Morphing wings are expected to have transformative impact on future transportation and energy systems. To enable analysis and optimization of morphing wings, efficient numerical models are critically important. In this work, we present an accurate and tractable reduced-order model embedded in a genetic-algorithm-based optimization framework. The modeling and optimization framework allows concurrent aerostructural design and flight trajectory optimization of morphing wings considering complete flight missions. The approach is demonstrated on a camber-morphing wing airborne wind energy (AWE) system. The system’s power production capability is improved by enabling wing shape changes, and thus adaptation of the aerodynamic properties through morphing at different flight conditions and operating modes. The results of this study highlight the potential of the proposed modeling and optimization approach: 1) the power production capability of the investigated AWE system is improved by 46.0% compared to a sequentially optimized wing design; and 2) by exploiting camber morphing to adapt the aerodynamic properties of the wing at different flight conditions, the power production is further increased by 7.8%.

变形翼有望对未来的运输和能源系统产生变革性的影响。为了能够分析和优化变形机翼，高效的数值模型至关重要。在这项工作中，我们提出了一个准确且易于处理的降阶模型，该模型嵌入了基于遗传算法的优化框架中。建模和优化框架允许同时考虑到完整的飞行任务的变形机翼的航空结构设计和飞行轨迹优化。该方法在弧形机翼机载风能（AWE）系统上得到了证明。通过改变机翼形状来改善系统的发电能力，从而通过在不同的飞行条件和操作模式下变形来适应空气动力学特性。这项研究的结果突出了所提出的建模和优化方法的潜力：1）与顺序优化的机翼设计相比，所研究的AWE系统的发电能力提高了46.0％；2）通过利用弯度变形来适应机翼在不同飞行条件下的空气动力学特性，发电量进一步提高了7.8％。