The American Institute of Aeronautics and Astronautics (AIAA) is a professional society for the field of aerospace engineering. The AIAA is the U.S. representative on the International Astronautical Federation and the International Council of the Aeronautical Sciences. In 2015, it had more than 30,000 members among aerospace professionals worldwide (a majority are American and/or live in the United States).
Aerial manipulation (AM) expands UAV capabilities beyond passive observation to contact-based operations at high altitudes and in otherwise inaccessible environments. Although recent advances show promise, most AM systems are developed in controlled settings that overlook key aerodynamic effects. Simplified thrust models are often insufficient to capture the nonlinear wind disturbances and proximity-induced flow variations present in real-world environments near infrastructure, while high-fidelity CFD methods remain impractical for real-time use. Learning-based models are computationally efficient at inference, but often struggle to generalize to unseen condition. This paper combines both approaches by integrating a physics-based blade-element model with a learning-based residual force estimator, along with a rotor-speed allocation strategy for disturbance compensation, resulting in a unified control framework. The blade-element model computes per-rotor aerodynamic forces under wind and provides a refined feedforward disturbance estimate. A learning-based estimator then predicts the residual forces not captured by the model, enabling compensation for unmodeled aerodynamic effects. An online adaptation mechanism further updates the residual-force prediction and rotor-speed allocation jointly to reduce the mismatch between desired and realized thrust. We evaluate this framework in both free-flight and wall-contact tracking tasks in a simulated near-wall wind environment. Results demonstrate improved disturbance estimation and trajectory-tracking accuracy over conventional approaches, enabling robust wall-contact execution under challenging aerodynamic conditions.
High-altitude, multi-spectral, aerial imagery is scarce and expensive to acquire, yet it is necessary for algorithmic advances and application of machine learning models to high-impact problems such as wildfire detection. We introduce a human-annotated dataset from the NASA Autonomous Modular Sensor (AMS) using 12-channel, medium to high altitude (3 - 50 km) aerial wildfire images similar to those used in current US wildfire missions. Our dataset combines spectral data from 12 different channels, including infrared (IR), short-wave IR (SWIR), and thermal. We take imagery from 20 wildfire missions and randomly sample small patches to generate over 4000 images with high variability, including occlusions by smoke/clouds, easily-confused false positives, and nighttime imagery. We demonstrate results from a deep-learning model to automate the human-intensive process of fire perimeter determination. We train two deep neural networks, one for image classification and the other for pixel-level segmentation. The networks are combined into a unique real-time segmentation model to efficiently localize active wildfire on an incoming image feed. Our model achieves 96
One of the main challenges limiting the maximum forward speed of high-speed rotorcraft is the development of reverse flow on retreating blades. Flow separation at the sharp aerodynamic leading edge during reverse flow leads to negative lift, high drag, high pitching moment, and a pitching moment impulse. The study experimentally investigates the effectiveness of a sinusoidal trailing edge as a passive method for the control of reverse flow, under static pitching conditions. Three NACA 0015 two-dimensional blades were evaluated under both reverse and forward flow conditions. A baseline blade with a straight trailing edge and two modified blades with sinusoidal trailing edges, each having a spatial amplitude of 10
Energy harvesting from vortex-induced vibrations is a promising technology that relies on the vibrations of bluff bodies due to the vortex shedding phenomenon. Increasing the vibration amplitude at a given freestream kinetic energy is equivalent to enhancing the efficiency of the harvesting device. In this study, we assess the potential of active flow control (AFC) to amplify force fluctuations. Pressurized air is ejected alternately from the top and bottom parts of a cylinder. Through experimentation in a low-speed wind tunnel (Re=8000), we show that the magnitude of lift fluctuations can be enhanced by up to a factor of three compared to the unforced flow when the actuation is aligned with the natural vortex shedding frequency. Particle image velocimetry measurements indicate that this is caused by strong streamline bending, whereas at a higher forcing frequency, fluctuations are reduced because the shedding instability is disrupted in each cycle. This work elaborates on the physics of slot blowing for amplitude enhancement, and results suggest that a significant increase in the dynamic load acting on a cylinder can be achieved with carefully chosen AFC parameters, thereby promoting future energy harvesting applications.
