Abstract
A novel approach of predicting aerodynamic data via artificial intelligence technique is proposed in this article. Based on wind tunnel tests of partial test states, combined with several CFD results, machine learning via Kriging model is used to predict the whole aerodynamic characteristics to shorten the development cycle and reduce the expensive wind-tunnel tests as many as possible. After solving several key technical problems such as the selection of correlation functions, hyper-parameters training, data verification and application of "man-in-loop" technique, the complete set of aerodynamic data was obtained successfully and used to the control law design in the rocket first stage landing area control project with grid fins. The correctness of the proposed method was validated by a flight test on 26th July, 2019, which was carried out successfully for the first time in China. At the end, the grading of technology-maturity-degree for the artificial intelligence technique is presented to evaluate application to aerodynamic engineering design problems.
| Translated title of the contribution | The Application of Aerodynamic Coefficients Prediction Technique via Artificial Intelligence Method to Rocket First Stage Landing Area Control Project |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 61-73 |
| Number of pages | 13 |
| Journal | Yuhang Xuebao/Journal of Astronautics |
| Volume | 42 |
| Issue number | 1 |
| DOIs | |
| State | Published - 30 Jan 2021 |