  The inverse approach is computationally efficient in aerodynamic design as
the desired target performance distribution is prespecified. However, it has
some significant limitations that prevent it from achieving full efficiency.
First, the iterative procedure should be repeated whenever the specified target
distribution changes. Target distribution optimization can be performed to
clarify the ambiguity in specifying this distribution, but several additional
problems arise in this process such as loss of the representation capacity due
to parameterization of the distribution, excessive constraints for a realistic
distribution, inaccuracy of quantities of interest due to theoretical/empirical
predictions, and the impossibility of explicitly imposing geometric
constraints. To deal with these issues, a novel inverse design optimization
framework with a two-step deep learning approach is proposed. A variational
autoencoder and multi-layer perceptron are used to generate a realistic target
distribution and predict the quantities of interest and shape parameters from
the generated distribution, respectively. Then, target distribution
optimization is performed as the inverse design optimization. The proposed
framework applies active learning and transfer learning techniques to improve
accuracy and efficiency. Finally, the framework is validated through
aerodynamic shape optimizations of the wind turbine airfoil. Their results show
that this framework is accurate, efficient, and flexible to be applied to other
inverse design engineering applications.
