  Machine learning has been widely utilized in fluid mechanics studies and
aerodynamic optimizations. However, most applications, especially flow field
modeling and inverse design, involve two-dimensional flows and geometries. The
dimensionality of three-dimensional problems is so high that it is too
difficult and expensive to prepare sufficient samples. Therefore, transfer
learning has become a promising approach to reuse well-trained two-dimensional
models and greatly reduce the need for samples for three-dimensional problems.
This paper proposes to reuse the baseline models trained on supercritical
airfoils to predict finite-span swept supercritical wings, where the simple
swept theory is embedded to improve the prediction accuracy. Two baseline
models for transfer learning are investigated: one is commonly referred to as
the forward problem of predicting the pressure coefficient distribution based
on the geometry, and the other is the inverse problem that predicts the
geometry based on the pressure coefficient distribution. Two transfer learning
strategies are compared for both baseline models. The transferred models are
then tested on the prediction of complete wings. The results show that transfer
learning requires only approximately 500 wing samples to achieve good
prediction accuracy on different wing planforms and different free stream
conditions. Compared to the two baseline models, the transferred models reduce
the prediction error by 60% and 80%, respectively.
