MOOSE: Enabling Massively Parallel Multiphysics Simulation.
Cody J. Permann,Derek Gaston,David Andrs,Robert W. Carlsen,Fande Kong,Alexander Lindsay,Jason M. Miller,John W. Peterson,Andrew E. Slaughter,Roy H. Stogner,Richard C. Martineau +10 more
TL;DR: The Multiphysics Object Oriented Simulation Environment (MOOSE) aims to enable development by providing simplified interfaces for specification of partial differential equations, boundary conditions, material properties, and all aspects of a simulation without the need to consider the parallel, adaptive, nonlinear, finite-element solve that is handled internally.
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Abstract: Harnessing modern parallel computing resources to achieve complex multi-physics simulations is a daunting task. The Multiphysics Object Oriented Simulation Environment (MOOSE) aims to enable such development by providing simplified interfaces for specification of partial differential equations, boundary conditions, material properties, and all aspects of a simulation without the need to consider the parallel, adaptive, nonlinear, finite-element solve that is handled internally. Through the use of interfaces and inheritance, each portion of a simulation becomes reusable and composable in a manner that allows disparate research groups to share code and create an ecosystem of growing capability that lowers the barrier for the creation of multiphysics simulation codes. Included within the framework is a unique capability for building multiscale, multiphysics simulations through simultaneous execution of multiple sub-applications with data transfers between the scales. Other capabilities include automatic differentiation, scaling to a large number of processors, hybrid parallelism, and mesh adaptivity. To date, MOOSE-based applications have been created in areas of science and engineering such as nuclear physics, geothermal science, magneto-hydrodynamics, seismic events, compressible and incompressible fluid flow, microstructure evolution, and advanced manufacturing processes.
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Figures
Table 1: Code metadata (mandatory) 
Figure 1: Scaling study of MOOSE using up to 32,768 processor cores. Each data point is annotated with the total compute time and the parallel efficiency. The total computed time does not include the input/ouptut cost and the mesh preparation. 
Figure 2: Results after 58 seconds of simulation for a thermo-mechanical problem with feedback from a micro-structure calculation that shows the temperature at the engineering scale, the geometry of each degraded micro-structure, and the effective thermal conductivity over time for each of the micro-structure calculations.
Citations
Parametric Dynamic Mode Decomposition for Reduced Order Modeling
TL;DR: In this article , a parametric DMD approach based on the interpolation of the reduced-order DMD eigen-pair and the reduced DMD (Koopman) operator is presented.
Recovery efficiency in high-temperature aquifer thermal energy storage systems
TL;DR: In this paper, the authors used a convolutional neural network (CNN) to estimate the recovery efficiency of a single-well aquifer thermal energy storage (ATES) system with injection temperatures as high as 300 ∘ C.
28
A perspective on applied geochemistry in porous media: Reactive transport modeling of geochemical dynamics and the interplay with flow phenomena and physical alteration
Hang Deng,Mehdi Gharasoo,Liwei Zhang,Zhenxue Dai,A. Hajizadeh,Catherine A. Peters,Cyprien Soulaine,Martin Thullner,Philippe Van Cappellen +8 more
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Modeling mesoscale fission gas behavior in UO2 by directly coupling the phase field method to spatially resolved cluster dynamics
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Materials for Sustainable Nuclear Energy: A European Strategic Research and Innovation Agenda for All Reactor Generations
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TL;DR: In this article , a research agenda that combines modern digital technologies with materials science practices is proposed to pursue a change of paradigm that promotes innovation, equally serving the different nuclear energy interests and positions throughout Europe.
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