Best practice guidelines for the dynamic mode decomposition from a wind engineering perspective

Li, Cruz Y.; Chen, Zengshun; Weerasuriya, Asiri Umenga; Zhang, Xuelin; Lin, Xisheng; Zhou, Lei; Fu, Yunfei; Tse, Tim K.T.

doi:10.1016/j.jweia.2023.105506

Cited by 8 publications

(2 citation statements)

References 86 publications

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“…In contrast, POD [30] offers a distinct advantage in terms of simplicity and robustness, especially when dealing with the estimation of the mean reattachment length (X r ) based on general pressure patterns on roofs. While DMD and HODMD provide a more detailed temporal analysis, they require more complex data processing and interpretation compared to POD.…”

Section: Introductionmentioning

confidence: 99%

Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition

Ham,

Lee,

Choi

et al. 2023

Applied Sciences

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Investigating flow separation regions on the surfaces of three-dimensional bluff bodies in turbulent flows is important because these regions can induce significant aerodynamic loads. Separation bubbles can generate extreme pressures, making the roof components of low-rise buildings vulnerable. In this study, proper orthogonal decomposition (POD) was applied to wind-induced roof pressures to elucidate the physical significance of the dominant modes. Based on the interpretation of the first mode from the POD, the mean reattachment length of the roof separation bubbles on a low-rise building model in turbulent flow was determined. The mean reattachment length derived from the POD was then compared with the length obtained from an aerodynamic database. For the centerline of the roof, the mean reattachment length based on the POD aligned well with that from the aerodynamic database, showing a difference of less than 5%. This study highlights the efficacy of POD as a powerful tool for estimating the reattachment length of separation bubbles on bluff bodies.

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Section: Introductionmentioning

confidence: 99%

Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition

Ham,

Lee,

Choi

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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“…In the past few years, with the development of computational methods and data-driven algorithms [15][16][17] for big data, artificial intelligence methods including machine and deep learning have proven to be successful 16 . Machine learning may be categorized as supervised, semisupervised, and unsupervised learning 18 .…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for propulsive performance of a flapping foil

Bao,

Shi,

Wang

et al. 2023

Physics of Fluids

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While it is challenging for a traditional propulsor to achieve a wide range of force profile manipulation and propulsion efficiency, nature provides a solution for a flapping foil such as that found in birds and turtles. In this paper, we introduce a deep reinforcement learning (DRL) algorithm with great potential for solving nonlinear systems during the simulation to achieve a self-learning posture adjustment for a flapping foil to effectively improve its thrust performance. With DRL, a brute-force search is first carried out to provide intuition about the optimal trajectories of the foil and also a database for the following case studies. We implement an episodic training strategy for intelligent agent learning using the DRL algorithm. To address a slow data generation issue in the computational fluid dynamics simulation, we introduce a multi-environment technique to accelerate data exchange between the environment and the agent. This method is capable of adaptively and automatically performing an optimal foil path planning to generate the maximum thrust under various scenarios and can even outperform the optimal cases designed by users. Numerical results demonstrate how the proposed DRL is powerful to achieve optimization and has great potential to solve a more complex problem in the field of fluid mechanics beyond human predictability.

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The NOx-O3 photochemical reactive air pollutant dispersion around an isolated building—the role of turbulence model and building aspect ratio

Fu,

Lin,

et al. 2023

Building and Environment

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Best practice guidelines for the dynamic mode decomposition from a wind engineering perspective

Cited by 8 publications

References 86 publications

Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition

Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition

Deep reinforcement learning for propulsive performance of a flapping foil

The NOx-O3 photochemical reactive air pollutant dispersion around an isolated building—the role of turbulence model and building aspect ratio

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