MOOSE (software)
The MOOSE GUI Peacock.  
Original author(s)  Derek Gaston 

Developer(s)  Idaho National Laboratory and contributors. 
Initial release  June 10, 2008 
Stable release 
continuous release

Development status  Active 
Operating system  Linux, Mac OS X, Unix 
Available in  C++ 
Type  Finite Element Analysis Simulator 
License  LGPL 
Website 
mooseframework 
MOOSE (Multiphysics Object Oriented Simulation Environment) is an objectoriented C++ finite element framework for the development of tightly coupled multiphysics solvers from Idaho National Laboratory.^{[1]} MOOSE makes use of the PETSc nonlinear solver package and libmesh to provide the finite element discretization.
A key design aspect of MOOSE is the decomposition of weak form residual equations into separate terms that are each represented by compute kernels. The combination of these kernels into complete residuals describing the problem to be solved is performed at run time. This allows modifications such as toggling of mechanisms and the addition of new physics without recompilation. MOOSE comes with an extensive library of kernels providing residual terms for solid mechanics, Navier–Stokes equations, phase field models and more.
MOOSE uses VTK.
Background
The development of MOOSE at Idaho National Laboratory (INL), beginning in May 2008, has resulted in a unique approach to computational engineering that combines computer science with a strong underlying mathematical description in a unique way that allows scientists and engineers to develop engineering simulation tools in a fraction of the time previously required.^{[2]} The heart of MOOSE is the Kernel. A Kernel is a “piece” of physics. To add new physics to an application built using MOOSE, all that is required is to supply a new Kernel that describes the discrete form of the equation. It’s usually convenient to think of a Kernel as a mathematical operator, such as a Laplacian or a convection term in a partial differential equation (PDE). Kernels may be swapped or coupled together to achieve different application goals. These Kernels, which now number in the hundreds, allow a scientist or engineer to develop an application rapidly.
For a new application, existing Kernels are selected asis, or modified as necessary, and “plugged” in. An advectiondiffusionreaction equation is of the same mathematical form no matter what application it is being used for. Typically, only the form of the coefficients or the dependencies on other physics need to be defined; rarely must complete Kernels be constructed from scratch. With MOOSE, only the Kernel development is required from the scientist or engineer (application developer). MOOSE is designed to do everything else for the application developer, such as finite element discretization of the PDEs, the nonlinear solver, and the parallel high performance computing.
The idea of naming MOOSEbased applications after Idaho indigenous animal species is loosely based on Los Alamos National Laboratory's 1970s – 1990s approach to naming their codes after Native American tribes and artifacts, such as APACHE, CONCHAS, and the KIVA series of codes. There are now over twenty MOOSEbased application animals in various stages of development, ranging from recently obtaining preliminary results to being nationally recognized as stateoftheart efforts (such as BISON, PEREGRINE, and MARMOT for fuels performance modeling & simulation activities).
Description
MOOSE is a development and runtime environment for the solution of multiphysics systems that involve multiple physical models or multiple simultaneous physical phenomena. The systems are generally represented (modeled) as a system of fully coupled nonlinear partial differential equation systems (an example of a multiphysics system is the thermal feedback effect upon neutronics crosssections where the crosssections are a function of the heat transfer). Inside MOOSE, the JacobianFree Newton Krylov (JFNK) method is implemented as a parallel nonlinear solver that naturally supports effective coupling between physics equation systems (or Kernels).^{[3]} The physics Kernels are designed to contribute to the nonlinear residual, which is then minimized inside of MOOSE. MOOSE provides a comprehensive set of finite element support capabilities (libMesh) and provides for mesh adaptation and parallel execution. The framework heavily leverages software libraries from the Department of Energy (DOE) and the National Nuclear Security Administration (NNSA), such as the nonlinear solver capabilities in either the Portable, Extensible Toolkit for Scientific Computation (PETSc) project or the Trilinos project.
ELK (Extended Library of Kernels)
ELK is a library for common Kernels, boundary conditions and material base classes.^{[4]}
YAK (Yet Another Kernel)
YAK is a library of common actions, Kernels, boundary conditions, and material base classes for radiation transport applications. YAK is currently linked with RattleSnake (multigroup radiation Sn transport), Pronghorn (multigroup diffusion), and Critter (multilength scale temperature feedback).
Applications
BISON
BISON was the first MOOSEbased application "animal," and is a finite elementbased nuclear fuel performance code applicable to a variety of fuel forms including light water reactor fuel rods, TRISO fuel particles, and metallic rod and plate fuel.^{[5]}^{[6]}^{[7]} It solves the fully coupled equations of thermomechanics and species diffusion and includes important fuel physics such as fission gas release and material property degradation with burnup. BISON is based on the MOOSE framework^{[8]} and can therefore efficiently solve problems on either twodimensional axisymmetric or threedimensional geometries using standard workstations or large high performance computers. Plasticity, irradiation growth, and thermal and irradiation creep models are implemented for clad materials. Models are also available to simulate gap heat transfer, mechanical contact, and the evolution of the gap/plenum pressure with plenum volume, gas temperature, and fission gas addition. BISON is also coupled to a MOOSEbased mesoscale phase field material property simulation capability.^{[9]}^{[10]}
MARMOT
MARMOT is a finite elementbased phase field code for modeling irradiationinduced microstructure evolution. MARMOT predicts the effect of radiation damage on microstructure evolution, including void nucleation and growth, bubble growth, grain boundary migration, and gas diffusion and segregation. The phase field equations can be coupled with heat conduction and solid mechanics from ELK to consider the effect of temperature and stress gradients on the evolution. In addition, MARMOT calculates the effect of the microstructure evolution on various bulk material properties, including thermal conductivity and porosity. Once the bulk properties have been calculated, they can be passed to BISON for a fuel performance simulation. This coupling between MARMOT and BISON has been achieved in the hybrid code BARMOT.^{[11]}^{[12]}^{[13]}
FALCON
FALCON is being developed to enable simulation of the tightly coupled fluidrock behavior in hydrothermal and engineered geothermal system (EGS) reservoirs, targeting the dynamics of fracture stimulation, fluid flow, rock deformation, and heat transport in a single integrated code, with the ultimate goal of providing a tool that can be used to test the viability of EGS in the United States and worldwide.^{[14]}^{[15]}^{[16]} Reliable reservoir performance predictions of EGS systems require accurate and robust modeling for the coupled thermalhydrologicalmechanical processes. Conventionally, these types of problems are solved using operator splitting methods, usually by coupling a subsurface flow and heat transport simulator with a solid mechanics simulator via input files.^{[17]}^{[18]}^{[19]}^{[20]} FALCON eliminates the need for using operator splitting methods to simulate these systems, and the scalability of MOOSE supported applications allows for simulating these tightly coupled processes at the reservoir scale, allowing for examination of the system as a whole (something the operator splitting methodologies generally cannot do).^{[21]}^{[22]}
RAT
ReActive Transport (RAT) has been developed to solve reactive transport problems in subsurface porous media that involves highly nonlinearly coupled physical processes of fluid flow, solute transport, biogeochemical reactions and mediasolution interactions. These problems are common in various subsurfaceengineered systems, such as engineered environmental remediation, enhanced geothermal systems and carbon dioxide geological sequestration. Currently, the physics that could be coupled in RAT include: singlephase fluid flow in porous media, advection, dispersion and diffusion transport, aqueous kinetic reaction, aqueous equilibrium reaction, kinetic mineral precipitation/dissolution reaction, and CarmenKozeny porositypermeability relationship.^{[23]}^{[24]}
RELAP7
RELAP7 is the nextgeneration tool in the RELAP safety/systems analysis application series and is based upon the MOOSE development and runtime environment framework.^{[25]}^{[26]} RELAP7 will retain and improve the basic analysis capability of RELAP5. The four major improvements are 1) A wellposed sevenequation twophase flow model (liquid, gas, and interface pressures) versus the obsolete illposed sixequation flow model (nonphysical mixture sound speed) found in RELAP5; 2) Improved numerical approximations resulting in secondorder accuracy in both space and time versus the first order approximations in RELAP5; 3) Implicit tightly coupled time integration for long duration transients, such as providing plant behavior for full life fuel cycle evaluations; and 4) the ability to easily couple to multidimensional core simulators being developed in other programs (NEAMS, CASL, ATR LEP).^{[27]}^{[28]}^{[29]}
Pronghorn
Pronghorn was originally developed for simulation of the gascooled pebblebed VHTR concept. The current capabilities of Pronghorn include transient and steady coupled porous fluid flow and solidstate heat conduction with a standard multigroup diffusion model (i.e., fixedsource, criticality, and timedependent).^{[30]} Recently added capabilities include a nonlinear acceleration scheme for criticality problems and a simple thermalfluid model for the prismatic reactor concept. Future capabilities will include a more advanced multiphase flow type of model (to study thermal boundary layer effects) and a radiation transport model. The physics can be solved in threedimensional Cartesian (x, y, z) or cylindrical (r, q , z) space, with precursor and adiabatic thermal feedback models. This code has been validated against the PBMR400 benchmark problem. Using Pronghorn, rod ejection simulations have been conducted for thermalfluids/neutronics for both pebblebed and prismatic gascooled reactors and simple thermalneutronics coupled LWR benchmark problems.^{[31]}^{[32]}
Features
 Fully coupled, fully implicit multiphysics solver
 Dimension independent physics
 Automatically parallel (largest runs >100,000 CPU cores)
 Modular development simplifies code reuse
 Builtin mesh adaptivity
 Continuous and Discontinuous Galerkin (DG) (at the same time)
 Intuitive parallel multiscale solves (see videos below)
 Dimension agnostic, parallel geometric search (for contact related applications)
 Flexible, plugable graphical user interface
 ~30 plugable interfaces allow specialization of every part of the solve
Notes
 ↑ "MOOSE Framework  Open Source Multiphysics". Idaho National Laboratory. Retrieved 20130328.
 ↑
 ↑
 ↑ "MOOSE instructions  EVOWiki". Icme.hpc.msstate.edu. 20120417. Retrieved 20121211.
 ↑ "Journal of Nuclear Materials  Multidimensional multiphysics simulation of nuclear fuel behavior". Journal of Nuclear Materials. 423: 149–163. 20120119. doi:10.1016/j.jnucmat.2012.01.012. Retrieved 20121211.
 ↑
 ↑ "Journal of Nuclear Materials  Three dimensional coupled simulation of thermomechanics, heat, and oxygen diffusion in UO2 nuclear fuel rods". Journal of Nuclear Materials. 392: 6–15. doi:10.1016/j.jnucmat.2009.03.035. Retrieved 20121211.
 ↑ "Nuclear Engineering and Design  MOOSE: A parallel computational framework for coupled systems of nonlinear equations". Nuclear Engineering and Design. 239: 1768–1778. 20090719. doi:10.1016/j.nucengdes.2009.05.021. Retrieved 20121211.
 ↑ "Computational Materials Science  An objectoriented finite element framework for multiphysics phase field simulations". Computational Materials Science. 51: 20–29. 20110828. doi:10.1016/j.commatsci.2011.07.028. Retrieved 20121211.
 ↑ "Nuclear Engineering and Design  A coupling methodology for mesoscaleinformed nuclear fuel performance codes". Nuclear Engineering and Design. 240: 2877–2883. 20100703. doi:10.1016/j.nucengdes.2010.06.005. Retrieved 20121211.
 ↑ Tonks MR, Gaston D, Millett PC, Andrs D, Talbot P. 2012. An objectoriented finite element framework for multiphysics phase field simulations. Computational Materials Science 51:20–29.
 ↑ K Chockalingam, MR Tonks, JD Hales, DR Gaston, PC Millett, L Zhang. 2012. Crystal plasticity with JacobianFree Newton–Krylov. Computational Mechanics 01787675:1–10.
 ↑ L Zhang, MR Tonks, PC Millett, Y Zhang, K Chockalingam, B Biner. 2012. Phasefield modeling of temperature gradient driven pore migration coupling with thermal conduction. Computational Materials Science 56:161–165.
 ↑ "ESTSC  Find the latest in U.S. Department of Energy Software". Osti.gov. Retrieved 20121211.
 ↑ Podgorney, R.K, H. Huang, and D. Gaston, 2010, Massively Parallel Fully Coupled Modeling of Coupled ThermalHydroMechanical Processes for Enhanced Geothermal System Reservoirs, 35th Stanford Geothermal Workshop, Stanford, CA, February 2010
 ↑ Gaston, D., L. Guo, H. Huang, R. Johnson, H. Park, R. Podgorney, M. Tonks, and R, Williamson. 2010. Parallel algorithms and software for nuclear, energy, and environmental applications Part I: Multiphysics Algorithms, Communications in Computational Physics
 ↑ Gaston, D., L. Guo, H. Huang, R. Johnson, H. Park, R. Podgorney, M. Tonks, and R, Williamson. 2010. Parallel algorithms and software for nuclear, energy, and environmental applications Part II: Multiphysics Software, Communications in Computational Physics
 ↑ Podgorney, R.K, G. Gunnarsson, and H. Huang, 2011, Numerical Simulation of temperature dependent fluid reinjection behavior, Hellisheidi geothermal field, Southwest Iceland, Geothermal Resources Council 35th Annual Meeting, San Diego, CA, October 23–26, 2011
 ↑ Huang, H., P. Meakin, Podgorney, R.K, S. Deng, and C. Lu. 2011, Physicsbased modeling of fracturing and permeability evolution in engineered geothermal systems, Geothermal Resources Council 35th Annual Meeting, San Diego, CA, October 23–26, 2011
 ↑ Podgorney, R.K, C. Lu, and H. Huang. 2012, Thermohydromechncial modeling of working fluid injection and thermal energy extraction in EGS fractures and rock matrix, 37th Stanford Geothermal Workshop, Stanford, CA, February 2012.
 ↑ Podgorney, R.K, H. Huang, and D. Gaston, 2010, FALCON: A Hybrid Finite ElementDiscrete Element Physics Based Model for Simultaneously Solving Fully Coupled Multiphase Fluid Flow, Heat Transport, Rock Deformation, and Fracturing, Geothermal Resources Council 34th Annual Meeting, Sacramento, CA, October 24–27, 2010
 ↑ Podgorney, R.K, H. Huang, and D. Gaston, 2010, PhysicsBased Modeling for Coupled ThermoHydroMechanical Problems in Geologic Media: An Application for Enhanced Geothermal System, Geological Society of America Annual Meeting, Denver CO, Oct 31Nov. 3, 2010
 ↑
 ↑ "Reactive Transport Modeling of Induced Calcite Precipitation Reaction Fronts in". Adsabs.harvard.edu. Retrieved 20121211.
 ↑ "NEAMS Update" (PDF). Ipd.anl.gov. April 2012. Retrieved 20160927.
 ↑
 ↑ "NEAMS Update" (PDF). Ipd.anl.gov. January 2012. Retrieved 20160927.
 ↑
 ↑ "Microsoft Word  Demonstration of a Steady State PWR Problem with RELAP7 r3.docx" (PDF). Inl.gov. Retrieved 20160927.
 ↑
 ↑ H. Park , D. Gaston , S. Kadiouglu, D. Knoll, D. LebrunGrandie , R. Martineau and W. Taitano", "Tightly Coupled Multiphysics Simulation for Pebble Bed Reactors," American Nuclear Society 2009 International Conference on Advances in Mathematics, Computational Methods, and Reactor Physics, Saratoga Springs, NY, May 3–7, 2009.
 ↑
References
 Tonks, M.; Gaston, D.; Millett, P.; Andrs, D.; Talbot, P. (2012). "An objectoriented finite element framework for multiphysics phase field simulations". Comp. Mat. Sci. 51 (1): 20–29. doi:10.1016/j.commatsci.2011.07.028.
 Williamson, R.; Hales, J.; Novascone, S.; Tonks, M.; Gaston, D.; Permann, C.; Andrs, D.; Martineau, R. "Multidimensional Multiphysics Simulation of Nuclear Fuel Behavior". Journal of Nuclear Materials. 423 (149–163): 2012. doi:10.1016/j.jnucmat.2012.01.012.
 L. Guo, H. Huang, D. Gaston, and G. Redden. "Modeling of calcite precipitation driven by bacteriafacilitated urea hydrolysis in a flow column using a fully coupled, fully implicit parallel reactive transport simulator. In Eos Transactions American Geophysical Union, 90(52), Fall Meeting Supplement, AGU 90(52), San Francisco, CA, Dec 1418 2009.
 R. Podgorney, H. Huang, and D. Gaston. Massively parallel fully coupled implicit modeling of coupled thermalhydrologicalmechanical processes for enhanced geothermal system reservoirs. In Proceedings, 35th Stanford Geothermal Workshop, Stanford University, Palo Alto, CA, Feb 13 2010.
 H. Park, D. Knoll, D. Gaston, and R. Martineau. "Tightly Coupled Multiphysics Algorithms for Pebble Bed Reactors. Nuclear Science and Engineering, 166(2):118133,
External links
 MOOSE Homepage
 Idaho National Laboratory's Technology Deployment Office
 moose on GitHub
 MOOSE publications  List of publications on the MOOSE framework.