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Wind and Structures Volume 39, Number 4, October 2024 , pages 271-285 DOI: https://doi.org/10.12989/was.2024.39.4.271 |
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Reproduction of wind speed time series in a two-dimensional numerical multiple-fan wind tunnel using deep reinforcement learning |
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Qingshan Yang, Zhenzhi Luo, Ke Li and Teng Wu
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| Abstract | ||
| The multiple-fan wind tunnel is an important facility for reproducing target wind field. Existing control methods for the multiple-fan wind tunnel can generate wind speeds that satisfy the target statistical characteristics of a wind field (e.g., power spectrum). However, the frequency-domain features cannot well represent the nonstationary winds of extreme storms (e.g., downburst). Therefore, this study proposes a multiple-fan wind tunnel control scheme based on Deep Reinforcement Learning (DRL), which will completely transform into a data-driven closed-loop control problem, to reproduce the target wind field in the time domain. Specifically, the control scheme adopts the Deep Deterministic Policy Gradient (DDPG) paradigm in which the strong fitting ability of Deep Neural Networks (DNN) is utilized. It can establish the complex relationship between the target wind speed time series and the current control strategy in the DRL-agent. To address the fluid memory effect of the wind field, this study innovatively designs the system state and control reward to improve the reproduction performance based on historical data. To validate the performance of the model, we established a simplified flow field based on Navier Stokes equations to simulate a two-dimensional numerical multiple-fan wind tunnel environment. Using the strategy of DRL decision maker, we generated a wind speed time series with minor error from the target under low Reynolds number conditions. This is the first time that the AI methods have been used to generate target wind speed time series in a multiple-fan wind tunnel environment. The hyperparameters in the DDPG paradigm are analyzed to identify a set of optimal parameters. With these efforts, the trained DRL-agent can simultaneously reproduce the wind speed time series in multiple positions. | ||
| Key Words | ||
| active flow control; DDPG paradigm; deep reinforcement learning; multiple-fan wind tunnel; wind speed reproduction | ||
| Address | ||
| Qingshan Yang:1)Key Laboratory of New Technology for Construction of Cities in Mountain Area (Chongqing University), Ministry of Education, Chongqing, China, 400045 2)School of Civil Engineering, Chongqing University, Chongqing, China, 400045 Zhenzhi Luo:School of Civil Engineering, Chongqing University, Chongqing, China, 400045 Ke Li:1)Key Laboratory of New Technology for Construction of Cities in Mountain Area (Chongqing University), Ministry of Education, Chongqing, China, 400045 2)School of Civil Engineering, Chongqing University, Chongqing, China, 400045 Teng Wu:Department of Civil, Structural & Environmental Engineering, The University at Buffalo, New York, US, 14260 | ||