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- Title
Data-driven model for Lagrangian evolution of velocity gradients in incompressible turbulent flows.
- Authors
Das, Rishita; Girimaji, Sharath S.
- Abstract
Velocity gradient tensor, $A_{ij}\equiv \partial u_i/\partial x_j$ , in a turbulence flow field is modelled by separating the treatment of intermittent magnitude ($A = \sqrt {A_{ij}A_{ij}}$) from that of the more universal normalised velocity gradient tensor, $b_{ij} \equiv A_{ij}/A$. The boundedness and compactness of the $b_{ij}$ -space along with its universal dynamics allow for the development of models that are reasonably insensitive to Reynolds number. The near-lognormality of the magnitude $A$ is then exploited to derive a model based on a modified Ornstein–Uhlenbeck process. These models are developed using data-driven strategies employing high-fidelity forced isotropic turbulence data sets. A posteriori model results agree well with direct numerical simulation data over a wide range of velocity-gradient features and Reynolds numbers.
- Subjects
TURBULENT flow; INCOMPRESSIBLE flow; TURBULENCE; REYNOLDS number; ORNSTEIN-Uhlenbeck process
- Publication
Journal of Fluid Mechanics, 2024, Vol 984, p1
- ISSN
0022-1120
- Publication type
Article
- DOI
10.1017/jfm.2024.235