Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

Multiscale Modeling of Granular Materials

Granular materials have a “discrete” nature whose global mechanical behaviors are originated from the grain scale micromechanical mechanisms. The intriguing properties and non-trivial behaviors of a granular material pose formidable challenges to the multiscale modeling of these materials. Some of the key challenges include upscaling of coarse-scale continuum equation form fine-scale governing equations, calibrating material parameters at different scales, alleviating pathological mesh dependency in continuum models, and generating unit cells with versatile morphological details. This dissertation aims to addressing the aforementioned challenges and to investigate the mechanical behavior of granular materials through multiscale modeling. Firstly, a three-dimensional nonlocal multiscale discrete-continuum model is presented for modeling the mechanical behavior of granular materials. We establish an information-passing coupling scheme between DEM that explicitly replicates granular motion of individual particles and a finite element continuum model, which captures nonlocal overall response of the granular assemblies. Secondly, a new staggered multilevel material identification procedure is developed for phenomenological critical state plasticity models. The emphasis is placed on cases in which available experimental data and constraints are insufficient for calibration. The key idea is to create a secondary virtual experimental database from high-fidelity models, such as discrete element simulations, then merge both the actual experimental data and secondary database as an extended digital database to determine material parameters for the phenomenological macroscopic critical state plasticity model. This expansion of database provides additional constraints necessary for calibration of the phenomenological critical state plasticity models. Thirdly, a regularized phenomenological multiscale model is investigated, in which elastic properties are computed using direct homogenization and subsequently evolved using a simple three-parameter orthotropic continuum damage model. The salient feature of the model is a unified regularization framework based on the concept of effective softening strain. The unified regularization scheme is employed in the context of constitutive law rescaling and the staggered nonlocal approach to alleviate pathological mesh dependency. Lastly, a robust parametric model is presented for generating unit cells with randomly distributed inclusions. The proposed model is computationally efficient using a hierarchy of algorithms with increasing computational complexity, and is able to generate unit cells with different inclusion shapes

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware