非定常气动力建模研究与虚拟飞行试验验证

非定常气动力建模涉及空气动力学、飞行力学、飞行控制等多个领域，是完善飞机大迎角气动数据库的关键。传统的气动数据库模型为动导数模型，由静态气动力、旋转天平、动导数等数据构成，无法精细表征过失速机动状态下的非定常效应。循环神经网络（RNN）结构是一种处理和预测序列数据的神经网络结构，在人工智能领域运用广泛，与非定常气动力一样都具有时间序列依赖的特点。重点研究了循环神经网络在非定常气动力建模中的应用，利用单自由度俯仰振荡的风洞试验数据进行建模。使用强迫运动试验与虚拟飞行试验2种方法对非定常模型进行验证:在强迫运动试验中，通过直接对比气动力曲线，对具有实战意义的眼镜蛇机动进行了验证；在虚拟飞行试验中，通过对比试验与建模仿真的运动参数曲线，验证了气动力模型的准确性。2种验证方法均表明循环神经网络模型比传统动导数模型更接近试验结果。

Unsteady aerodynamic modeling research and virtual flight test verification

Unsteady aerodynamic modeling involves aerodynamics, flight mechanics, flight control and other fields, which is the key of aircraft high angle of attack database. The traditional aerodynamic model is composed of static aerodynamic, rotating balance and dynamic derivative data, which cannot describe unsteady aerodynamics exactly. Recurrent Neural Network （RNN） structure is a kind of neural network structure for processing and predicting sequence data, which is widely used in the field of artificial intelligence. RNN has the same time–dependent characteristic as unsteady aerodynamics. The application of RNN on the unsteady aerodynamic modeling has been researched. The forced motion test and virtual flight test have been used for unsteady aerodynamic model’s verification. In the forced motion wind tunnel test, comparing the aerodynamic forces of cobra maneuver, the result demonstrates that the RNN model is more accurate than the traditional model. In the virtual flight test, comparing the movement parameters curve of the wind tunnel test and simulation, the results also demonstrate that the RNN model is closer to the wind tunnel test than the traditional dynamic derivative model.