Rigorous justification of low-magnetic-Reynolds-number approximation for MHD duct flows

The low-magnetic-Reynolds-number approximation has been widely used in mathematical modeling of conducting fluids under the influence of external magnetic fields. The aim of this paper is to justify this approximation using a rigorous analysis in the appropriate functional setting. We derive absolute and relative error estimates for both three-dimensional magnetohydrodynamic duct flow and two-dimensional shallow-channel flow. The derived relative error estimate vanishes as the magnetic Reynolds number tends to zero, thereby validating the approximation and also providing its order of accuracy and the range of applicability.

Authors

Publication Details

Journal
Quarterly of Applied Mathematics
Published
2026-05-19
DOI
https://doi.org/10.1090/qam/1745
Primary Topic
Nanofluid Flow and Heat Transfer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rigorous justification of low-magnetic-Reynolds-number approximation for MHD duct flows

Igor Pažanin, Filip Vučić
Quarterly of Applied Mathematics
Nanofluid Flow and Heat Transfer
article

Rigorous justification of low-magnetic-Reynolds-number approximation for MHD duct flows

Igor Pažanin, Filip Vučić
article en

Abstract

The low-magnetic-Reynolds-number approximation has been widely used in mathematical modeling of conducting fluids under the influence of external magnetic fields. The aim of this paper is to justify this approximation using a rigorous analysis in the appropriate functional setting. We derive absolute and relative error estimates for both three-dimensional magnetohydrodynamic duct flow and two-dimensional shallow-channel flow. The derived relative error estimate vanishes as the magnetic Reynolds number tends to zero, thereby validating the approximation and also providing its order of accuracy and the range of applicability.

Quarterly of Applied Mathematics
Openalex Percentile: Top 8%
Nanofluid Flow and Heat Transfer
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.