In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

在这项工作中，为小型无人机的设计优化和数学建模提出了一种计算效率高且精度高的非线性空气动力学构型分析方法。首先，我们开发了一种新颖的非线性提升线方法，与实验和计算密集型工具相比，该方法 (a) 为失速前和失速后的空气动力学行为提供了非常好的匹配，(b) 以更少的时间数量级生成这些结果与计算密集型方法（例如计算流体动力学）相比。这种方法进一步扩展为一个完整的配置分析工具，它结合了基本机身几何形状的影响。而且，使用新空气动力学工具生成的数据开发了基于深度学习的替代模型，该工具可以表征无人机的非线性空气动力学性能。该模型的主要新颖之处在于，它可以仅使用几何参数来预测无人机配置的空气动力学特性，而无需其他计算空气动力学分析工具所需的任何特殊输入数据或预处理阶段。获得的黑盒函数可以计算无人机在毫秒量级的大攻角范围内的性能，而计算流体动力学解决方案在类似的计算环境中需要几天/几周的时间。即使对于模型训练中未使用的具有不同翼型的配置，空气动力学模型预测显示与数值数据几乎 1-1 重合。开发的模型为设计优化研究提供了一个功能强大的空气动力学求解器，如通过说明性轮廓设计示例所展示的。