Abstract
As a multidisciplinary field in fluid mechanics, flow control has played a key role in both scientific research and engineering applications. Due to complicated features of flow systems such as strong nonlinearity, flow control, especially closed-loop control, has been a challenging issue in the past decades. Recently, the rapid developing machine learning has brought new methods, new perspectives, and new views to diverse fields, and also to flow control. This article reviews three distinct ideas that involve machine learning into flow control, so as to demonstrate an overall view of machine learning in flow control, and furthermore, to outline some trends for this field.
| Translated title of the contribution | Machine learning for flow control: Applications and development trends |
|---|---|
| Original language | Chinese (Simplified) |
| Article number | 524686 |
| Journal | Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica |
| Volume | 42 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 25 Apr 2021 |
Keywords
- Deep reinforcement learning
- Flow control
- Genetic programming
- Machine learning
- Reduced order modeling
ASJC Scopus subject areas
- Modelling and Simulation
- Aerospace Engineering
- Space and Planetary Science
- Applied Mathematics