scholarly journals Data-driven CFD modeling of turbulent flows through complex structures

2016 ◽  
Vol 62 ◽  
pp. 138-149 ◽  
Author(s):  
Jian-Xun Wang ◽  
Heng Xiao
Keyword(s):  
Turbulent Flows ◽  
Data Driven ◽  
Cfd Modeling ◽  
Complex Structures

scholarly journals Bubbles in turbulent flows: Data-driven, kinematic models with history terms

2020 ◽  
Vol 129 ◽  
pp. 103286
Author(s):  
Zhong Yi Wan ◽  
Petr Karnakov ◽  
Petros Koumoutsakos ◽  
Themistoklis P. Sapsis
Keyword(s):  
Turbulent Flows ◽  
Data Driven ◽  
Kinematic Models

scholarly journals Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

2022 ◽  
Author(s):  
Ricardo Vinuesa ◽  
Oriol Lehmkuhl ◽  
Adrian Lozano-Duran ◽  
Jean Rabault
Keyword(s):  
Reinforcement Learning ◽  
Flow Control ◽  
Flat Plate ◽  
Turbulent Flows ◽  
Control Strategies ◽  
Data Driven ◽  
Crucial Aspect ◽  
Aerodynamic Efficiency ◽  
Turbulence Simulation ◽  
Novel Approaches

In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modelling. Finally, we thoroughly revise data-driven methods, their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

scholarly journals Predicting the roughness length of turbulent flows over landscapes with multi-scale microtopography

2016 ◽  
Vol 4 (2) ◽  
pp. 391-405 ◽  
Author(s):  
Jon D. Pelletier ◽  
Jason P. Field
Keyword(s):  
Turbulent Flows ◽  
Roughness Length ◽  
Velocity Profiles ◽  
Cfd Modeling ◽  
Shear Stresses ◽  
Law Of The Wall ◽  
Multi Scale ◽  
Rough Form ◽  
Order Of Magnitude ◽  
The One

Abstract. The fully rough form of the law of the wall is commonly used to quantify velocity profiles and associated bed shear stresses in fluvial, aeolian, and coastal environments. A key parameter in this law is the roughness length, z0. Here we propose a predictive formula for z0 that uses the amplitude and slope of each wavelength of microtopography within a discrete-Fourier-transform-based approach. Computational fluid dynamics (CFD) modeling is used to quantify the effective z0 value of sinusoidal microtopography as a function of the amplitude and slope. The effective z0 value of landscapes with multi-scale roughness is then given by the sum of contributions from each Fourier mode of the microtopography. Predictions of the equation are tested against z0 values measured in  ∼ 105 wind-velocity profiles from southwestern US playa surfaces. Our equation is capable of predicting z0 values to 50 % accuracy, on average, across a 4 order of magnitude range. We also use our results to provide an alternative formula that, while somewhat less accurate than the one obtained from a full multi-scale analysis, has an advantage of being simpler and easier to apply.

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.

scholarly journals Data-driven POD-Galerkin reduced order model for turbulent flows

2020 ◽  
Vol 416 ◽  
pp. 109513 ◽  
Author(s):  
Saddam Hijazi ◽  
Giovanni Stabile ◽  
Andrea Mola ◽  
Gianluigi Rozza
Keyword(s):  
Turbulent Flows ◽  
Reduced Order Model ◽  
Data Driven ◽  
Order Model ◽  
Reduced Order

CFD-Modeling of turbulent flows in rod bundle and comparison to experiments

Kerntechnik ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. 68-71
Author(s):  
C. Lifante ◽  
B. Krull ◽  
T. Frank ◽  
R. Franz ◽  
U. Hampel
Keyword(s):  
Turbulent Flows ◽  
Cfd Modeling ◽  
Rod Bundle

scholarly journals Predicting the roughness length of turbulent flows over landscapes with multi-scale microtopography

2015 ◽  
Vol 3 (4) ◽  
pp. 1107-1142
Author(s):  
J. D. Pelletier ◽  
J. P. Field
Keyword(s):  
Turbulent Flows ◽  
Roughness Length ◽  
Velocity Profiles ◽  
Cfd Modeling ◽  
Shear Stresses ◽  
Law Of The Wall ◽  
Multi Scale ◽  
Rough Form ◽  
Order Of Magnitude ◽  
Computational Fluid Dynamics Cfd

Abstract. The fully rough form of the law of the wall is commonly used to quantify velocity profiles and associated bed shear stresses in fluvial, aeolian, and coastal environments. A key parameter in this law is the roughness length, z0. Here we propose a predictive formula for z0 that uses the amplitude and slope of each wavelength of microtopography within a discrete-Fourier-transform-based approach. Computational fluid dynamics (CFD) modeling is used to quantify the effective z0 value of sinusoidal microtopography as a function of the amplitude and slope. The effective z0 value of landscapes with multi-scale roughness is then given by the sum of contributions from each Fourier mode of the microtopography. Predictions of the equation are tested against z0 values measured in ~105 wind velocity profiles from southwestern US playa surfaces. Our equation is capable of predicting z0 values to 50 % accuracy, on average, across a four order-of-magnitude range.

Sign in / Sign up

Export Citation Format

Share Document