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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.

As part of the complete solution to deal with atmospheric in-flight icing on unmanned aerial vehicles (UAV), a path planner is a valuable tool for finding an optimal path for accomplishing UAV missions. When considering icing conditions, the planner manages areas with icing risk. Together with an electro-thermal ice protection system (IPS), the path planner can optimize energy consumption by comparing energy consumed flying through the cloud or around it, as the UAV can now more safely pass through the ice. The UAV’s aerodynamic stability is also considered by meeting lift requirements, producing enough thrust, and having battery capacity left. These are constraints in the planner to ensure that the UAV can complete its mission. Benchmark icing cases are constructed to validate that the path planner performs as intended.

In-flight icing can result in severe aerodynamic performance penalties for unmanned aerial vehicles. It is therefore important to understand to which extent ice will build up on fixed-wing unmanned aerial vehicles wings and empennages, namely rudder and elevator, and how this ice will impact the aerodynamic performance and limits the flight envelope. This work investigates numerically icing effects on wing and empennage over a wide range of icing parameters. This is conducted using the icing CFD code FENSAP-ICE on the Maritime Robotics PX-31 Falk UAV. Therefore, the 2D profiles of these airfoils, which are RG-15 for the wing and SD8020 for rudder and elevator, are investigated. The investigated angles of attack are between –5° and 14° in 0.5° increments. Furthermore, the icing conditions are chosen according to the FAA CS 25 Appendix C for continuous maximum and intermittent maximum icing.

In-flight atmospheric icing is a significant threat to the use of unmanned aerial vehicles (UAVs) in adverse weather. The propeller of the UAV is especially sensitive to icing conditions, as it accumulates ice at a faster rate than the wings of the UAVs. Ice protection systems can be developed to counteract the danger of icing on the propeller of UAVs. In this study, the influence of different meteorological conditions on a propeller of a UAV is analyzed for a UAV with a wingspan of a few meters. The ice accretion and the performance degradation and the required anti-icing heat fluxes have been calculated using numerical methods with ANSYS FENSAP-ICE. This analysis has been used to evaluate the critical conditions for the operation of a UAV in icing conditions and the design of a thermal IPS system for a propeller. The highest ice mass has been found at a temperature of −10 °C and an MVD of 20 μm in intermittent maximum icing conditions.

Icing is a severe hazard to aircraft and in particular to unmanned aerial vehicles (UAVs). One important activity to understand icing risks is the prediction of ice shapes with simulation tools. Nowadays, several icing computational fluid dynamic (CFD) models exist. Most of these methods have been originally developed for manned aircraft purposes at relatively high Reynolds numbers. In contrast, typical UAV applications experience Reynolds numbers an order of magnitude lower, due to the smaller airframe size and lower airspeeds. This work proposes a set of experimental ice shapes that can serve as validation data for ice prediction methods at low Reynolds numbers. Three ice shapes have been collected at different temperatures during an experimental icing wind tunnel campaign. The obtained ice shapes represent wet (glaze ice, −2 °C), mixed (−4 °C), and dry (rime ice, −10 °C) ice growth regimes. The Reynolds number is between Re=5.6…6.0×105, depending on the temperature.

Urban air mobility (UAM) is a fast-growing industry that utilizes electric vertical take-off and landing (eVTOL) technologies to operate in densely populated urban areas with limited space. However, atmospheric icing serves as a limitation to its operational envelope as in-flight icing can happen all year round anywhere around the globe. Since icing in smaller aviation systems is still an emerging topic, there is a necessity to study icing of eVTOL rotors specifically. Two rotor geometries were chosen for this study. A small 15-inch rotor was selected to illustrate a multirotor UAV drone, while a large 80-inch rotor was chosen to represent a UAM passenger aircraft. The ice accretion experiments were conducted in an icing wind tunnel on the small 15-inch rotor. The icing simulations were performed using FENSAP-ICE. The ice accretion simulations of the 15-inch rotor sections at –5 °C show a large, rather streamlined ice shape instead of the expected glaze ice characteristics.

Atmospheric in-flight icing poses a challenge to all aircraft including unmanned aerial vehicles (UAVs). Aircraft should avoid icing conditions unless they have ways of mitigating the negative effects of icing, e.g., if they are equipped with an ice protection system (IPS). When de-icing systems are used, a certain amount of ice is allowed to accumulate before it is removed. This intercycle ice deteriorates the aerodynamics by reducing the lift, adding mass, and increasing the drag. This study combines the energy that is required to compensate for the added drag of intercycle ice shapes with the energy required for a wing IPS and compares the energy needs for different IPS operations. Two different kinds of intercycle ice shapes are simulated numerically using FENSAP-ICE, one ice shape that would accrete on an unprotected wing and one ice shape that would accrete when using a parting strip, a continuously heated element at the leading edge.

Unmanned aerial vehicles (UAVs) are an emerging technology with a large variety of commercial and military applications. In-flight icing occurs during flight in supercooled clouds or freezing precipitation and is a potential hazard to all aircraft. In-flight icing on UAVs imposes a major limitation on the operational envelope. This report describes the unsettled topics related to UAV icing. First, typical UAV applications and the general hazards of icing are described. Second, an overview of the special technical characteristics of icing on autonomous and unmanned aircraft is given. Third, the operational challenges for flight in icing conditions are discussed. Fourth, technologies for ice protection that mitigate the icing hazard are introduced. Fifth, the tools and methods required to understand UAV icing and to develop aircraft with cold-weather capabilities are presented. Finally, an assessment of the current and future regulations regarding icing on UAVs is provided.

Atmospheric icing is a key challenge to the operational envelope of medium-sized fixed-wing UAVs. Today, several numeric icing codes exist, that all have been developed for general aviation applications. UAVs with wingspans of several meters typically operate at Reynolds numbers an order of magnitude lower than commercial and military aircraft. Therefore, the question arises to what extent the existing codes can be applied for low-Reynolds UAV applications to predict ice accretion. This paper describes an experimental campaign at the Cranfield icing wind tunnel on a RG-15 and a NREL S826 airfoil at low velocities (25-40m/s). Three meteorological icing conditions have been selected to represent the main ice typologies: rime, glaze, and mixed ice. Each case has been run at least twice in order to assess the repeatability of the experiments. Manual ice shape tracings have been taken at three spanwise locations for each icing case.