Embry–Riddle Aeronautical University, Daytona Beach

The aeroacoustic impact of ground proximity on an eVTOL propeller in hover and edgewise flight is examined using a multi-fidelity framework. Delayed-Detached Eddy Simulations (DDES) in OpenFOAM, coupled with PSU-WOPWOP, for high-fidelity acoustic predictions, and CDI-CHARM, a free-vortex panel-method code, as a lower fidelity approach. Extensive validation of the numerical simulations against experiments was performed. Performance metrics and flow-field analyses were examined prior to conducting the acoustic analysis. In-ground-effect (IGE) conditions, here defined as rotor operation within a few radii of a rigid ground plane, modify wake dynamics and alter tonal and overall sound levels, producing around similar to 5 dB SPL increases beneath the rotor. CHARM reproduces the main tonal trends observed in DDES cannot predict broadband content. We also evaluate approaches for treating ground reflections, including the Method of Images (MOI), and introduce a permeable "upside-down T" acoustic surface that captures reflected waves without requiring MOI. These findings clarify rotor-ground acoustics and offer practical guidance for permeable acoustic surface selection in high-fidelity simulations.

Abstract We develop a theoretical analysis of the sea-ice thickness distribution equation as a nonlinear transport problem in the extended phase space of horizontal position, time, and ice thickness. The thickness distribution is first formulated rigorously as a Borel measure, obtained as the normalized pushforward of planar area measure under the ice-thickness field. We then study the sea-ice thickness distribution equation with horizontal advection, thermodynamic growth or melting, and a nonlocal mechanical redistribution function. Due to the presence of thermodynamic the governing equation is represented by a conservation law in the extended phase space. Using extended characteristics, we derive a Green-kernel/Duhamel representation including initial and inflow boundary contributions, and establish local well-posedness criteria based on transport regularity, measurability, Lipschitz continuity, and compatibility in the space of absolute integrable functions. We examine classical ridging-type redistribution models and show how regularization of the open-water Dirac term and of the velocity-dependent ridging coefficient is required for the hypotheses of the well-posedness theorem. A Fourier expansion in the thin-ice, divergence-free regime reveals the infinite-dimensional coupled structure generated by the nonlocal dependence on the cumulative distribution. We further derive reduced criteria for the evolution, displacement, and possible splitting of peaks in the thickness profile, including conditions for the transition from unimodal to bimodal distributions under thermodynamic and mechanical forcing. Finally, we formulate the coupled sea-ice thickness, momentum, and elastic-viscoplastic rheology as a quasi-linear evolution problem and discuss local strong solutions, energy bounds, possible global continuation, and mechanisms of blow-up. The analysis identifies the mathematical assumptions needed for predictive sea-ice thickness models and clarifies the limitations imposed by singular open-water states, non-Lipschitz ridging closures, and derivative-loss mechanisms.

Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in search and rescue (SAR) missions to rapidly locate survivors. However, deploying autonomous swarms in low-altitude airspace shared with crewed rescue aircraft poses significant algorithmic and safety challenges. Following PRISMA 2020 guidelines, this systematic review synthesizes 44 peer-reviewed studies (2022–2026) to evaluate the literature across algorithmic optimization, reality-gap limitations, tactical deconfliction, and validation maturity. The synthesis reveals a consistent trend toward decentralized swarms, driven by Deep Reinforcement Learning in dynamic environments and by bio-inspired metaheuristics for static coverage. Despite these algorithmic advancements, the literature exhibits a severe reality gap: approximately 86% of evaluated models rely exclusively on idealized software simulations, abstracting away critical constraints like communication denial and sensor noise. Furthermore, most models assume uncontested airspace and lack the Manned–Unmanned Teaming (MUM-T) and tactical deconfliction protocols necessary for safe coexistence with rescue helicopters. To achieve true operational readiness within the critical “Golden 72 Hours” of disaster response, the discipline must transition toward hardware-in-the-loop and physical field trials, natively integrating airspace deconfliction into core swarm optimization loops.

This study examines the impact of feedback tone and feedback order on flight instructors’ evaluations of student performance in an aviation training context. Using a 2×2 mixed factorial design, 40 certified flight instructors reviewed fictitious training records and rated students based on feedback that varied in tone (positive vs. negative) and order (positive-first vs. negative-first). Results showed that positive feedback significantly influenced evaluations across all measured dimensions, while feedback order had no significant effect, challenging assumptions about primacy and recency effects. These findings highlight the role of positive reinforcement in shaping instructor evaluations and suggest that structured training environments can reduce cognitive bias. Recommendations include implementing structured feedback training, limiting access to previous instructors’ qualitative feedback, and employing AI tools to ensure balanced feedback. This research offers insights into improving evaluation practices in aviation and other high-stakes training environments.

Vehicular Ad Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS) by enabling real-time vehicle communication for safety and traffic management. However, the open and decentralized nature of VANETs makes them vulnerable to False Information Attacks (FIA), where malicious vehicles disseminate fabricated data such as fake congestion alerts or incorrect speed information. This paper presents a lightweight and infrastructure-free framework for detecting FIA using an unsupervised machine learning approach based on the Isolation Forest algorithm. Unlike existing methods that require roadside units (RSUs), labeled datasets, or computationally intensive network simulators, the proposed framework operates using a small set of behavioral features extracted from vehicle beacon messages. The system is implemented entirely in Python and evaluated on a synthetically generated dataset designed to emulate realistic VANET conditions. Experimental results demonstrate an accuracy of 94.2%, precision of 86.1%, recall of 77.5%, and an F 1 -score of 81.6%. The results show that the proposed framework achieves competitive detection performance while maintaining low computational overhead, making it suitable for deployment on resource-constrained onboard units (OBUs) in real-world vehicular networks.