Long-span bridges located in thunderstorm-prone areas can potentially be struck by downburst transient winds. In this study, the downburst time-varying mean wind was simulated by an impinging jet model based on computational fluid dynamics (CFD). To make the simulation results fit well with the measurements, a parameter optimization method was developed. The objective function was established based on the errors between the simulated characteristic points and the target values from the measurement data. To increase the effectiveness, a Kriging surrogate model that was trained using data from numerical simulations was used. The parameter optimization method and the Kriging model were verified using five groups of test samples. The optimization efficiency was significantly increased by replacing the numerical model with a surrogate model during the optimization iteration. The simulation accuracy was clearly improved by the numerical modeling of a downburst based on optimized parameters. Subsequently, the nonstationary turbulent downburst wind was obtained by the combination of the Hilbert-based nonstationary fluctuations and the CFD-based time-varying trend. Finally, the dynamic response of a long-span bridge subjected to the moving downburst was presented. The results based on the simulation validate the optimized downburst wind field and highlight the significant influence on the bridge's aerodynamics and buffeting response.
机构:
Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Univ Macau, Dept Elect & Comp Engn, Macau 999078, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Yang, Qifan
Huang, Dihong
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Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Univ Macau, Dept Elect & Comp Engn, Macau 999078, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Huang, Dihong
Chen, Yong
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Guangdong Power Grid Corp, Zhuhai Power Supply Bur, DC Power Distribut & Consumpt Technol, Guangzhou 200235, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Chen, Yong
Dai, Ningyi
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Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
Univ Macau, Dept Elect & Comp Engn, Macau 999078, Peoples R China
Univ Macau, Zhuhai UM Sci & Technol Res Inst, Macau 999078, Peoples R ChinaUniv Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China