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Bifurcation of Symmetric Domain Walls for the Bénard–Rayleigh Convection Problem.

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Title: Bifurcation of Symmetric Domain Walls for the Bénard–Rayleigh Convection Problem.
Authors: Haragus, Mariana1 (AUTHOR) mharagus@univ-fcomte.fr, Iooss, Gérard2 (AUTHOR)
Source: Archive for Rational Mechanics & Analysis. 2021, Vol. 239 Issue 2, p733-781. 49p.
Subjects: Domain walls (String models), Symmetric domains
Abstract: We prove the existence of domain walls for the Bénard–Rayleigh convection problem. Our approach relies upon a spatial dynamics formulation of the hydrodynamic problem, a center manifold reduction, and a normal forms analysis of an eight-dimensional reduced system. Domain walls are constructed as heteroclinic solutions connecting suitably chosen periodic solutions of this reduced system. [ABSTRACT FROM AUTHOR]
Copyright of Archive for Rational Mechanics & Analysis is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: We prove the existence of domain walls for the Bénard–Rayleigh convection problem. Our approach relies upon a spatial dynamics formulation of the hydrodynamic problem, a center manifold reduction, and a normal forms analysis of an eight-dimensional reduced system. Domain walls are constructed as heteroclinic solutions connecting suitably chosen periodic solutions of this reduced system. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Archive for Rational Mechanics & Analysis is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00205-020-01584-6
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