The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - September/October (1999 vol.1)
pp: 20-23
Published by the IEEE Computer Society
ABSTRACT
How materials fracture is one of the most fundamental problems in materials science and engineering. Typically, the stress fields near the crack tip are highly nonlinear, and stress-field decay far from the tip is very slow, making fracture a difficult problem. Progress in large-scale scientific computing during the last decade has helped researchers successfully study dynamic fracture.
How materials fracture is one of the most fundamental problems in materials science and engineering. Typically, the stress fields near the crack tip are highly nonlinear, and stress-field decay far from the tip is very slow, making fracture a difficult problem. Progress in large-scale scientific computing during the last decade has helped researchers successfully study dynamic fracture.
Dynamic fracture involves multiple scales of length and time. When a material with a notch is stretched, a crack starts to propagate. Initially, the crack propagates slowly and leaves behind smooth, mirror-like fracture surfaces. The crack then accelerates to a certain critical speed at which the propagation becomes unstable. The resulting fracture surfaces are very rough and sometimes involve multiple branches (see Figure 1). Crack branching is also a highly nonlinear and complex phenomenon.


Figure 1. An example of crack branching in dynamic fracture. (a) Molecular-dynamics simulation of dynamic fracture in a graphite sheet at a high strain rate, which we and our colleagues performed at the Concurrent Computing Laboratory for Materials Simulations at Louisiana State University. (b) Krishnaswamy Ravi-Chandar's experimental observation of crack branching in glass. Note that the length scales in (a) and (b) differ by a factor of 10 5, yet at these two widely different length scales, the nature of the crack branching is remarkably similar.

A special type of fractal geometry called self-affine fractal helps us describe rough fracture surfaces. When we view a fracture surface at different length scales—for example, using a microscope with continuously varying magnification—the root-mean square roughness of the surface varies as Lζ. The roughness exponent, ζ, denotes how the roughness scales with the length, L. Researchers have experimentally measured fracture-surface roughness with optical, atomic-force, and scanning-tunneling microscopes for a wide variety of materials (such as metals, alloys, semiconductors, and ceramics). The scaling law for fracture-surface roughness is L0.8. Surprisingly, this scaling law of surface roughness (ζ = 0.8) is independent of the material or fracture mode.
From an engineering point of view, designing microstructures to increase materials' fracture toughness (or resistance to fracture) is becoming increasingly important. For example, ceramics are extremely brittle in conventional forms, but they can be made ductile when we assemble them by consolidating nanometer-sized powders. In ceramic matrix composites, the composite's toughness is often much larger than the average of its constituents' toughness values. You can understand this synergistic effect only by considering atomistic processes such as the structure and frictional motion at the interfaces.
Simulation Methods
Molecular-dynamics (MD) methods—which physicists, chemists, biologists, and engineers alike regularly use now—have introduced great degrees of cross-fertilization in various fields. Availability of efficient, scalable, and portable parallel-simulation algorithms coupled with sustained teraflop computing speeds on parallel computers are making it possible to perform real-materials simulations.
The ambitious goal of realistic-materials simulations is to study synthesis, processing, and properties of new materials before experimental synthesis and to test their behavior in extreme environments of temperature, pressure, and uniaxial stresses. However, in an age where we have a trillion-dollar economy of manmade materials in energy, transportation, aerospace, electronics, and defense technologies, such an approach will clearly yield enormous dividends.
MD is a powerful tool for understanding dynamic fracture that involves long-range stress-mediated phenomena and mechanical nanostructure failure. MD simulation provides phase-space trajectories—positions and velocities of all atoms at all times—which are then analyzed using classical statistical mechanics. For realistic modeling of these systems, however, you must extend the scope of simulations to large system sizes and long simulated times.
The most prohibitive computational problem in these simulations is associated with the calculation of long-range parts of the interatomic potentials—the Coulomb interaction between charged atoms in the material. To overcome this problem, various researchers have designed space-time multiresolution algorithms. These include the computation of the Coulomb interaction with the Fast Multipole Method, which reduces the computation from O( N2) to O( N) for an N-atom system. A multiple time-scale approach exploits disparate time scales associated with slowly and rapidly varying parts of interatomic interactions.
Understanding dynamic fracture in metal, ceramic, or polymer components requires microscopic examination of plasticity. This is due to dislocation emission and the interaction of cracks with defects such as grain boundaries. In this context, it is also important to know the stress inhomogeneities within the system.
Environmental effects are also important in dynamic fracture. Oxidation is one of the major causes of damage, especially at high temperatures and under stress. For example, oxidation embrittlement of ceramic-matrix composites involves ingress of oxygen through matrix cracks in the composite, and it drastically changes structural performance. Most metals, polymers, and some ceramics are not stable against oxidation. Design and lifetime prediction of materials depend crucially on understanding oxidation's effects.
Recent developments in parallel-computing technologies have made it possible to perform atomistic simulations of dynamic fracture containing up to 100 million atoms. A number of issues mentioned earlier—dynamic crack instability, roughness of fracture surfaces, and toughness of nanophase materials and composites—are under investigation in various experiments. Effects of oxidation and other reactive environments are also being attempted. However, computing at this scale requires innovative parallel algorithms. These include space-time multiresolution schemes, load balancing for parallel processing, data compression for scalable I/O, and visualization of and knowledge discovery from very large data sets to make the simulation results comprehensive to humans.
Although atomistic processes are essential to understanding dynamic fracture, the stress field associated with a crack is very long-ranged. Accordingly, it is impossible to separate boundary conditions from crack dynamics. Coupling of length scales is therefore essential for an understanding of macroscopic fracture phenomena. Hybrid schemes combining finite-element approaches based on linear elasticity and atomistic simulations using MD method and electronic-structure calculations have a promising future (see the " Matching length scales" sidebar). However, the complexity of these hybrid schemes poses an unprecedented challenge in scientific software development. Dynamic fracture thus provides an exciting testbed for multidisciplinary research between materials science and computer science.
The Articles
This issue on dynamic fracture analysis features five articles that survey important developments in the field and speculate about future research directions. The survey is by no means a comprehensive one.
This issue combines the confluence of ideas and expertise on fracture from diverse points of view, ranging from high-performance computing and communications to atomistic simulations. The first article, written by Krishnaswany Ravi-Chandar and Wolfgang Knauss, introduces experimental issues in fracture and how multi-scale computational schemes can answer some of the outstanding problems. The article by Elisabeth Bouchaud and Florin Paun deals with combined experimental and computational approaches to the fascinating problem of fracture-surface roughness, which has puzzled numerous mathematicians, computer scientists, physicists, materials scientists, and engineers. The article by Aiichiro Nakano, Rajiv K. Kalia, and Priya Vashishta reviews computer science techniques that enable large-scale atomistic simulations of fracture, including multiresolution approaches to parallel computing, data management, and visualization. Michael Marder's article also uses both computer simulations and laboratory experiments to understand the complex, nonlinear dynamics of fracture. Finally, Priya Vashishta, Rajiv K. Kalia, and Aiichiro Nakano explain crucial roles played by large-scale atomistic simulations to understand a number of materials issues in dynamic fracture—nanostructural design of novel high-fracture-toughness materials, environmental effects on fracture, and interfacial and nanoindentation fractures.
A word about computational science education: From the undergraduate and graduate student point of view, the standard university-level physics, chemistry, materials science, or engineering curricula seldom cultivate the ability to perform large-scale computer simulations. Such an effort requires an integration of several disciplines covered in disjointed courses. We hope to someday see courses on simulation methods that integrate these disparate disciplines under one unified point of view. 1,2

References

Priya Vashishta is the Cray Research Professor of computational sciences in the Department of Physics and Astronomy and the Department of Computer Science at Louisiana State University. He cofounded the Louisiana State University Concurrent Computing Laboratory for Materials Simulations. His research interests include very large-scale atomistic simulations of synthesis, processing, and properties of novel materials, nanoscale devices, and dynamic fracture on massively parallel and distributed computers. Contact him at the CCLMS, Nicholson Hall, Louisiana State Univ., Baton Rouge, LA 70803-4001; priyav@bit.csc.lsu.edu; http://www.cclms.lsu.edu.
Aiichiro Nakano is an associate professor of computer science and a member of the Concurrent Computing Laboratory for Materials Simulations at Louisiana State University. He obtained his PhD in theoretical physics from University of Tokyo, Japan. His research interests include parallel multilevel algorithms; visualization, data-management, and networking technologies for computational sciences; computer-aided nanomaterials design; computational nanoelectronics; and dynamic fracture. Contact him at the Dept. of Computer Science, Coates Hall, Louisiana State Univ., Baton Rouge, LA 70803-4020; nakano@bit.csc.lsu.edu; http://www.cclms.lsu.edu.
5 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool