Numerical study of tornado-borne debris on a low-rise building through large eddy simulation

Liu, Zhenqing; Cao, Yiwen; Cao, Jun; Wang, Yi–Ze; Cao, Shuyang

doi:10.1016/j.jfluidstructs.2021.103379

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…These models are clearly over-simplified. The tornado wind fields are also modeled using CFD simulations (e.g., Kuai et al, 2008;Ishihara et al, 2011;Liu and Ishihara 2015;Eguchi et al, 2018;Gairola and Bitsuamlak 2019;Kawaguchi et al, 2019;Huo et al, 2020;Liu et al, 2021) wind field). Just like ML applications to downbursts, a high number of input variables (predictors) were utilized for the reviewed ML models.…”

Section: Tornadoesmentioning

confidence: 99%

Applications of Machine Learning to Wind Engineering

Snaiki

2022

Front. Built Environ.

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Advances of the analytical, numerical, experimental and field-measurement approaches in wind engineering offers unprecedented volume of data that, together with rapidly evolving learning algorithms and high-performance computational hardware, provide an opportunity for the community to embrace and harness full potential of machine learning (ML). This contribution examines the state of research and practice of ML for its applications to wind engineering. In addition to ML applications to wind climate, terrain/topography, aerodynamics/aeroelasticity and structural dynamics (following traditional Alan G. Davenport Wind Loading Chain), the review also extends to cover wind damage assessment and wind-related hazard mitigation and response (considering emerging performance-based and resilience-based wind design methodologies). This state-of-the-art review suggests to what extend ML has been utilized in each of these topic areas within wind engineering and provides a comprehensive summary to improve understanding how learning algorithms work and when these schemes succeed or fail. Moreover, critical challenges and prospects of ML applications in wind engineering are identified to facilitate future research efforts.

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Section: Tornadoesmentioning

confidence: 99%

Applications of Machine Learning to Wind Engineering

Snaiki

2022

Front. Built Environ.

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Performance of Hurricane-Resistant Housing during the 2022 Arabi, Louisiana, Tornado

Roueche,

Chen,

Soto

et al. 2024

J. Struct. Eng.

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Numerical study on self-excited forces and flow fields for a thin plate under a sinusoidal non-stationary wind condition

2023

Physics of Fluids

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The self-excited forces and flow fields around a thin plate under stationary and sinusoidal non-stationary wind conditions were simulated using the computational fluid dynamics method. The differences between the simulated self-excited force results and calculated results based on Scanlan's linear flutter theory under the non-stationary wind condition were analyzed from the perspective of flow field characteristics. Furthermore, the effects of different torsional amplitudes on the thin plate's self-excited forces under the non-stationary wind condition were investigated. The results showed that there are significant nonlinear effects of self-excited forces on the thin plate for the non-stationary wind, with large differences between the simulated and calculated amplitudes for each harmonic component. An obvious flow pressure gradient distribution is observed along the thin plate for the non-stationary wind, and the flow pressure around the thin plate is closely related to the slope of the wind speed. The non-stationary incoming wind aggravates the disturbances in the shear layer at the leading edge of the thin plate, leading to deviations between the simulated and calculated self-excited forces. As the torsional amplitude increases, there is no longer a linearly proportional relationship between the self-excited forces and torsional amplitude under the non-stationary wind condition, and more severe flow separations and influence ranges of shedding vortex occur around the thin plate.

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Numerical study of tornado-borne debris on a low-rise building through large eddy simulation

Cited by 3 publications

References 54 publications

Applications of Machine Learning to Wind Engineering

Applications of Machine Learning to Wind Engineering

Performance of Hurricane-Resistant Housing during the 2022 Arabi, Louisiana, Tornado

Numerical study on self-excited forces and flow fields for a thin plate under a sinusoidal non-stationary wind condition

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