Blowing-only opposition control: Characteristics of turbulent drag reduction and implementation by deep learning-Reference-Cited by-同舟云学术

Blowing-only opposition control: Characteristics of turbulent drag reduction and implementation by deep learning

Published:2021-03-01 Issue:3 Volume:11 Page:035016
ISSN:2158-3226
Container-title:AIP Advances
language:en
Short-container-title:AIP Advances

Author:

Li Zexiang¹²^ORCID,Dang Xiangxin¹^ORCID,Lv Pengyu¹,Duan Huiling¹²^ORCID

Affiliation:

1. State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, Peking University, Beijing 100871, China

2. Key Laboratory of High Energy Density Physics Simulation, Center for Applied Physics and Technology, Peking University, Beijing 100871, China

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

AIP Publishing

Subject

General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0042740

Reference62 articles.

1. Turbulence statistics in fully developed channel flow at low Reynolds number

2. Coherent Motions in the Turbulent Boundary Layer

3. Coherent structures and associated subgrid-scale energy transfer in a rough-wall turbulent channel flow

4. Near-wall structure of turbulent boundary layer with spanwise-wall oscillation

5. Drag reduction by spanwise wall oscillation in wall-bounded turbulent flows

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1. Convolutional Neural Networks for Implementing Opposition Control in Turbulent Channel Flows;2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON);2023-05-01

2. Drag reduction of blowing-based active control in a turbulent boundary layer;Physics of Fluids;2022-11

3. Robust training approach of neural networks for fluid flow state estimations;International Journal of Heat and Fluid Flow;2022-08