Far-from-equilibrium attractors and nonlinear dynamical systems approach to the Gubser flow
Auteurs : Alireza Behtash, C. N. Cruz-Camacho, M. Martinez
Résumé : The dynamical non-equilibrium attractors of systems undergoing Gubser flow within relativistic kinetic theory are studied. In doing so we employ well-established methods of nonlinear dynamical systems which rely on finding the fixed points, investigating the structure of the flow diagrams of the evolution equations, and characterizing the basin of attraction using a Lyapunov function near the stable fixed points. We obtain the dynamical attractors of anisotropic hydrodynamics, Israel-Stewart (IS) and transient fluid (DNMR) theories. For the Gubser flow we show that attractors for all the hydrodynamical theories are non-planar and the basin of attraction is essentially three dimensional. The attractors of each hydrodynamical model are compared with the one obtained from the exact Gubser solution of the Boltzmann equation within the relaxation time approximation. We observe that anisotropic hydrodynamics is able to match up to high numerical accuracy the non-equilibrium attractor of the exact solution while the second order hydrodynamical theories fail to describe it. We show that the IS and DNMR asymptotic series expansion diverge and use resurgence techniques to perform the resummation of these divergences. We also comment on a possible link between manifold of steepest descent paths in path integrals and basin of attraction for the attractors via Lyapunov functions that opens a new horizon toward effective field theory description of hydrodynamics. Our findings indicate that anisotropic hydrodynamics is an effective theory for far-from-equilibrium fluid dynamics which resums the Knudsen and inverse Reynolds numbers to all orders.
Explorez l'arbre d'article
Cliquez sur les nœuds de l'arborescence pour être redirigé vers un article donné et accéder à leurs résumés et assistant virtuel
Recherchez des articles similaires (en version bêta)
En cliquant sur le bouton ci-dessus, notre algorithme analysera tous les articles de notre base de données pour trouver le plus proche en fonction du contenu des articles complets et pas seulement des métadonnées. Veuillez noter que cela ne fonctionne que pour les articles pour lesquels nous avons généré des résumés et que vous pouvez le réexécuter de temps en temps pour obtenir un résultat plus précis pendant que notre base de données s'agrandit.