**NEW PUBLICATION** Computational fluid dynamics (CFD) simulation methods are one of the most important design and development tools for aircraft – manned or unmanned. For the challenge of in-flight icing, special icing CFD codes have been developed such as FENSAP-ICE or LEWICE3D. These tools can simulate and predict the behavior of airfoils and aircraft in icing conditions. A common characteristic of these icing CFD tools is that they have been designed for manned aircraft — typically large passenger airplanes. When it comes to unmanned aircraft — unmanned aerial vehicles (UAVs), unmanned aerial systems (UAS), drones, or urban air mobility (UAM) vehicles — the existing icing CFD codes have significant limitations.
In a recent publication, we explore these limitations and gaps. The objective is to highlight the main challenges of icing CFD on unmanned aircraft and to suggest research steps to overcome them. In short, the main challenge is related to the low Reynolds numbers at which unmanned aircraft operate. Existing icing tools often use models that are based on experiments or are validated at high Reynolds numbers at which manned aircraft operate. The application of such models is thus limited when applied to unmanned aircraft. In addition, there are several physical flow phenomena that occur more frequently at low Reynolds numbers and which have gotten little attention for manned aircraft. The main issues are the following:
- Laminar-turbulent transition;
- Surface roughness modeling;
- Laminar separation bubbles;
- Turbulence models for low Reynolds numbers;
- Ice shedding model for rotors and propellers;
- Electrothermal ice protection systems;
- and lack of experimental validation data.
Based on the findings in the paper, we suggest the following steps to advance icing CFD on unmanned aircraft in order to unlock the full potential of digital twin development methods:
- Apply advanced turbulence models in existing icing CFD codes to improve the capabilities to capture low Reynolds number flow effects. In particular, LSB, laminar-turbulent transition, and surface roughness interactions.
- Generate validation datasets from experiments with conditions and geometries specifically for unmanned aircraft. Conduct validation of existing icing CFD tools for ice accretion, aerodynamic performance degradation, and ice protection systems.
- Develop or adapt existing models for ice shedding, surface roughness, and ice density that are valid at low Reynolds numbers.
Reference: Hann, R. (2022). UAV Icing: Challenges for computational fluid dynamic (CFD) tools. International Conference on Computational Fluid Dynamics, ICCFD11.