mechanics is typically being captured by empirical models. A scalable, multiscale, physics-based approach is sorely needed. Approved for Public Release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 13, 2019
- Source ID
- W911NF1910245
Entities
People
- José E Andrade
Organizations
- Army Contracting Command
- California Institute of Technology
- United States Army