Sequential aero‐structural optimization method for efficient bridge design

Active aerodynamic flaps can effectively improve the aerodynamic stability of bridges; however, the determination of optimal control parameters often requires a large number of experiments. This study proposes a method for determining the optimal control parameters of active flaps based on the surrogate model and computational fluid dynamics (CFD) simulation technology. The computational fluid dynamics method is used to calculate the flutter derivatives of the deck–flap system under a small number of control parameters, and then the surrogate model is used to extend the flutter derivatives to the design domain of entire control parameters. Furthermore, the changing trend of flutter performance under flap control parameters can be obtained. This method can avoid complex and expensive active control wind tunnel tests and obtain more accurate optimal control parameters at less cost, which is helpful for the convenient application of active control technology in actual engineering.

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Aeroelastic force prediction via temporal fusion transformers

Cid Montoya,

Mishra,

Verma

et al. 2024

Computer aided Civil Eng

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Aero‐structural shape design and optimization of bridge decks rely on accurately estimating their self‐excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. State‐of‐the‐art methods address deck shape tailoring by interpolating discrete values of integrated flutter derivatives (FDs) in the frequency domain. Nevertheless, more sophisticated strategies can improve surrogate accuracy and potentially reduce the required number of samples. We propose a time domain emulation strategy harnessing temporal fusion transformers (TFTs) to predict the self‐excited forces time series before their integration into FDs. Emulating aeroelastic forces in the time domain permits the inclusion of time‐series amplitudes, frequencies, phases, and other properties in the training process, enabling a more solid learning strategy that is independent of the self‐excited forces modeling order and the inherent loss of information during the identification of FDs. TFTs' long‐ and short‐term context awareness, combined with their interpretability and enhanced ability to deal with static and time‐dependent covariates, make them an ideal choice for predicting unseen aeroelastic forces time series. The proposed TFT‐based metamodel offers a powerful technique for drastically improving the accuracy and versatility of wind‐resistant design optimization frameworks.

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Sequential aero‐structural optimization method for efficient bridge design

Cited by 4 publications

References 100 publications

Shape- and frequency-dependent self-excited forces emulation for the aero-structural design of bluff deck bridges

Shape- and frequency-dependent self-excited forces emulation for the aero-structural design of bluff deck bridges

Flutter performance simulation on streamlined bridge deck with active aerodynamic flaps

Aeroelastic force prediction via temporal fusion transformers

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