Accurate prediction of aerodynamic forces in real-time is crucial for autonomous navigation of unmanned aerial vehicles (UAVs). This paper presents a data-driven aerodynamic force prediction model based on a small number of pressure sensors located on the surface of UAV. The model is built on a linear term that can make a reasonably accurate prediction and a nonlinear correction for accuracy improvement. The linear term is based on a reduced basis reconstruction of the surface pressure distribution, where the basis is extracted from numerical simulation data and the basis coefficients are determined by solving linear pressure reconstruction equations at a set of sensor locations. Sensor placement is optimized using the discrete empirical interpolation method (DEIM). Aerodynamic forces are computed by integrating the reconstructed surface pressure distribution. The nonlinear term is an artificial neural network (NN) that is trained to bridge the gap between the ground truth and the DEIM prediction, especially in the scenario where the DEIM model is constructed from simulation data with limited fidelity. A large network is not necessary for accurate correction as the linear model already captures the main dynamics of the surface pressure field, thus yielding an efficient DEIM+NN aerodynamic force prediction model. The model is tested on numerical and experimental dynamic stall data of a 2D NACA0015 airfoil, and numerical simulation data of dynamic stall of a 3D drone. Numerical results demonstrate that the machine learning enhanced model can make fast and accurate predictions of aerodynamic forces using only a few pressure sensors, even for the NACA0015 case in which the simulations do not agree well with the wind tunnel experiments. Furthermore, the model is robust to noise.

翻译：实时准确预测气动力对于无人飞行器（UAV）的自主导航至关重要。本文提出一种基于无人机表面少量压力传感器的数据驱动气动力预测模型。该模型包含一个能进行合理准确预测的线性项，以及一个用于提升精度的非线性修正项。线性项基于表面压力分布的降阶基重构，其中基函数从数值模拟数据中提取，基系数通过求解一组传感器位置处的线性压力重构方程确定。传感器布局采用离散经验插值法（DEIM）进行优化。气动力通过对重构的表面压力分布积分计算得到。非线性项为人工神经网络（NN），其训练目标是在地面真值与DEIM预测之间建立桥梁，尤其适用于DEIM模型基于保真度有限的模拟数据构建的场景。由于线性模型已捕获表面压力场的主要动力学特征，因此无需大型网络进行精确修正，从而得到高效的DEIM+NN气动力预测模型。该模型在二维NACA0015翼型的数值模拟与实验动态失速数据，以及三维无人机的动态失速数值模拟数据上进行了测试。数值结果表明，即使对于模拟与风洞实验吻合度较差的NACA0015案例，机器学习增强模型仅需少量压力传感器即可快速准确地预测气动力。此外，该模型对噪声具有鲁棒性。