A Hybrid High-Order Method for the Power-Law Brinkman Problem with Robust Error Estimates in all Regimes

Abstract. In this work, we propose and analyze a new hybrid high-order method for the Brinkman problem for fluids with power-law viscosity. The proposed method supports general meshes and arbitrary approximation orders and is robust in all regimes, from pure (power-law) Stokes to pure Darcy. Robustness is reflected by error estimates that distinguish the contributions from Stokes- and Darcy-dominated elements as identified by an appropriate dimensionless number and that additionally account for pre-asymptotic orders of convergence. Theoretical results are illustrated by a complete panel of numerical experiments.

Authors

Institutions

Publication Details

Journal
SIAM Journal on Numerical Analysis
Published
2026-08-24
DOI
https://doi.org/10.1137/25m1778389
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Hybrid High-Order Method for the Power-Law Brinkman Problem with Robust Error Estimates in all Regimes

Daniel Castañón Quiroz, Daniele A. Di Pietro, Jérôme Droniou, M. Ben Salah
SIAM Journal on Numerical Analysis
Probabilistic and Robust Engineering Design
article

A Hybrid High-Order Method for the Power-Law Brinkman Problem with Robust Error Estimates in all Regimes

Daniel Castañón Quiroz, Daniele A. Di Pietro, Jérôme Droniou, M. Ben Salah
article en

Abstract

Abstract. In this work, we propose and analyze a new hybrid high-order method for the Brinkman problem for fluids with power-law viscosity. The proposed method supports general meshes and arbitrary approximation orders and is robust in all regimes, from pure (power-law) Stokes to pure Darcy. Robustness is reflected by error estimates that distinguish the contributions from Stokes- and Darcy-dominated elements as identified by an appropriate dimensionless number and that additionally account for pre-asymptotic orders of convergence. Theoretical results are illustrated by a complete panel of numerical experiments.

SIAM Journal on Numerical AnalysisVol. 64(4)
Centre National de la Recherche Scientifique (FR), Université de Montpellier (FR), Universidad Nacional Autónoma de México (MX)
European Commission
Openalex Percentile: Top 99%
Probabilistic and Robust Engineering Design
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.