UCRL-PROP-220148

**[Material Damage and Failure]{.underline}**

**3/13/06**

**Abstract**

The Material Damage and Failure area seeks to improve and expand
fundamental computational and physical knowledge of the material failure
process. This is a broad area and it underpins many applications.
Substantial ongoing efforts in material failure prediction exist in the
NNSA Defense Program Laboratories. The intent of this paper is not to
provide a list of ongoing material failure projects in the Laboratories
but rather, present a comprehensive description of the pertinent issues
in material damage and failure prediction that are currently facing the
Laboratories. We particularly seek innovative ideas and collaborations
that enhance our capabilities to predict material failure in these
topical areas.

**Introduction**

Predicting the process of material failure and fracture is an extremely
challenging scientific problem, central to a number of applications from
the performance of munitions to explosive forming processes to
predicting the long-time structural integrity of engineered structures.
The conditions can range from quasi-static tensile failure to high
strain rate shock loading, for example the failure of metals under
explosive loading. To predict the component and system performance,
safety and reliability under this wide variety of conditions also
requires the ability to understand the subsequent response of the
damaged material. The development of physics-based models that are
capable of describing both the failure process and the subsequent
response of materials is thus of great interest.

Truly predictive failure models require understanding the process at all
relevant length scales, ranging from the atomistic scale where damage is
nucleated, through the continuum, where most large scale systems
simulations are performed. What are the critical processes at each
length scale? How do we integrate models across length scales? How does
damage nucleate and evolve in a material under a variety of loading
conditions? How much of the microphysics needs to be accounted for
explicitly and what can be glossed over? These are all important
questions to be addressed.

For example, the damage and failure process for polycrystalline metals
is a sophisticated sequence of physical events occurring at different
length scales. It is a stochastic process that is influenced by loading
state, microstructure, and boundary conditions. Physical manifestations
of a ductile process are generally varying combinations of porosity
nucleation and growth, as well as shear localization and banding.
Brittle behavior often exhibits plastic deformation in combination with
mixed mode crack initiation, growth, crack percolation and ultimately
failure. Generally both ductile and brittle behaviors are of interest.
Although lower length scale work is invaluable for the articulation of
the physical processes leading to failure, there is still strong
practical need to develop continuum level tools capable of predicting
the phenomena in question.

The problem can be even more complex since under dynamic loading
conditions many materials exhibit solid-solid phase transformations.
This transformation in crystallographic structure is also a complex
stochastic process that may transition between crystallographic
structures which may change the response of the material from ductile to
brittle or vice-versa. Some crystallographic structures may be prone to
exhibit one type of behavior over another. Still others exhibit
transitions between ductile and brittle behaviors depending upon loading
and strain rate which may or may not be related to a phase
transformation.

Standard mechanical strength models for metallic materials spell out the
relationship between stress and the resulting strain during elastic and
stable plastic deformation. However at some background strain, metals
will transition from uniform or homogeneous deformation to heterogeneous
or localized unstable behavior. To be predictive, models must capture
the fundamental relationships connecting the independent variables of
stress, strain-rate, strain, and temperature to specific bulk material
responses such as yield strength, strain-hardening, texture evolution,
evolution of global damage, subsequent heterogeneous damage such as
strain localization and cracking, and finally material failure.
Moreover, for the applications-of-interest, we need to predict those
responses accurately for extreme conditions such as at large
deformation, high strain-rates, high pressures, and high temperatures.
Our materials models must therefore be based on quantifiable physical
mechanisms, characterized with inexpensive direct experiments, and
validated via comparison with small-scale and integral tests.

Although we use metals to illustrate the technical points, our interest
is broad and not limited to this class of materials. The following list
of topics, though not exhaustive, provides indication of areas of
interest:

-   Post-Failure material response and subsequent behavior

-   Failure simulation capabilities

-   Failure under long-term aging and aggressive environments

-   Failure simulation spanning long time scales

-   Material failure in micro and nano systems

A brief discussion on what is needed in each topical area is given in
the following sections.

**Post-Failure material response and subsequent behavior**

Conventional practices commonly design systems with a large margin
against failure, either at the component or system level. This approach
can be unnecessarily conservative and provides little insight into the
fundamental physical processes underlying failure or key potential
failure modes. The ability to predict post-failure response of materials
can potentially enable more cost-efficient designs by assessing design
constraints more accurately and providing a more realistic assessment of
risks. In most cases, initiation of material failure does not directly
translate into system collapse. Understanding how materials fail and
also the subsequent load transferring process will result in an
efficient system design by realizing the full potential of material
load-bearing capabilities.

**Failure simulation capabilities**

Despite recent advances in computational sciences and engineering,
simulation of fracture and failure of materials remains one of the most
challenging material science problems extant. The difficulties arise not
only from the need for more fundamental understanding of the underlying
physical processes in order to motivate physically-based models to
describe material failure, but also in developing numerical methods that
can treat the changing geometries associated with the creation of new
free surfaces and the excessive bulk deformations. The simulation of the
behavior of damaged material puts further demands on the ability to
simulate the evolution of the failure process. Numerical algorithms that
will alleviate the constraints imposed by discretization and mimic the
physical process of strength degradation are needed for predictive
material failure simulations.

**Failure under long-term aging and aggressive environments**

The extension of stockpile life has resulted in increased attention to
the response of materials under the combination of aging and aggressive
environmental conditions. Material failure models are required that can
not only predict damage but also assess the full life-cycle for a
material, component or system in order to support lifetime predictions,
providing the additional challenge of mixing long- and short-time-scale
processes. For example, the phenomena of diffusion, corrosion, and
oxidation affect performance in most of the systems (micro and macro) of
interest. However, little scientific understanding has been developed on
the interactions that occur over these larger time scales, and on the
magnitude of their effect as compared with mechanistic behavior observed
over much shorter times.

**Failure simulation spanning long time scales**

Critically important to life-cycle predictions is the extension of
simulation technologies to very long time scales. While this is
currently possible with meso-scale models and simulations, the models in
use at these scales are often ad hoc or empirical in nature, limiting
their predictive capability. At the microscale, where many of the
interesting physical processes begin, the time scales accessible by
simulation are very short and are currently inadequate to this task.
Development on this topic would have dramatic impact on our ability to
model phenomena such as the behavior of impurities/gases in metallic and
ceramic materials, adhesion between metallic and polymer thin films on
ceramic substrates used in nano-scale devices, and the material property
degradation caused by aging processes of corrosion and oxidation.

**Failure of micro and nano systems**

Nano- and micro-technologies can present particular challenges for new
material failure models. Application of conventional continuum mechanics
to nano- and micro-systems has been problematic and little understanding
of the failure response of such systems exists. Observed size-dependent
material behavior adds more complexity to the problem. Development of
failure models that can lead to the prediction of nano- and micro-system
performance and reliability is encouraged.

Contact: Er-Ping Chen (<epchen@sandia.gov>)

Curt Bronkhorst (<cabronk@lanl.gov>)

Kimberly Budil (<ksbudil@llnl.gov>)

**Acknowledgement**

This work was, in part, performed by the Lawrence Livermore National
Laboratory, Los Alamos National Laboratory, and Sandia National
Laboratories under auspices of the U.S. Department of Energy.

This document was prepared as an account of work sponsored by an agency
of the United States Government. Neither the United States Government
nor the University of California nor any of their employees, makes any
warranty, express or implied, or assumes any legal liability or
responsibility for the accuracy, completeness, or usefulness of any
information, apparatus, product, or process disclosed, or represents
that its use would not infringe privately owned rights. Reference herein
to any specific commercial product, process, or service by trade name,
trademark, manufacturer, or otherwise, does not necessarily constitute
or imply its endorsement, recommendation, or favoring by the United
States Government or the University of California. The views and
opinions of authors expressed herein do not necessarily state or reflect
those of the United States Government or the University of California,
and shall not be used for advertising or product endorsement purposes.

This work was performed under the auspices of the U.S. Department of
Energy by University of California, Lawrence Livermore National
Laboratory under Contract W-7405-Eng-48.