Mitigating the sonic boom to an acceptable stage is crucial for the next generation of supersonic transports. The primary way to suppress sonic booms is to develop a low sonic boom aerodynamic shape design. This paper proposes an inverse design approach to optimize the near-field signature of an aircraft, making it close to the shaped ideal ground signature after propagation in the atmosphere. By introducing the Deep Neural Network (DNN) model for the first time, a predicted input of Augmented Burgers equation is inversely achieved. By the K-fold cross-validation method, the predicted ground signature closest to the target ground signature is obtained. Then, the corresponding equivalent area distribution is calculated using the classical Whitham’s F-function theory from the optimal near-field signature. The inversion method is validated using the classic example of the C608 vehicle provided by the Third Sonic Boom Prediction Workshop (SBPW-3). The results show that the design ground signature is consistent with the target signature. The equivalent area distribution of the design result is smoother than the baseline distribution, and it shrinks significantly in the rear section. Finally, the robustness of this method is verified through the inverse design of sonic boom for the non-physical ground signature target.

中文翻译：

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用于低动臂超音速配置逆向设计的深度学习

将音爆降低到可接受的水平对于下一代超音速运输至关重要。抑制音爆的主要方法是开发低音爆的气动外形设计。本文提出了一种逆向设计方法来优化飞机的近场特征，使其在大气中传播后接近成形的理想地面特征。通过首次引入深度神经网络（DNN）模型，反向实现了增广伯格斯方程的预测输入。通过K折交叉验证方法，得到与目标地面特征最接近的预测地面特征。然后，使用经典 Whitham 的 F 函数理论从最佳近场特征计算相应的等效面积分布。使用第三次音爆预测研讨会 (SBPW-3) 提供的 C608 车辆的经典示例验证反演方法。结果表明，设计地面特征与目标特征一致。设计结果等效面积分布较基线分布平滑，后段明显收缩。最后，通过非物理地面特征目标的音爆逆向设计，验证了该方法的鲁棒性。