Search for: All records

Wind-sensitive bridges are commonly designed based on their aeroelastic responses under synoptic winds. However, a holistic aero-structural design framework must address all potential wind scenarios along the bridge life cycle, including non-synoptic events and synoptic winds with relevant variations in the mean angle of attack due to wind-induced static deck deformation or complex terrain effects. This requires the evaluation of the aeroelastic responses considering the sensitivity of the fluid-structure interaction parameters to the wind angle of attack. Aiming at properly modeling these effects within design frameworks, this study proposes harnessing a multi-directional aeroelastic Kriging surrogate trained with forced vibration CFD simulations to emulate the flutter derivatives as a function of the deck shape, reduced velocity, and mean angle of attack. A bridge deck with a variable depth ranging from streamlined to bluff configurations is studied in detail, showing drastic changes in relevant flutter derivatives. The deck shape drives the impact of the mean angle of attack in some critical flutter derivatives, including the occurrence of A2* sign flipping, with its implications in the torsional stability. The resulting aeroelastic surrogate is conceived to be integrated into aero-structural optimization frameworks for optimally shaping bridge decks under synoptic and non-synoptic wind scenarios. 

Contemporary aero-structural design frameworks for wind-sensitive bridges are mostly based on the assessment of aeroelastic responses under synoptic winds. However, holistic design methodologies must address all potential wind scenarios, such as non-synoptic wind events and variations in the angle of attack due to complex terrains. This requires the evaluation of the aeroelastic responses considering the sensitivity of the fluid-structure interaction parameters with the angle of attack. Hence, this study proposes a Kriging-based multi-directional aeroelastic surrogate to emulate the flutter derivatives of bridge decks as a function of the deck shape, frequency of oscillation of the deck, and the mean incident angles of wind. This design tool is pivotal to properly modeling the nonlinear features of flutter derivatives at low reduced velocities and their sensitivity with the angle of attack. The aeroelastic surrogate will be later integrated into aero-structural design frameworks for the shape optimization of bridge decks under non-stationary winds. 

The shape design and optimization of bluff decks prone to aeroelastic phenomena require emulating the fluid-structure interaction parameters as a function of the body shape and the oscillation frequency. This is particularly relevant for long- and medium-span bridges equipped with single-box decks that are far from being considered streamlined and for other girder typologies such as traditional truss decks and modern twin- and multi-box decks. The success of aero-structural design frameworks, which are inherently iterative, relies on the efficient and accurate numerical evaluation of the wind-induced responses. This study proposes emulating the fluid-structure interaction parameters of bluff decks using surrogate modeling techniques to integrate them into aero-structural optimization frameworks. The surrogate is trained with data extracted from forced-vibration CFD simulations of a typical single-box girder to emulate the values of the flutter derivatives as a function of the deck shape and reduced velocity. The focus is on deck configurations ranging from streamlined to bluff cross-sections and on low reduced velocities to capture eventual aerodynamic nonlinearities. The girder cross-section geometry is tailored based on its buffeting performance. This design tool is fundamental to finding the optimum balance between the structural and aeroelastic requirements that drive the design of bluff deck bridges. 

Abstract 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.