2019
DOI: 10.1063/1.5081031
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On reversals in 2D turbulent Rayleigh-Bénard convection: Insights from embedding theory and comparison with proper orthogonal decomposition analysis

Abstract: Turbulent Rayleigh-Bénard convection in a 2D square cell is characterized by the existence of a large-scale circulation which varies intermittently. We focus on a range of Rayleigh numbers where the large-scale circulation experiences rapid non-trivial reversals from one quasi-steady (or metastable) state to another. In previous work (Podvin and Sergent JFM 2015, Podvin and Sergent PRE 2017), we applied Proper Orthogonal Decomposition (POD) to the joint temperature and velocity elds at a given Rayleigh number … Show more

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Cited by 2 publications
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“…Decomposing the turbulent flow into coherent flow structures and incoherent turbulence, allows us to focus on dynamically significant events (Hussain 1986). Such decomposition is usually performed either in terms of predetermined basis functions, like spatial Fourier decomposition (see, for instance, Das, Ghosal & Kumar 2000, Chandra & Verma 2011), or in terms of basis functions extracted from the data, like proper orthogonal decomposition (POD) (see, for instance Bailon-Cuba, Emran & Schumacher 2010, Podvin & Sergent 2015, 2017, Faranda, Podvin & Sergent 2019), dynamic mode decomposition (DMD) (Schmid 2010; Horn & Schmid 2017) or Koopman eigenfunction analysis (Giannakis et al. 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Decomposing the turbulent flow into coherent flow structures and incoherent turbulence, allows us to focus on dynamically significant events (Hussain 1986). Such decomposition is usually performed either in terms of predetermined basis functions, like spatial Fourier decomposition (see, for instance, Das, Ghosal & Kumar 2000, Chandra & Verma 2011), or in terms of basis functions extracted from the data, like proper orthogonal decomposition (POD) (see, for instance Bailon-Cuba, Emran & Schumacher 2010, Podvin & Sergent 2015, 2017, Faranda, Podvin & Sergent 2019), dynamic mode decomposition (DMD) (Schmid 2010; Horn & Schmid 2017) or Koopman eigenfunction analysis (Giannakis et al. 2018).…”
Section: Introductionmentioning
confidence: 99%