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Transfer learning from Re,=180 to 550
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Classroom Contents
Modelling and Controlling Turbulent Flows through Deep Learning
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- 1 Intro
- 2 Modeling and controlling turbulent flow through deep learning
- 3 Motivation
- 4 Effect of Reynolds number for a given pressure gradient history: well-resolved LES
- 5 Adaptive simulations of NACA0012 profile with rounded wing tip
- 6 High-fidelity simulation of wing-tip vortex at Rec-200,000 and 5 degree angle of attack
- 7 Applications of machine learning to fluid mechanics
- 8 Outline of machine-learning applications to fluid mechanics
- 9 Flow reconstruction with a convolutional neu network (CNN)
- 10 CNN architecture
- 11 Turbulence statistics at Re,=550
- 12 Improving training performance: Transfer learning at Re,=180
- 13 Transfer learning from Re,=180 to 550
- 14 From sparse measurements to high-resolution predictions using GANS
- 15 FCN model for predictions closer to the wall
- 16 Self similarity in the overlap region: Off-wall boundary conditions
- 17 Turbulent flow in a simplified urban environment
- 18 CNN-based B-variational autoencoders CNN- Introducing stochasticity
- 19 Orthogonality: determinant of the cross-corre matrix
- 20 Effect of the penalization factor B
- 21 Optimality: ranking CNN-BVAE modes and interpretability
- 22 Deep reinforcement learning for flow control Introduction
- 23 Control of a 2D separation bubble
- 24 DRL and opposition control in turbulent channel flow: blowing and suction
- 25 Summary and Conclusions
