Machine learning-accelerated aerodynamic inverse design

The computational cost of iterative design methods has been a challenge in aerodynamics. In this research, the data-driven acceleration of an iterative inverse design method was implemented to reduce its computational cost. Although iterative design methods are robust, a lot of unwanted data is generated during their intermediate stages. Inverse design methods rely on correcting an initial geometry based on a given target parameter distribution. The generated data during the early iterations of the inverse design was incorporated into two deep-learning models to accelerate target geometry attainment. The deep learning models were used to recognize the correlation between the pressure distribution and corresponding geometry as well as the meaningful changes of geometry and pressure distribution toward their targets. The deep learning models were validated in viscous and inviscid compressible flows for various benchmark aerodynamics problems. In conclusion, between 70 to 80% computational cost decrease was observed for online uses of the machine learning module with the inverse design algorithm. This approach suggests incorporating machine learning techniques into design algorithms by exploiting the intermediate data for further improvement of them. We draw a new interpretation of learning dynamic changes through consecutive iterations instead of typical time-dependent problems in the use of LSTM network.