scholarly journals Data‐driven identification of the spatiotemporal structure of turbulent flows by streaming dynamic mode decomposition

2022 ◽  
Author(s):  
Rui Yang ◽  
Xuan Zhang ◽  
Philipp Reiter ◽  
Detlef Lohse ◽  
Olga Shishkina ◽  
...  
Keyword(s):  
Turbulent Flows ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
Spatiotemporal Structure ◽  
Mode Decomposition

scholarly journals Enhancing CFD predictions in shape design problems by model and parameter space reduction

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Marco Tezzele ◽  
Nicola Demo ◽  
Giovanni Stabile ◽  
Andrea Mola ◽  
Gianluigi Rozza
Keyword(s):  
Turbulent Flows ◽  
Parameter Space ◽  
Dimensional Space ◽  
Lift Coefficient ◽  
Target Function ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Space Reduction ◽  
Mode Decomposition ◽  
Computational Resources

Abstract In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD.

scholarly journals A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

2015 ◽  
Vol 25 (6) ◽  
pp. 1307-1346 ◽  
Author(s):  
Matthew O. Williams ◽  
Ioannis G. Kevrekidis ◽  
Clarence W. Rowley
Keyword(s):  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
Koopman Operator ◽  
Mode Decomposition

scholarly journals Dynamic mode decomposition of inertial particle caustics in Taylor–Green flow

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Omstavan Samant ◽  
Jaya Kumar Alageshan ◽  
Sarveshwar Sharma ◽  
Animesh Kuley
Keyword(s):  
Turbulent Flows ◽  
Particle Image ◽  
Experimental System ◽  
Stokes Number ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Complex Structures ◽  
Inertial Particles ◽  
Mode Decomposition ◽  
Image Velocimetry

AbstractInertial particles advected by a background flow can show complex structures. We consider inertial particles in a 2D Taylor–Green (TG) flow and characterize particle dynamics as a function of the particle’s Stokes number using dynamic mode decomposition (DMD) method from particle image velocimetry (PIV) like-data. We observe the formation of caustic structures and analyze them using DMD to (a) determine the Stokes number of the particles, and (b) estimate the particle Stokes number composition. Our analysis in this idealized flow will provide useful insight to analyze inertial particles in more complex or turbulent flows. We propose that the DMD technique can be used to perform similar analysis on an experimental system.

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