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Research Article

Shape optimization of closed-box girder considering dynamic and aerodynamic effects on flutter: a CFD-enabled and Kriging surrogate-based strategy

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Article: 2191693 | Received 15 Oct 2022, Accepted 13 Mar 2023, Published online: 30 Mar 2023
 

Abstract

Shape optimization of single box girder allows the bridge to achieve the most favourable aerodynamic performance. Currently, aerodynamic optimization of bridge deck is usually achieved by traversing all potential schemes using wind tunnel tests or computational fluid dynamics (CFD), which are time-consuming, laborious, and restricted to few numbers of geometric parameter. This study proposed an aerodynamic optimization strategy using CFD technique, uniform design, Kriging surrogate model and hybrid infill sampling criteria to achieve the most favourable girder shape with the largest flutter wind speed. The effect of the variation of section shape on the dynamic characteristics of a real suspension bridge is considered. The Kriging surrogate model is then applied to approximately describe the aerodynamic parameters including static force coefficients and flutter derivatives as well as dynamic characteristics of the bridge. The cross-validation and samples infill are conducted to validate and improve the accuracy of the model. The optimal ranges, influence degree of each factor and their interactions on flutter performance are examined by statistical analysis. The contributions of aerodynamic parameters and dynamic characteristics to flutter performance are compared and discussed. The girder shape with the best flutter performance is obtained in updated surrogate model.

Acknowledgments

The authors gratefully acknowledge the support of the National Natural Science Foundation of China (52108469, 51978527, 52078383, 52278520), the Fundamental Research Funds for the Central Universities (22120220577).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant numbers 52108469, 51978527, 52078383, 52278520]; Fundamental Research Funds for the Central Universities [grant number 22120220577].