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Numerical Ward-Type Tornado Simulator and its Application to Transient Wind-Induced Response of Long-Span Bridges

  • Chang'an University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this study, the tornado-like wind field is simulated by the numerical Ward-type tornado simulator based on computational fluid dynamics (CFD) techniques. To minimize the discrepancy between the simulated and field-measured tornado winds, the optimization strategy is developed to achieve optimal parameters of the numerical Ward-type tornado simulator, namely the inflow angle and translation speed. To facilitate the optimization process, a multi-fidelity surrogate model is utilized to effectively integrate both low-fidelity and high-fidelity data for accurate and efficient simulations. The “best” parameters based on the multi-fidelity surrogate model is input to the numerical Ward-type tornado simulator (using LES technique). Finally, the transient wind field generated using the validated numerical Ward-type tornado simulator is employed as the dynamic inputs to the finite element (FE) model of a long-span bridge.

Original languageEnglish
Title of host publicationIABSE Congress Nanjing 2022 - Bridges and Structures
Subtitle of host publicationConnection, Integration and Harmonisation, Report
PublisherInternational Association for Bridge and Structural Engineering (IABSE)
Pages283-290
Number of pages8
ISBN (Electronic)9783857481840
StatePublished - 2022
EventIABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation - Nanjing, China
Duration: Sep 21 2022 → Sep 23 2022

Publication series

NameIABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report

Conference

ConferenceIABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation
Country/TerritoryChina
CityNanjing
Period09/21/22 → 09/23/22

Keywords

  • computational fluid dynamics
  • long-span bridges
  • multi-fidelity data
  • optimization
  • surrogate model
  • tornado-like wind
  • transient wind-induced response

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