Numerical study of damage in particulate composites during high-strain rate loading using novel damage model
Not provided.
Materials Science↗
Engineering topics
Matouš, Karel
Publications and source records attributed to Matouš, Karel.
A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling
Developing an accurate nonlinear reduced order model from simulation data has been an outstanding research topic for many years. For many physical systems, data collection is very expensive and the optimal data distribution is not known in advance. Thus, maximizing the information gain remains a grand challenge. In a recent paper, Bhattacharjee and Matouš (2016) proposed a manifold-based nonlinear reduced order model for multiscale problems in mechanics of materials. Expanding this work here, we develop a novel sampling strategy based on the physics/pattern-guided data distribution. Our adaptive sampling strategy relies on enrichment of sub-manifolds based on the principal stretches and rotational sensitivity analysis. This novel sampling strategy substantially decreases the number of snapshots needed for accurate reduced order model construction (i.e., ~5 x reduction of snapshots over Bhattacharjee and Matouš (2016)). Moreover, we build the nonlinear manifold using the displacement rather than deformation gradient data. We provide rigorous verification and error assessment. We demonstrate both localization and homogenization of the multiscale solution on a large particulate composite unit cell.
97 MATHEMATICS AND COMPUTING↗
Chemo-thermal model and Gaussian process emulator for combustion synthesis of Ni/Al composites
Not provided.
Energy & Fuels↗
Asynchronous space–time domain decomposition method with localized uncertainty quantification
The computational cost associated with uncertainty quantification of engineering problems featuring localized phenomenon can be reduced by confining the random variability of the model parameters within a region of interest. Here in this case, a localized treatment of mesh and time resolutions is required to capture the effect of the confined material uncertainty on the global response. We present a computational approach for localized uncertainty quantification with the capability of asynchronous treatment of mesh and time resolutions. In particular, we allow each subdomain to have its local uncertainty representation and the corresponding mesh and time resolutions. As a result, computing resources can be directed toward a small region of interest where a model with high spatial and temporal resolutions is required. To verify the numerical implementation, we consider elastic wave propagation in an axially loaded beam. Moreover, we perform convergence studies with respect to the spatial and temporal discretizations as well as the size of an uncertain subdomain. A projectile impacting a composite sandwich plate is considered as an engineering application for the proposed method.
42 ENGINEERING↗
Sharp volumetric billboard based characterization and modeling of complex 3D Ni/Al high energy ball milled composites
Not provided.
Materials Science↗
A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials
Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
X-ray nanotomography and focused-ion-beam sectioning for quantitative three-dimensional analysis of nanocomposites
Knowing the relationship between three-dimensional structure and properties is paramount for complete understanding of material behavior. In this work, the internal nanostructure of micrometer-size (~10 µm) composite Ni/Al particles was analyzed using two different approaches. The first technique, synchrotron-based X-ray nanotomography, is a nondestructive method that can attain resolutions of tens of nanometers. The second is a destructive technique with sub-nanometer resolution utilizing scanning electron microscopy combined with an ion beam and `slice and view' analysis, where the sample is repeatedly milled and imaged. The obtained results suggest that both techniques allow for an accurate characterization of the larger-scale structures, while differences exist in the characterization of the smallest features. Using the Monte Carlo method, the effective resolution of the X-ray nanotomography technique was determined to be ~48 nm, while focused-ion-beam sectioning with `slice and view' analysis was ~5 nm.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
A nonlinear manifold-based reduced order model for multiscale analysis of heterogeneous hyperelastic materials
Not Available
Bhattacharjee, Satyaki↗
Computational homogenization at extreme scales
Multi-scale simulations at extreme scales in terms of both physical length scales and computational resources are presented. In this letter, we introduce a hierarchically parallel computational homogenization solver that employs hundreds of thousands of computing cores and resolves in material length scales (from to ). Simulations of this kind are important in understanding the multi-scale essence of many natural and synthetically made materials. Thus, we present a simulation consisting of 53.8 Billion finite elements with 28.1 Billion nonlinear equations that is solved on 393,216 computing cores (786,432 threads). The excellent parallel performance of the computational homogenization solver is demonstrated by a strong scaling test from 4,096 to 262,144 cores. A fully coupled multi-scale damage simulation shows a complex crack profile at the micro-scale and the macroscopic crack tunneling phenomenon. Such large and predictive simulations are an important step towards Virtual Materials Testing and can aid in development of new material formulations with extreme properties. Furthermore, the high computational efficiency of our computational homogenization solver holds great promise for utilizing the next generation of exascale parallel computing platforms that are expected to accelerate computations through orders of magnitude increase in parallelism rather than speed of each processor.
Engineering↗