Jeremy Laliberte

The future performance of the Rideau Canal Skateway (RCS) is increasingly uncertain under climate change. This study evaluates historical trends in RCS performance and projects its response to future climate forcing, using CMIP6 projections across three Shared Socio-economic Pathways (SSP1-2.6, SSP2-4.5 and SSP3-7.0) to drive semi-empirical season performance models and a physics-based ice growth model calibrated against 52 seasons of observed RCS start dates. The historical record (1970-71 through 2025-26) indicates that intervention activities, such as surface flooding and snow management, advance the RCS start date by a median of 20 days relative to natural congelation ice growth. However, the observed start dates are shifting later at approximately twice the rate of the natural congelation baseline, eroding this advance by 0.23 days per year. Projected to 2100, managed operations are estimated to attain the 30 cm criterion in 100%, 99% and 73% of seasons across the three pathways, against 82%, 36% and 31% for natural congelation conditions. The RCS start dates shift into later January with increasing median (P50) radiative forcing and under SSP3-7.0 the 30 cm ice thickness criterion is last attained in 2086. These results identify where operational and maintenance practices may be refined, and adaptation strategies explored, to promote RCS resilience and support decision making under uncertainty.

The increasing adoption of remotely piloted aircraft systems in urban areas will require a quantitative assessment of collision risks with other air traffic. Current approaches for assessing the effectiveness of detect and avoid systems may have limitations in both accounting for the influence of traffic coordination in controlled urban airspaces and understanding how strategic and tactical mitigations can reduce risks. By quantifying and combining the effect of these factors with a traffic density analysis, this paper proposes a new methodology to quantify collision risk and improve mitigation capability estimates by calculating a metric called the weighted risk ratio. The authors' findings indicate that short-range noncooperative detect and avoid systems, when used as the only means of tactical mitigation, have minimal effect on decreasing the collision risks in urban scenarios. Consequently, achieving adequate mitigation for future urban air mobility flights necessitates a combination of long-range noncooperative and cooperative sensors, along with strategic mitigations and traffic coordination. Finally, the developed methodology is demonstrated through a case study to highlight the quantitative and variable impact of various mitigation strategies to ease the integration of emerging technologies into the shared airspace with traditional aviation.

Wind flow patterns around building features are crucial for the safe operation of drones and urban air mobility missions. Fixed anemometer stations and computational fluid dynamics simulations face limitations in measuring airflow behind buildings. The use of a drone-mounted ultrasonic anemometer addresses these limitations by providing high-resolution measurements, flexibility in positioning, and the ability to capture real-time localized wind patterns in complex urban environments. This paper presents a wind tunnel study using full factorial design of experiment on a low-cost drone-mountable ultrasonic anemometer to be used to measure urban wind fields. Measurements under uniform flow conditions, with reference instruments, validate the anemometer's performance in airspeed and direction measurements. The study also explores the anemometer's capabilities in measuring wake characteristics behind a cylinder in turbulent flow. The results demonstrate that using compact ultrasonic anemometers for drone-based anemometry in urban environments can yield adequate measurements within specific angle of attack ranges.

The integration of remotely piloted aircraft systems (RPAS) into shared airspace requires a thorough risk analysis. Specific operations risk assessment (SORA) is a widely adopted approach by international civil aviation authorities to guide RPAS operators in evaluating risks associated with their mission. A critical step in the SORA process is analyzing the airspace where the operation will take place, which requires knowledge of the intruder aircraft's flight characteristics as well as the airspace model. This paper proposes a methodology for developing a statistical airspace model using historical aircraft track data collected in the Winnipeg Manitoba Flight Information Region. The developed methods include data cleaning routines, Kalman filters for track smoothing, and Bayesian networks for synthetic track generation, following an approach similar to that employed by the Massachusetts Institute of Technology Lincoln Lab. Additionally, the developed methodology allows for the analysis of specific models by altitude or aircraft type. The methods presented were subsequently adjusted for a comprehensive analysis spanning across Canada's diverse airspace. The initial statistical model, derived from Canada-wide data, is currently accessible to the public via the National Research Council's GitHub repository [ https://github.com/nrc-cnrc/Canadian-Airspace-Models ].