Non-unique self-similar blowups in shell models: insights from dynamical systems and machine-learning
Ciro Campolina, Eric Simonnet, Simon Thalabard

TL;DR
This paper investigates the self-similar blowup phenomena in the Sabra shell model of turbulence, combining bifurcation analysis and machine learning to reveal multiple non-universal blowup profiles and deepen understanding of turbulence scaling behaviors.
Contribution
It introduces two novel strategies—bifurcation analysis and machine learning—to analyze and characterize non-unique self-similar blowups in shell models, advancing the understanding of turbulence singularities.
Findings
Identification of homoclinic bifurcations converging to Sabra solutions
Discovery of a continuous family of non-universal blowup profiles
Machine learning reveals multiple blowup exponents and pulse structures
Abstract
Strong numerical hints exist in favor of a universal blowup scenario in the Sabra shell model, a popular cascade model of 3D turbulence, which features complex velocity variables on a geometric progression of scales . The blowup is thought to be of self-similar type and characterized by the finite-time convergence towards a universal profile with non-Kolmogorov (anomalous) small-scale scaling . Solving the underlying nonlinear eigenvalue problem has however proven challenging, and prior insights mainly used the Dombre-Gilson renormalization scheme, transforming self-similar solutions into solitons propagating over infinite rescaled time horizon. Here, we further characterize Sabra blowups by implementing two strategies targeting the eigenvalue problem. The first involves formal expansion in terms of the bookkeeping parameter $\delta =…
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Taxonomy
TopicsComputational Physics and Python Applications
