基于遗传算法的无人机旋翼气动-噪声多目标优化

      随着无人机用途越来越广泛，能够垂直起降的多旋翼无人机的市场规模逐渐扩大，人们开始关注旋翼的设计，因为它对多旋翼无人机的气动效率和噪声有很大影响。然而，传统的旋翼设计高度依赖设计人员的经验，设计工作耗时长，因此需要开发一个基于优化算法的旋翼优化设计框架。从悬停状态下的气动性能和噪声角度出发，以气动效率（FM）和总声压级（OASPL）为优化目标，对旋翼的桨叶形状以及桨叶数进行优化设计。优化过程中使用定常CFD 仿真和FW-H 方程对旋翼设计进行评估，优化完成后通过非定常CFD 仿真对最优旋翼进一步分析。优化变量为桨叶的径向扭转角分布，优化工作在旋翼产生相同拉力的约束下进行。
      基准旋翼采用开源程序JBLADE 进行设计，使用矩形桨叶和线性扭转角分布。与基准旋翼相比，最优旋翼叶尖区域的扭转角较小，将叶尖处的载荷重新分配到了叶中/叶根区域，削弱了叶尖损失的影响，从而提升了FM。CFD 仿真结果表明，最优旋翼延迟了桨叶吸力面上的流动分离，且叶尖涡强度更低。
      桨叶数是旋翼设计中的一个重要设计参数，影响旋翼的气动效率和噪声。我们选择了在多旋翼飞行器上广泛应用的2 – 6 叶桨作为研究对象，分别对其桨叶扭转角分布进行优化设计，然后对比分析桨叶数的影响。桨叶数的增加会减小每个桨叶上的载荷，减弱叶尖损失；会降低转速，降低桨叶截面的雷诺数，降低FM；会增加桨涡干涉发生的频率，降低FM。在三个因素的共同作用下，当桨叶数从2增加到4 时，FM 值持续增加，在桨叶数为4 时达到最高值，之后桨叶数继续增加时，FM 几乎保持不变。随着桨叶数的增加，旋翼转速和每个桨叶上载荷的降低分别减小了厚度噪声和载荷噪声，因此OASPL 持续降低。

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