Search for: All records

Flight safety is central to the certification process and relies on assessment methods that provide evidence acceptable to regulators. For drones operating as Advanced Air Mobility (AAM) platforms, this requires an accurate representation of the complex wind fields in urban areas. Large-eddy simulations (LES) of such environments generate datasets from hundreds of gigabytes to several terabytes, imposing heavy storage demands and limiting real-time use in simulation frameworks. To address this challenge, we apply a Convolutional Autoencoder (CAE) to compress a 40 m-deep section of an LES wind field. The dataset size was reduced from 7.5 GB to 651 MB, corresponding to a 91% compression ratio, while maintaining maximum magnitude errors within a few tenths of the spatio-temporal wind velocity. Predicted vehicle responses showed only marginal differences, with close agreement between the full LES and CAE reconstructions. These findings demonstrate that CAEs can significantly reduce the computational cost of urban wind field integration without compromising fidelity, thereby enabling the use of larger domains in real-time and supporting efficient sharing of disturbance models in collaborative studies. 

Urban wind, and particularly turbulence present in the roughness zone near structures, poses a critical challenge for next-generation drones. Complex flow patterns induced by large buildings produce significant disturbances that the vehicle must reject at low altitudes. Traditional turbulence models, such as the von Kármán model, underestimate these localized effects, compromising flight safety. To address this gap, we integrate high-resolution time and spatially varying urban wind fields from Large Eddy Simulations into a flight dynamics simulation framework using vehicle plant models based on configuration geometry and commonly deployed Ardupilot control laws, enabling a detailed analysis of drone responses in urban environments. Our results reveal that high-risk flight zones can be systematically identified by correlating drone response metrics with the spatial distribution of Turbulent Kinetic Energy (TKE). Notably, maximum g-loads coincide with abrupt TKE transitions, underscoring the critical impact of even short-lived wind fluctuations. By coupling advanced computational fluid dynamics with a real-time vehicle dynamics model, this work establishes a foundational methodology for designing safer and more reliable advanced air mobility platforms in complex urban airspaces. This work distinguishes itself from the existing literature by incorporating an efficient vortex lattice aerodynamic solver that supports arbitrary fixed-wing drone platforms through the simple specification of planform geometry and mass properties, and operating full-flights throughout a time and spatially varying urban wind field. This framework enables a robust assessment of stability and control for a wide range of fixed-wing drone platforms operating in urban environments, with delivery drones serving as a representative and practical use case. 

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research. 

Abstract The concept of Advanced Air Mobility involves utilizing cutting-edge transportation platforms to transport passengers and cargo efficiently over short distances in urban and suburban areas. However, using simplified atmospheric models for aircraft simulations can prove insufficient for modeling large disturbances impacting low-altitude flight regimes. Due to the complexities of operating in urban environments, realistic wind modeling is necessary to ensure trajectory planning and control design can maintain high levels of safety. In this study, we simulate the dynamic response of a representative advanced air mobility platform operating in wing-borne flight through an urban wind field generated using Large Eddy Simulations (LES) and a wind field created using reduced-order models based on full-order computational solutions. Our findings show that the longitudinal response of the aircraft was not greatly affected by the fidelity of the LES models or if the spatial variation was considered while evaluating the full-order wind model. This is encouraging as it indicates that the full LES generation of the wind field may not be necessary, which decreases the complexity and time needed in this analysis. Differences are present when comparing the lateral response, owing to the differences in the asymmetric loading of the planform in the full and reduced order models. These differences seen in the lateral responses are expected to increase for planforms with smaller wing loadings, which could pose challenges. Additionally, the response of the aircraft to the mean wind field, the temporal average of the full order model, was misrepresentative in the longitudinal response and greatly under-predicted control surface activity, particularly in the lateral response. 

Results of a previous aerodynamics study conducted over a wing that exhibits the Prandtl Bell Spanload were implemented into a simulation environment with the intent of studying unique flight characteristics that are theorized to be presented by this spanload. However, early simulations over the dynamics show that the yawing moment due to roll rate is of higher effect than the yaw moment due to aileron deflection angle. This over-prediction of the roll-yaw coupling term has been called into question. A new method is to be tested, which implements a compact vortex-lattice (CVLM) formulation, to show the difference between the flight dynamics predicted by this new method and the stability derivative method currently in use. The analysis utilizes two initial conditions to test the differences as the dynamics propagate through time. The first, a large initial bank angle, leads to the stabiltiy derivative method diverging while the CVLM results show this to not be the case. The second condition, a wind-field representative of a stable nocturnal boundary layer over the ground, leads to much more agreement between methods before divergence occurs due to a velocity higher than that of the stability derivative linearization point. It is then agreed that, since CVLM cannot predict stall effects and other nonlinear flight regions, a hybrid approach is proposed that takes advantage of the roll-yaw coupling prediction of the CVLM and the range of condition available to the stability derivative method. 

The study of water adsorption on mineral surfaces is fundamental to soil and atmospheric science. The physiochemical effects of mineral aerosol influence atmospheric chemistry and climate as well as soil moisture. Iron-containing minerals are abundant on Earth as well as Mars, where the existence and location of surface water is uncertain. Experimental water adsorption measurements have been conducted as a function of relative humidity (RH) on goethite (α-FeO(OH)), a common component of atmospheric mineral dust and Martian crustal material. Water adsorption on goethite was monitored using Horizontal Attenuated Total Reflectance Fourier Transform Infrared (HATR-FTIR) spectroscopy equipped with a flow cell and quantified according to Beer’s Law. Water content as a function of RH was analyzed using type II adsorption isotherms to model multilayer water adsorption. Brunauer Emmet and Teller (BET), Frenkel Halsey and Hill (FHH) and Freundlich adsorption isotherms were applied to model the experimental data. Monolayer water coverage was found to be 4.758x1013 molecules/cm2 based on BET analysis. FHH Adsorption Activation Theory (AT) was used to predict cloud condensation nuclei (CCN) activity of goethite under Earth’s atmospheric conditions. Results aid in the effort of climate prediction on Earth as well as locating liquid water on Mars’ surface.