  Accurate prediction of laminar-turbulent transition is a critical element of
computational fluid dynamics simulations for aerodynamic design across multiple
flow regimes. Traditional methods of transition prediction cannot be easily
extended to flow configurations where the transition process depends on a large
set of parameters. In comparison, neural network methods allow higher
dimensional input features to be considered without compromising the efficiency
and accuracy of the traditional data driven models. Neural network methods
proposed earlier follow a cumbersome methodology of predicting instability
growth rates over a broad range of frequencies, which are then processed to
obtain the N-factor envelope, and then, the transition location based on the
correlating N-factor. This paper presents an end-to-end transition model based
on a recurrent neural network, which sequentially processes the mean
boundary-layer profiles along the surface of the aerodynamic body to directly
predict the N-factor envelope and the transition locations over a
two-dimensional airfoil. The proposed transition model has been developed and
assessed using a large database of 53 airfoils over a wide range of chord
Reynolds numbers and angles of attack. The sequence-to-sequence transduction
model proposed herein provides a more direct approach for accurate predictions
of the transition location than the earlier neural network methods, which
predict the local amplification rate of a single instability mode at a fixed
location along the airfoil. The large universe of airfoils encountered in
various applications causes additional difficulties. As such, we provide
further insights on selecting training datasets from large amounts of available
data.
