Convection in a Rapidly Rotating Spherical Shell: Newton’s Method Using Implicit Coriolis Integration

Geophysical flows are characterized by rapid rotation. Simulating these flows requires small timesteps to achieve stability and accuracy. Numerical stability can be greatly improved by the implicit integration of the terms that are most responsible for destabilizing the numerical scheme. We have implemented an implicit treatment of the Coriolis force in a rotating spherical shell driven by a radial thermal gradient. We modified the resulting timestepping code to carry out steady-state solving via Newton’s method, which has no timestepping error. The implicit terms have the effect of preconditioning the linear systems, which can then be rapidly solved by a matrix-free Krylov method. We computed the branches of rotating waves with azimuthal wavenumbers ranging from 4 to 12. As the Ekman number (the non-dimensionalized inverse rotation rate) decreases, the flows are increasingly axially independent and localized near the inner cylinder, in keeping with well-known theoretical predictions and previous experimental and numerical results. The advantage of the implicit over the explicit treatment also increases dramatically with decreasing Ek, reducing the cost of computation by as much as a factor of 20 for Ekman numbers of order of 10−5. We carried out continuation for both the Rayleigh and Ekman numbers and obtained interesting branches in which the drift velocity remained unchanged between pairs of saddle–node bifurcations.

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Journal
Mathematics
Published
2025-06-27
DOI
https://doi.org/10.3390/math13132113
Primary Topic
Tribology and Lubrication Engineering
Type
article
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article

Convection in a Rapidly Rotating Spherical Shell: Newton’s Method Using Implicit Coriolis Integration

F. Feudel, Laurette S. Tuckerman, Juan Cruz Gonzalez Sembla, Camille Rambert
Mathematics
Tribology and Lubrication Engineering
article

Convection in a Rapidly Rotating Spherical Shell: Newton’s Method Using Implicit Coriolis Integration

F. Feudel, Laurette S. Tuckerman, Juan Cruz Gonzalez Sembla, Camille Rambert
article en

Abstract

Geophysical flows are characterized by rapid rotation. Simulating these flows requires small timesteps to achieve stability and accuracy. Numerical stability can be greatly improved by the implicit integration of the terms that are most responsible for destabilizing the numerical scheme. We have implemented an implicit treatment of the Coriolis force in a rotating spherical shell driven by a radial thermal gradient. We modified the resulting timestepping code to carry out steady-state solving via Newton’s method, which has no timestepping error. The implicit terms have the effect of preconditioning the linear systems, which can then be rapidly solved by a matrix-free Krylov method. We computed the branches of rotating waves with azimuthal wavenumbers ranging from 4 to 12. As the Ekman number (the non-dimensionalized inverse rotation rate) decreases, the flows are increasingly axially independent and localized near the inner cylinder, in keeping with well-known theoretical predictions and previous experimental and numerical results. The advantage of the implicit over the explicit treatment also increases dramatically with decreasing Ek, reducing the cost of computation by as much as a factor of 20 for Ekman numbers of order of 10−5. We carried out continuation for both the Rayleigh and Ekman numbers and obtained interesting branches in which the drift velocity remained unchanged between pairs of saddle–node bifurcations.

MathematicsVol. 13(13)
Centre National de la Recherche Scientifique (FR), University of Potsdam (DE), Université Paris Cité (FR), Université Paris Sciences et Lettres (FR), Sorbonne Université (FR), Physique et Mécanique des Milieux Hétérogènes (FR), ESPCI Paris (FR)
Openalex Percentile: Top 85%
Tribology and Lubrication Engineering
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Convection in a Rapidly Rotating Spherical Shell: Newton’s Method Using Implicit Coriolis Integration — F. Feudel, Laurette S. Tuckerman, et al. · Mathematics (2025) | TGRS Research Map | TGRS