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Taraneh Sayadi

Researcher at University of Paris

Publications -  44
Citations -  648

Taraneh Sayadi is an academic researcher from University of Paris. The author has contributed to research in topics: Boundary layer & Turbulence. The author has an hindex of 10, co-authored 37 publications receiving 508 citations. Previous affiliations of Taraneh Sayadi include Imperial College London & École Polytechnique.

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Direct numerical simulation of complete H-type and K-type transitions with implications for the dynamics of turbulent boundary layers

TL;DR: In this article, the onset and development of turbulence from controlled disturbances in compressible ( ), flat-plate boundary layers is studied by direct numerical simulation, and it is shown that H- and K-type breakdowns both relax toward the same statistical structure typical of developed turbulence at high Reynolds number immediately after the skin-friction maximum.
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Reduced-order representation of near-wall structures in the late transitional boundary layer

TL;DR: In this article, the skin-friction profiles of controlled H- and K-type transitions to turbulence in an M = 0.2 (where M is the Mach number) nominally zero-pressure-gradient and spatially developing flat-plate boundary layer are considered.
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Large eddy simulation of controlled transition to turbulence

Taraneh Sayadi, +1 more
- 29 Nov 2012 - 
TL;DR: In this paper, a large eddy simulation of H-and K-type transitions in a spatially developing zero-pressure-gradient boundary layer at Ma∞ = 0.2 is investigated using several subgrid scale (SGS) models including constant coefficient Smagorinsky and Vreman models and their dynamic extensions, dynamic mixed scale-similarity, dynamic one-equation kinetic energy model, and global coefficient V reman models.
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Parametrized data-driven decomposition for bifurcation analysis, with application to thermo-acoustically unstable systems

TL;DR: In this paper, a parametric DMD algorithm is introduced for studying dynamical systems going through a bifurcation, which alleviates multiple applications of the DMD decomposition to the system with fixed parametric values.
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Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers

TL;DR: The present study employs the parallel TSQR algorithm of (Demmel et al. in SIAM J Sci Comput 34(1):206–239, 2012), which is shown to scale well on machines with a large number of processors and, therefore, allows the decomposition of very large datasets.