Active Simulation of Transient Wind Field in a Multiple-Fan Wind Tunnel via Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Active Simulation of Transient Wind Field in a Multiple-Fan Wind Tunnel via Deep Reinforcement Learning

Published:2021-09 Issue:9 Volume:147 Page:04021056
ISSN:0733-9399
Container-title:Journal of Engineering Mechanics
language:en
Short-container-title:J. Eng. Mech.

Author:

Li Shaopeng¹,Snaiki Reda²,Wu Teng³^ORCID

Affiliation:

1. Graduate Student, Dept. of Civil, Structural and Environmental Engineering, Univ. at Buffalo, Buffalo, NY 14260.

2. Assistant Professor, Dept. of Construction Engineering, École de Technologie Supérieure, Univ. of Quebec, Montreal, QC, Canada H3C 1K3; formerly, Graduate Student, Dept. of Civil, Structural and Environmental Engineering, Univ. at Buffalo, Buffalo, NY 14260.

3. Associate Professor, Dept. of Civil, Structural and Environmental Engineering, Univ. at Buffalo, Buffalo, NY 14260 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EM.1943-7889.0001967

Reference37 articles.

1. Designing a blade-system to generate downburst outflows at boundary layer wind tunnel

2. Active modeling of large-scale turbulence

3. Surface pressure and wind load characteristics on prisms immersed in a simulated transient gust front flow field

4. Reproduction of wind velocity history in a multiple fan wind tunnel

5. Laboratory Simulation of the Atmospheric Boundary Layer

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