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The issue of greenhouse gas (GHG) emissions from the transportation sector is widely acknowledged. Recent years have witnessed a push towards the electrification of cars, with many considering it the optimal solution to address this problem. However, the substantial battery packs utilized in electric vehicles contribute to a considerable embedded ecological footprint. Research has highlighted that, depending on the vehicle's size, tens or even hundreds of thousands of kilometers are required to offset this environmental burden. Human-powered vehicles (HPVs), thanks to their smaller size, are inherently much cleaner means of transportation, yet their limited speed impedes widespread adoption for mid-range and long-range trips, favoring cars, especially in rural areas. This paper addresses the challenge of HPV speed, limited by their low input power and non-optimal distribution of the resistive forces.

A GE Aviation Systems report documents that the National Oceanic and Atmospheric Administration (NOAA) provided weather forecast data has a bias of 15 knots and a standard deviation of 13.3 knots for the 40 flights considered for the research. It also had a 0.47 bias in the temperature with a standard deviation of 0.27. The temperature errors are not as significant as the wind. There is a potential opportunity to reduce the operational cost by improving the weather forecast. The flight management system (FMS) currently uses the weather forecast, available before takeoff, to identify an optimized flight path with minimum operational costs depending on the selected speed mode. Such a flight plan could be optimum for a shorter flight because these flight path planning algorithms are very less susceptible to the accuracy of the weather forecast.

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Evaluating real-world hazards associated with perception subsystems is critical in enhancing the performance of autonomous vehicles. The reliability of autonomous vehicles perception subsystems are paramount for safe and efficient operation. While current studies employ different metrics to evaluate perception subsystem failures in autonomous vehicles, there still exists a gap in the development and emphasis on engineering requirements. To address this gap, this study proposes the establishment of engineering requirements that specifically target real-world hazards and resilience factors important to AV operation, using High-Definition Maps, Global Navigation Satellite System, and weather sensors. The findings include the need for engineering requirements to establish clear criteria for a high-definition maps functionality in the presence of erroneous perception subsystem inputs which enhances the overall safety and reliability of the autonomous vehicles.

“Hot Day”, “Tropical Day”, “Standard Day”, “Polar Day”, and “Cold Day” are part of the lexicon of the aircraft industry. These terms are generally understood to refer to specific, generally accepted characteristics of atmospheric temperature versus pressure altitude. There are also other, less well-known days, defined by their frequency of occurrence, such as “1% Hot Day”, “10% Cold Day”, or “Highest Recorded Day”. These temperature characteristics have their origins in multiple sources, including U.S. military specifications which are no longer in force.

This specification covers the general requirements for conventional AC and/or DC current carrying filter networks for the reduction of electromagnetic interference. A conventional filter is defined herein as a component containing definitive, lumped, R-L-C components and not employing distributed parameters as a required characteristic.

In-flight atmospheric icing is a severe hazard for propeller-driven unmanned aerial vehicles (UAVs) that can lead to issues ranging from reduced flight performance to unacceptable loss of lift and control. To address this challenge, a physics-based first principles model of an electric UAV propulsion system is developed and identified in varying icing conditions. Specifically, a brushless direct current motor (BLDC) based propeller system, typical for UAVs with a wing span of 1-3 meters, is tested in an icing wind tunnel with three accreted ice shapes of increasing size. The results are analyzed to identify the dynamics of the electrical, mechanical, and aerodynamic subsystems of the propulsion system. Moreover, the parameters of the identified models are presented, making it possible to analyze their sensitivity to ice accretion on the propeller blades.

The Current Icing Product (CIP; Bernstein et al. 2005) and Forecast Icing Product (FIP; Wolff et al. 2009) were originally developed by the United States’ National Center for Atmospheric Research (NCAR) under sponsorship of the Federal Aviation Administration (FAA) in the mid 2000’s and provide operational icing guidance to users through the NOAA Aviation Weather Center (AWC). The current operational version of FIP uses the Rapid Refresh (RAP; Benjamin et al. 2016) numerical weather prediction (NWP) model to provide hourly forecasts of Icing Probability, Icing Severity, and Supercooled Large Drop (SLD) Potential. Forecasts are provided out to 18 hours over the Contiguous United States (CONUS) at 15 flight levels between 1,000 ft and FL290, inclusive, and at a 13-km horizontal resolution.

Quasicrystalline (QC) coatings were evaluated as leading-edge protection materials for rotor craft blades. The QC coatings were deposited using high velocity oxy-fuel thermal spray and predominantly Al-based compositions. Ice adhesion, interfacial toughness with ice, wettability, topography, and durability were assessed. QC-coated sand-blasted carbon steel exhibited better performance in terms of low surface roughness (Sa ~ 0.2 μm), liquid repellency (water contact angles: θadv ~85°, θrec ~23°), and better substrate adhesion compared to stainless steel substrates. To enhance coating performance, QC-coated sand-blasted carbon steel was further exposed to grinding and polishing, followed by measuring surface roughness, wettability, and ice adhesion strength. This reduced the surface roughness of the QC coating by 75%, resulting in lower ice adhesion strengths similar to previously reported values (~400 kPa).

Measurements in snow conditions performed in the past were rarely initiated and best suited for pure and extremely detailed quantification of microphysical properties of a series of microphysical parameters, needed for accretion modelling. Within the European ICE GENESIS project, a considerable effort of natural snow measurements has been made during winter 2020/21. Instrumental means, both in-situ and remote sensing were deployed on the ATR-42 aircraft, as well as on the ground (ground station at ‘Les Eplatures’ airport in the Swiss Jura Mountains with ATR-42 overflights). Snow clouds and precipitation in the atmospheric column were sampled with the aircraft, whereas ground based and airborne radar systems allowed extending the observations of snow properties beyond the flight level chosen for the in situ measurements.

In the framework of the European ICE GENESIS project (https://www.ice-genesis.eu/), a field experiment was conducted in the Swiss Jura in January 2021 in order to characterize snow microphysical properties and document snow conditions for aviation industry purposes. Complementary to companion papers reporting on snow properties, this study presents an investigation on mixed-phase conditions sampled during the ICE GENESIS field campaign. Using in situ measurement of the liquid and total water content, the ice mass fraction is calculated and serves as a criteria to identify mixed-phase conditions. In the end, mixed phase conditions were identified in almost 30 % of the 3800 km long cloud samples included in the ICE GENESIS dataset. The data suggests that the occurrence of mixed-phase does not clearly depend on temperature in the 0 to -10 °C range, but varies significantly from one cloud system to another.

The RTCA SC-230 committee began working on minimum operational performance standards (MOPS) for ice crystal detection using weather radar in 2018. The resulting MOPS document will be released in 2023. This paper presents the rationale, summarizes key requirements, and discusses means of validation for an ice crystal detection function incorporated in an airborne weather radar system.

In 2017 the National Research Council of Canada developed an evaporation model for controlling engine icing tunnels in real time. The model included simplifications to allow it to update the control system once per second, including the assumption of sea level pressure in some calculations. Recently the engine icing system was required in an altitude facility requiring operation down to static temperatures of -40°C, and up to an altitude of 9.1 km (30 kft) or 30 kPa. To accommodate the larger temperature and pressure range the model was modified by removing the assumption of sea level operation and expanding the temperature range. In addition, due to the higher concentration of water vapor that can be held by the atmosphere at lower pressures, the significance of the effect of humidity on the air properties and the effect on the model was investigated.