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Integrating UAS swarming with formation drag reduction

机译：将UAS集群与编队减阻相结合

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In the seminal research into simulated swarming, Reynolds developed a methodology that guided a flock of agents using just three rules: collision avoidance, swarm centering, and velocity matching. By modifying these rules, an algorithm is created and applied to unmanned aircraft systems (UAS) so each aircraft in a "swarm" maintains a precise position relative to the preceding aircraft. Each aircraft experiences a decrease in induced aerodynamic drag, thus reducing overall fuel consumption, increasing range and endurance and expanding UAS utility. A simulation demonstrates the feasibility of the drag reduction swarm using a drag benefit map constructed from extant research. Due to both agent interaction and wind gust variability, the optimal position for drag reduction presented a severe collision hazard, and drag reduction was much more sensitive to lateral (wingtip) position than longitudinal (stream-wise) position. By increasing longitudinal spacing, the collision hazard was acceptably reduced. For one scenario, compared to a single UAS, a swarm of 10 aircraft demonstrated a 9.7% reduction in total aerodynamic drag, decreased fuel consumption by 14.2% and an increased endurance by 14.5%.
机译：在对模拟群的开创性研究中，雷诺兹开发了一种方法，该方法仅使用三个规则来引导一群特工：避免碰撞，群居中和速度匹配。通过修改这些规则，将创建一种算法并将其应用于无人驾驶飞机系统（UAS），以便处于“群居”状态的每架飞机都相对于前一架飞机保持精确的位置。每架飞机的风阻都减小了，从而降低了总体油耗，增加了航程和续航能力，并扩大了UAS的使用范围。仿真通过现有研究构建的减阻效益图证明了减阻群的可行性。由于代理人的相互作用和阵风的可变性，减阻作用的最佳位置存在严重的碰撞危险，减阻作用对横向（翼尖）位置比纵向（沿流向）位置更加敏感。通过增加纵向间距，可以将碰撞危险降低到可以接受的程度。在一种情况下，与单个UAS相比，有10架飞机的机群显示出总空气阻力减少了9.7％，燃油消耗量减少了14.2％，耐力提高了14.5％。

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