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SUMMARY:Stochastic Dynamical Surrogates for Thermal Convection.
DTSTART:20260511T133000Z
DTEND:20260511T153000Z
DTSTAMP:20260517T012800Z
UID:indico-event-9697@indico.fysik.su.se
DESCRIPTION:Speakers: Yanni Bills (University of Yale)\n\nThe Lorenz equat
 ions are a severe Galerkin-truncation of the Oberbeck-Boussinesq equations
  describing Rayleigh-Bénard convection (RBC)\, or the buoyancy-driven flo
 w between parallel isothermal plates. Here\, we use the chaotic lobe-switc
 hing behavior of the stochastic Lorenz equations as an analogue of the mea
 n wind reversals in the experiments of Sreenivasan et al. (Phys Rev E 65\,
  056306\, 2002). The boundary layers are central to the transport in RBC\,
  and we use the stochastic Lorenz system to model the associated small-sca
 le turbulence embedded within a large-scale convective roll. Forcing the Z
 -equation with additive Gaussian white noise (GWN) generates ergodic invar
 iant measures\, for all parameters\, while transparently manipulating the 
 boundary layers. Long-time numerical simulations yield a probability distr
 ibution for lobe inter-switch timings that exhibits multifractal behavior.
  Filtering that signal to frequencies with Brownian power spectral density
  roll-off creates a Gaussian distribution\, mirroring laboratory measureme
 nts. Multifractal analysis reveals a bent mass-scaling exponent spectrum a
 nd non-linear generalized dimensions\, while the classical Hurst exponent 
 gives Brownian second-moment statistics. A simple Cantor-cascade reproduce
 s these values\, showing that multiplicative intermittency strongly influe
 nces the statistics. This demonstrates that a GWN-forced Lorenz system is 
 a faithful\, low-dimensional surrogate for mean-wind reversals in RBC.\n\n
 https://indico.fysik.su.se/event/9697/
LOCATION:Albano 3: 6228 - Mega (22 seats) (Albano Building 3)
URL:https://indico.fysik.su.se/event/9697/
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