DOAJ Open Access 2022

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

Ricardo Vinuesa Oriol Lehmkuhl Adrian Lozano-Durán Jean Rabault

Abstrak

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 a high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modeling. Finally, we thoroughly revise data-driven methods and 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.

Topik & Kata Kunci

Thermodynamics Descriptive and experimental mechanics

Penulis (4)

Ricardo Vinuesa

Oriol Lehmkuhl

Adrian Lozano-Durán

Jean Rabault

Format Sitasi

APA MLA BibTeX

Vinuesa, R., Lehmkuhl, O., Lozano-Durán, A., Rabault, J. (2022). Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning. https://doi.org/10.3390/fluids7020062

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.3390/fluids7020062

Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.3390/fluids7020062
Akses: Open Access ✓