Multiscale high-order/low-order (HOLO) algorithms and applications

Chacon, Luis; Chen, Guangye; Knoll, Dana Alan; Newman, Christopher Kyle; Park, HyeongKae; Taitano, William; Willert, Jeff A.; Womeldorff, Geoffrey Alan

doi:10.1016/j.jcp.2016.10.069

Multiscale high-order/low-order (HOLO) algorithms and applications

Journal Article · Fri Nov 11 00:00:00 EST 2016 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2016.10.069· OSTI ID:1375167

^[1]; ^[1]; Knoll, Dana Alan ^[1]; Newman, Christopher Kyle ^[1]; ^[1]; ^[1]; Willert, Jeff A. ^[2]; ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Institute for Defense Analyses, Alexandria, VA (United States)

Here, we review the state of the art in the formulation, implementation, and performance of so-called high-order/low-order (HOLO) algorithms for challenging multiscale problems. HOLO algorithms attempt to couple one or several high-complexity physical models (the high-order model, HO) with low-complexity ones (the low-order model, LO). The primary goal of HOLO algorithms is to achieve nonlinear convergence between HO and LO components while minimizing memory footprint and managing the computational complexity in a practical manner. Key to the HOLO approach is the use of the LO representations to address temporal stiffness, effectively accelerating the convergence of the HO/LO coupled system. The HOLO approach is broadly underpinned by the concept of nonlinear elimination, which enables segregation of the HO and LO components in ways that can effectively use heterogeneous architectures. The accuracy and efficiency benefits of HOLO algorithms are demonstrated with specific applications to radiation transport, gas dynamics, plasmas (both Eulerian and Lagrangian formulations), and ocean modeling. Across this broad application spectrum, HOLO algorithms achieve significant accuracy improvements at a fraction of the cost compared to conventional approaches. It follows that HOLO algorithms hold significant potential for high-fidelity system scale multiscale simulations leveraging exascale computing.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1375167

Alternate ID(s):: OSTI ID: 22622241
OSTI ID: 1397820

Report Number(s):: LA-UR--16-23721

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Journal Issue: C Vol. 330; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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