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Träfflista för sökning "WFRF:(Imadera K.) "

Search: WFRF:(Imadera K.)

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1.
  • Anderson, Johan, 1973, et al. (author)
  • Statistical description of turbulent transport for flux driven toroidal plasmas
  • 2017
  • In: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 57:6, s. art. no. 066013 -
  • Journal article (peer-reviewed)abstract
    • A novel methodology to analyze non-Gaussian probability distribution functions (PDFs) of intermittent turbulent transport in global full-f gyrokinetic simulations is presented. In this work, the auto-regressive integrated moving average (ARIMA) model is applied to time series data of intermittent turbulent heat transport to separate noise and oscillatory trends, allowing for the extraction of non-Gaussian features of the PDFs. It was shown that non-Gaussian tails of the PDFs from first principles based gyrokinetic simulations agree with an analytical estimation based on a two fluid model.
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2.
  • Anderson, Johan, 1973, et al. (author)
  • The Information Length Concept Applied to Plasma Turbulence
  • 2024
  • In: Entropy. - 1099-4300. ; 26:6
  • Journal article (peer-reviewed)abstract
    • A methodology to study statistical properties of anomalous transport in fusion plasma is investigated. Three time traces generated by the full-f gyrokinetic code GKNET are analyzed for this purpose. The time traces consist of heat flux as a function of the radial position, which is studied in a novel manner using statistical methods. The simulation data exhibit transport processes with both medium and long correlation length along the radius. A typical example of a phenomenon with long correlation length is avalanches. In order to investigate the evolution of the turbulent state, two basic configurations are studied, one flux-driven and one gradient-driven with decaying turbulence. The information length concept in tandem with Boltzmann–Gibbs and Tsallis entropy is used in the investigation. It is found that the dynamical states in both flux-driven and gradient-driven cases are surprisingly similar, but the Tsallis entropy reveals differences between them. This indicates that the types of probability distribution function are nevertheless quite different since the higher moments are significantly different.
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