The Institute for Security Education and Research
(INSER) is a joint Texas A&M University, Texas A&M
Engineering Experiment Station (TEES), and Texas A&M Agrilife
Extension Service center. INSER is funded by Lawrence Livermore
National Laboratory as part of Texas A&M's role as a affiliate
of Livermore National Security LLC, which manages the laboratory.
The center has one educational component and three research
components, one of which is computational methods development in
support of nuclear weapons stockpile stewardship. This component is
executed through CLASS as a partner with INSER. There are currently
three major research projects with tasks as follows:
Materials Science:

Strength of Materials:
Using firstprinciplesbased interaction potentials within
largescale atomistic simulations Texas A&M will investigate
structure, energetics, and mobility of dislocation cores, with
input from particle based simulations. Texas A&M will also
perform microscopic simulations of dislocationdislocation
interactions using tessellationbased dislocation dynamics. As a
first step towards modeling of assemblies of grains/polycrystalline
plasticity, TEES will use both atomistic simulations of grain
boundaries and develop a 2.5dimensional dislocationdynamics
program.

Phase Transformations:
In this area, Texas A&M will use an integrated effort
combining detailed firstprinciples atomistic simulations with
phasefield and thermodynamics modeling. This task proposes a novel
integrated computational study by employing stateoftheart
multiscale modeling and simulations to address the solidsolid
phase transformations in plutonium and its alloys. The four
subtasks associated with this task are
 Abinitio calculations on phases of Plutonium and its
alloys
 MD simulations on phases of Plutonium and its alloys
 Thermodynamic and kinetic modeling of Plutonium and alloys
 Phase field modeling of phase transformations and
microstructure evolution in Plutonium and its alloys
Massively Parallel RadiationHydrodynamics including Uncertainty
Quantification:
This project contains tasks in discretization methods and
algorithms for coupled physics, computer science support of
massively parallel calculations, and assessment of the accuracy of
uncertaintyquantification methodologies. These tasks are
integrated into a coordinated project that will couple radiation
transport and hydrodynamics in massively parallel software and will
assess uncertaintyquantification methodologies applied to such
radiationhydrodynamics simulations. The four tasks associated with
this project are

Multiphysics Coupling and Operator
Splitting
Radiationhydrodynamics codes have traditionally used simple
operator splitting as a timeintegration technique. The advantage
of simple operator splitting is that it can be easily implemented.
The disadvantage is that operator splitting is only firstorder
accurate, and it can be unreliable in that false convergence can
occur as the time step is decreased. A first step in developing a
successful secondorder operator splitting technique is to develop
a time discretization for the radiation transport equation with
properties lacking in current secondorder discretizations. Texas
A&M will develop a secondorder accurate nonoscillatory time
discretization for the multifrequency discrete ordinates equations
that can be efficiently solved using Krylov methods preconditioned
with the linear multifrequencygrey acceleration technique.

Massively Parallel Computing
 Development of the ARMI communication library
Adaptive Remote Method Invocation (ARMI) is a communication
library that uses the remote method invocation (RMI) communication
abstraction to hide the lower level implementations (e.g., MPI,
OpenMP, PThreads, the machine's native communication primitives,
etc.). TEES will design and develop improved methods for
implementing global distributed locks, including support for
recursive locks. TEES will provide support for additional
communication patterns and groupbased versions of the operations
that select subgroups of threads for groupbased communication.
 Development of a distributed graph and partitioning
tools
The pGraph is a parallel and distributed graph data structure
that is being developed as one of the core pContainers of the STAPL
project. Texas A&M will further develop the pGraph and
related scheduling and partitioning tools.
 Implementation of radiationhydrodynamics software
Other tasks associated with massively parallel computing involve
discretization and coupling techniques for radiation transport and
for hydrodynamics. These techniques will be implemented and tested
in a massively parallel radiationhydrodynamics code that TEES will
assemble.
 Uncertainty Quantification
We are entering an era in which methodologies for uncertainty
quantification (UQ) are becoming more complicated, and are being
used to support highconsequence decisions. It is therefore
important to assess how accurately these methods quantify
uncertainty or predictive capability. Texas A&M will use a
"method of manufactured universes" (MMU) to quantitatively assess
methodologies that are designed to quantitatively assess predictive
capability. The MMU concept is newly developed at Texas A&M and
is based upon the idea of defining "laws of physics" for the
"universe" that can be exactly modeled and using "approximate
models" to predict the outcome of "experiments" generated according
to these laws.
Hydrodynamics and Transport Methods:
This project contains tasks in hydrodynamics methods and
radiation transport methods. Each task is described below.

High Order Hydrodynamics Methods
Texas A&M will develop a discontinuous Galerkin method in
rz geometry based upon a quadratic trial space for an unstructured
triangular mesh. Rather than upwind in the usual manner, Texas
A&M will use central averaging in conjunction with a new type
of entropybased artificial viscosity that has recently been
developed by Texas A&M researchers. In generalizing this
artificial viscosity to rz geometry, TEES will focus upon
preserving spherical symmetry.

Parallel Radiation Transport
The fundamental problem with transport scaling is that sweeps
are serial in one dimension (the direction of radiation
propagation). Texas A&M will investigate longcharacteristic
methods that yield a true 1D equation along each characteristic.
This is in contrast to standard Sn spatial discretizations that
couple cells laterally to the direction of flow. Reduced lateral
coupling eliminates a substantial portion of celltocell
dependencies and thus simplifies parallelization. In addition to
parallelism over the characteristic rays, substantial parallelism
is also possible for the solution process along each
characteristic, via "cyclic reduction" or "parallel prefix"
techniques. The basic idea of cyclic reduction is to locally
eliminate half of the unknowns in each of a series of reduction
steps. Eventually, the system becomes small enough to directly
solve, after which the entire solution is obtained via a succession
of backsubstitution steps that mirror the original reduction
steps. The application of cyclic reduction can produce logarithmic
or multigridtype scaling along the direction of flow, thereby
substantially improving upon the sequential scaling of current
algorithms in this direction.