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SCREAM: a performance-portable global cloud-resolving model based on the Energy Exascale Earth System Model

Hillman, Benjamin R.; Caldwell, Peter; Salinger, Andrew G.; Bertagna, Luca; Beydoun, Hassan; Peter, Bogenschutz; Bradley, Andrew M.; Donahue, Aaron; Eldred, Christopher; Foucar, James G.; Golaz, Chris; Guba, Oksana; Jacob, Robert; Johnson, Jeff; Keen, Noel; Krishna, Jayesh; Lin, Wuyin; Liu, Weiran; Pressel, Kyle; Singh, Balwinder; Steyer, Andrew J.; Taylor, Mark A.; Terai, Chris; Ullrich, Paul; Wu, Danqing; Yuan, Xingqui

Abstract not provided.

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TYPE Conference Poster YEAR 2020
OSTI

A linearity preserving nodal variation limiting algorithm for continuous Galerkin discretization of ideal MHD equations

Journal of Computational Physics

Mabuza, Sibusiso; Shadid, John N.; Cyr, Eric C.; Pawlowski, Roger; Kuzmin, Dmitri

In this work, a stabilized continuous Galerkin (CG) method for magnetohydrodynamics (MHD) is presented. Ideal, compressible inviscid MHD equations are discretized in space on unstructured meshes using piecewise linear or bilinear finite element bases to get a semi-discrete scheme. Stabilization is then introduced to the semi-discrete method in a strategy that follows the algebraic flux correction paradigm. This involves adding some artificial diffusion to the high order, semi-discrete method and mass lumping in the time derivative term. The result is a low order method that provides local extremum diminishing properties for hyperbolic systems. The difference between the low order method and the high order method is scaled element-wise using a limiter and added to the low order scheme. The limiter is solution dependent and computed via an iterative linearity preserving nodal variation limiting strategy. The stabilization also involves an optional consistent background high order dissipation that reduces phase errors. The resulting stabilized scheme is a semi-discrete method that can be applied to inviscid shock MHD problems and may be even extended to resistive and viscous MHD problems. To satisfy the divergence free constraint of the MHD equations, we add parabolic divergence cleaning to the system. Various time integration methods can be used to discretize the scheme in time. We demonstrate the robustness of the scheme by solving several shock MHD problems.

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TYPE Journal Article YEAR 2020
DOIOSTIScopus

SUSPECT: MINLP special structure detector for Pyomo

Optimization Letters

Ceccon, Francesco; Siirola, John D.; Misener, Ruth

We present SUSPECT, an open source toolkit that symbolically analyzes mixed-integer nonlinear optimization problems formulated using the Python algebraic modeling library Pyomo. We present the data structures and algorithms used to implement SUSPECT. SUSPECT works on a directed acyclic graph representation of the optimization problem to perform: bounds tightening, bound propagation, monotonicity detection, and convexity detection. We show how the tree-walking rules in SUSPECT balance the need for lightweight computation with effective special structure detection. SUSPECT can be used as a standalone tool or as a Python library to be integrated in other tools or solvers. We highlight the easy extensibility of SUSPECT with several recent convexity detection tricks from the literature. We also report experimental results on the MINLPLib 2 dataset.

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TYPE Journal Article YEAR 2020
DOIOSTIScopus
Results 1376–1400 of 9,998
Results 1376–1400 of 9,998