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DOE Pulse
  • Number 349  |
  • October 31, 2011

INL's MOOSE drives nuclear materials, design innovation

The MOOSE simulation platform lets researchers "plug-and-play" by entering the mathematical model describing their system and letting MOOSE execute the simulation.

The MOOSE simulation platform
lets researchers "plug-and-play"
by entering the mathematical
model describing their system
and letting MOOSE execute the
simulation.

In nature, moose tend to be loners. But the one at DOE's Idaho National Laboratory is working with a bison, marmot, rat and others to make computer simulation more accessible and to foster new collaboration opportunities.

The beast achieving all this is the Multiphysics Object-Oriented Simulation Environment, or MOOSE. This computer simulation framework advances the process for predicting the behavior of complex systems ranging from irradiation effects on materials to groundwater physics and chemistry.

In short, MOOSE makes it easier to create computer simulations from complex mathematical models. INL models that have been simulated using MOOSE include:

  • BISON, which has applications for nuclear reactor designers;
  • MARMOT, which shows microscopic responses of nuclear fuel to irradiation;
  • RAT, which simulates chemicals reacting and flowing through bedrock; and
  • FALCON, which simulates water and heat flow in geothermal reservoirs.

MOOSE can even run two or more related models simultaneously to reveal new insights. Such simulations can help inform real-world experiments, and researchers no longer have to be computer science experts to tackle state-of-the-art simulation.

"People were doing these simulations before, but they had to develop the entire code," said Derek Gaston, the computational mathematician leading INL's Computational Frameworks Group. "Something that would take 5 years with a team of 10 people can now be done in 1 year with three people."

Maximizing simulation output

Modeling and simulation have become indispensible research tools in many branches of science. Nuclear engineers studying how irradiation affects materials can build a mathematical model incorporating what they already know about radiation and material behavior, and then use that model to perform a computer simulation that predicts outcomes under new conditions.

But building simulations is a time-consuming task requiring a team of people with detailed understanding of everything from parallel code development to the physics of the system under study. Most scientists are not programmers (and vice versa), so tackling simulation often proved daunting.

Now MOOSE can carry much of the programming burden, making simulation tools more accessible for a wide array of researchers. The MOOSE platform is a general problem solver that can accommodate many mathematical models. It essentially lets researchers "plug-and-play" by entering the mathematics describing their system — whether it's irradiation effects or groundwater movement — and letting MOOSE execute the simulation.

INL’s multiphysics methods group began developing the MOOSE framework by utilizing code and libraries from existing massively scaling numerical tools developed elsewhere in the Department of Energy complex and academia. The result is a framework with a number of high-level features including a hybrid parallel mode and mesh adaptivity.

Plus, researchers don't have to access a supercomputer because MOOSE can also function at personal workstations.

New pairings, increased teamwork

MOOSE is helping bring materials scientists and nuclear engineers closer by making it easy to merge their respective mathematical models into a single simulation. For example, MOOSE is able to run both BISON and MARMOT simultaneously to create a simulation that shows how microscopic radiation effects evolve into fuel or cladding failures at the macroscopic scale.

In the future, MOOSE, BISON, MARMOT and a herd of associated programs can enable materials scientists to work hand-in-hand with nuclear engineers to achieve new insights far more quickly than was possible in the past. — by Nicole Stricker

Submitted by DOE’s Idaho National Laboratory