  Airfoil aerodynamic optimization based on single-point design may lead to
poor off-design behaviors. Multipoint optimization that considers the
off-design flow conditions is usually applied to improve the robustness and
expand the flight envelope. Many deep learning models have been utilized for
the rapid prediction or reconstruction of flowfields. However, the flowfield
reconstruction accuracy may be insufficient for cruise efficiency optimization,
and the model generalization ability is also questionable when facing airfoils
different from the airfoils with which the model has been trained. Because a
computational fluid dynamic evaluation of the cruise condition is usually
necessary and affordable in industrial design, a novel deep learning framework
is proposed to utilize the cruise flowfield as a prior reference for the
off-design condition prediction. A prior variational autoencoder is developed
to extract features from the cruise flowfield and to generate new flowfields
under other free stream conditions. Physical-based loss functions based on
aerodynamic force and conservation of mass are derived to minimize the
prediction error of the flowfield reconstruction. The results demonstrate that
the proposed model can reduce the prediction error on test airfoils by 30%
compared to traditional models. The physical-based loss function can further
reduce the prediction error by 4%. The proposed model illustrates a better
balance of the time cost and the fidelity requirements of evaluation for cruise
and off-design conditions, which makes the model more feasible for industrial
applications.
