人工智能气动特性预测技术在火箭子级落区控制项目的应用

发展了一种基于人工智能算法的气动特性预测技术,在开展部分工况风洞试验基础上,结合少量数值仿真结果,通过机器学习模型预测全部工况气动特性.该方法能够降低研制成本,缩短周期.先后解决了相关函数选择、模型超参数训练、数据检验和"人在回路"应用等关键算法与技术问题,应用于运载火箭子级栅格舵落区控制项目气动研制,获得了设计所需完...

The Application of Aerodynamic Coefficients Prediction Technique via Artificial Intelligence Method to Rocket First Stage Landing Area Control Project

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.