  The transonic buffet is a detrimental phenomenon occurs on supercritical
airfoils and limits aircraft's operating envelope. Traditional methods for
predicting buffet onset rely on multiple computational fluid dynamics
simulations to assess a series of airfoil flowfields and then apply criteria to
them, which is slow and hinders optimization efforts. This article introduces
an innovative approach for rapid buffet onset prediction. A machine-learning
flowfield prediction model is pre-trained on a large database and then deployed
offline to replace simulations in the buffet prediction process for new airfoil
designs. Unlike using a model to directly predict buffet onset, the proposed
technique offers better visualization capabilities by providing users with
intuitive flowfield outputs. It also demonstrates superior generalization
ability, evidenced by a 32.5% reduction in average buffet onset prediction
error on the testing dataset. The method is utilized to optimize the buffet
performance of 11 distinct airfoils within and outside the training dataset.
The optimization results are verified with simulations and proved to yield
improved samples across all cases. It is affirmed the pre-trained flowfield
prediction model can be applied to accelerate aerodynamic shape optimization,
while further work still needs to raise its reliability for this
safety-critical task.
