航母尾迹流场对舰载机的着舰有较大影响，所以需要对其流场特点进行研究，分析不同状态下舰载机气动特性的变化。采用嵌套网格技术对航母处于垂荡状态下无人机的着舰进行了模拟。首先，利用SFS2舰船进行数值计算，验证了舰船流场的数值模拟方法。然后，对比了单独无人机定常与非定常计算结果，表明所建立的嵌套网格适用于无人机流场的模拟。接着，对航母单相流和两相流的流场结果进行了分析，结果显示甲板下方的流动对甲板上方流场没有大的影响。因此，忽略了水的影响只对航母在空气流场中的特性进行研究，结果表明航母尾迹非定常特性明显，静止航母下滑轨迹上的速度均处于周期性波动状态，且波动幅值随着远离航母而逐渐衰减；而在垂荡情况下航母尾迹变得更加紊乱，水平方向速度波动的周期性减弱，但垂向速度的波动幅值进一步增大。对于静止航母，无人机在不同时刻着舰气动特性的变化也存在差异；当航母处于垂荡状态时，无人机的升力和俯仰力矩在短时间内会有更大的波动。
The major influence of ship awake on the landing for the carrier-based aircraft calls for studies on the characteristics of its flow field, and the analyses of the aerodynamic change of carrier-based aircraft in different airwakes. Thus, adopting overset mesh technique, a numerical simulation of UAV's landing in the heave of aircraft carrier is carried out. Firstly, the appropriate calculation method for the ship flow field is verified by comparing the simulation results of SFS2 with the experiment data. Secondly, by comparing the flow fields of single UAV in steady and unsteady conditions, the feasibility of overset mesh method for the landing of the UAV is verified. Next, the velocities of aircraft carrier's flow field at single phase flow and two-phase flow are compared. The results indicate that the flow under the deck does not significantly affect the velocity above the deck. Thus, the influence of water for aircraft carrier can be neglected and only the flow field in air is analyzed. The simulations show that the airwake of the aircraft carrier is significantly unsteady; the velocities of the landing track for a stationary ship shows more periodic fluctuation; and the fluctuation amplitude weakens with the increase of the distance from the ship. For the heave case, ship airwake becomes more complicated that the the periodic fluctuation of horizontal velocity fades, but the fluctuation amplitude of vertical velocity strengthens significantly. Meanwhile, for the stationary ship, the aerodynamic change of the UAV is different when landing at different times; for the ship in the heave motion, the lift and pitch moments of the UAV will demonstrate more fluctuations in a short period of time.
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