Data-driven model for Lagrangian evolution of velocity gradients in incompressible turbulent flows

Das, R and Girimaji, SS (2024) Data-driven model for Lagrangian evolution of velocity gradients in incompressible turbulent flows. In: Journal of Fluid Mechanics, 984 .

PDF Jou_flu_mec_984_3_2024 - Published Version Download (2MB)

Abstract

Velocity gradient tensor, Aij � �ui/�xj, in a turbulence flow field is modelled by separating the treatment of intermittent magnitude (A = �AijAij) from that of the more universal normalised velocity gradient tensor, bij � Aij/A. The boundedness and compactness of the bij-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. © The Author(s), 2024. Published by Cambridge University Press.

Item Type:	Journal Article
Publication:	Journal of Fluid Mechanics
Publisher:	Cambridge University Press
Additional Information:	The copyright for this article belongs to author.
Keywords:	Tensors; Turbulence, Data-driven model; Gradient tensors; Incompressible turbulent flow; Intermittency; Isotropic turbulence; Lagrangian evolution; Reynold number; Turbulence flow fields; Turbulence modeling; Velocity gradients, Reynolds number, flow field; incompressible flow; Lagrangian analysis; modeling; Reynolds number; turbulence; turbulent flow
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	17 May 2024 08:48
Last Modified:	17 May 2024 08:48
URI:	https://eprints.iisc.ac.in/id/eprint/84791

Actions (login required)

View Item