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Unmanned aerial vehicles (UAVs), the prodigy of remotely piloted vehicles, largely used for military purposes, are slowly gaining momentum in our civil lives. Some of the applications of UAVs go beyond surveillance or photography, we already have drones that are employed by farmers to monitor crops and are used in the solar industry for thermographic studies. By 2026, UAVs for both consumer and corporate applications are expected to impact the United States annual GDP by around $31 billion to $46 billion. The increasing value of the drone industry is pushing the limits of evaluating and improving the aerodynamics of UAVs for enhanced range and extreme maneuvers.
In the fifteenth century, the great Leonardo da Vinci had envisioned a helix-shaped vertical take-off vehicle, the Aerial Screw. In 2020, students from across the world were challenged to reimagine this aerial screw design at the 37th annual design competition by the Vertical Flight Society (VFS). After two years of determination and hard work, the UAV named Elico won the design prize, as it was rooted in da Vinci’s design and looked functional from the CFD simulations. Later, a working model, Crimson Spin, was produced to validate and improve Elico’s design, and to study the formation of vortex edge during the lift mechanism. This small UAV flies with the lift produced by the four-spiral-shaped blades and was unveiled at the VFS’s 2022 Transformative Vertical Flight Conference. Another example where aerodynamic studies have helped improve the UAV design is the fixed-wing UAV, Mojave, which can carry as many as 16 Hellfire missiles. Its strong undercarriage offers improved aerodynamics for short takeoffs from rugged runways and can provide fire support for about nine hours.
At autonomous drone racing competitions, it is often observed that at high speeds, these vehicles become unstable, it is difficult to predict their aerodynamics, and they are more prone to crashes. A team of aerospace engineers at MIT developed an algorithm to help drones fly as fast as possible around obstacles without crashing. Drones trained with this new algorithm were noted to move at a 20 percent faster pace than those trained with conventional algorithms. According to Ezra Tal, a graduate student at the department of aeronautics and astronautics at MIT, “ At high speeds, there is intricate aerodynamics that is hard to simulate, so we use experiments in the real world to fill in those black holes to find, for instance, that it might be better to slow down first to be faster later.” This is an excellent example to reinforce the fact that simulation alone cannot replace real-world testing because there are multiple real-world factors that are missed out on or are difficult to include due to limited human hours and skills. A hybrid electric UAS (Unmanned Aerial System), HAMR, is aerodynamically designed to spread its weight across its wider body and is stacked axially to reduce parasitic drag, promising to offer 3.5 flight hours depending on the payload. The fuel-powered propulsion system of HAMR is computer-controlled, and the batteries play their part whenever required. The line replaceable units (LRUs) in this drone’s system allow flight in harsh environments with minimal maintenance compared to traditional gas engines. For commercial operations, the system carries sensors such as infrared cameras and LiDAR; and for defense applications, electro-optical, infrared, laser, and communication systems are installed. Here we see that electrification hasn’t spared the UAVs either.
From fighting wars to forecasting weather conditions, drones are impacting our society to an unforeseeable extent. With easy access to this technology, drones can largely be used for applications where human involvement is dangerous and hostile. For improved flight hours and to withstand harsh environmental conditions, the aerodynamics of UAVs needs to be experimented with using both CFD simulations and real world-testing. A few years from now, it is possible for UAVs to replace human delivery executives. Let’s wait and watch!