Aeroelastic force prediction via temporal fusion transformers
Overview
Research
Identity
Other
View All
Overview
abstract
AbstractAerostructural shape design and optimization of bridge decks rely on accurately estimating their selfexcited 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. Stateoftheart 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 selfexcited forces time series before their integration into FDs. Emulating aeroelastic forces in the time domain permits the inclusion of timeseries amplitudes, frequencies, phases, and other properties in the training process, enabling a more solid learning strategy that is independent of the selfexcited forces modeling order and the inherent loss of information during the identification of FDs. TFTs' long and shortterm context awareness, combined with their interpretability and enhanced ability to deal with static and timedependent covariates, make them an ideal choice for predicting unseen aeroelastic forces time series. The proposed TFTbased metamodel offers a powerful technique for drastically improving the accuracy and versatility of windresistant design optimizationframeworks.
