The European Union’s Horizon 2020 programme has funded the SENS4ICE (Sensors for Certifiable Hybrid Architectures for Safer Aviation in Icing Environment) international collaboration flagship programme. Under this programme a number of different organizations have developed ice detection technologies, specifically aimed at providing information to differentiate between ‘classical’ Appendix C icing conditions and the larger droplets found in Appendix O icing. As a partner within the SENS4ICE project, AeroTex UK has developed an ice detection concept called the Atmospheric Icing Patch (AIP). The sensor utilizes a network of iso-thermal sensors to detect icing and differentiate between small and large droplet icing conditions. This paper discusses the development of the sensor technology with a focus on the outcomes of the flight testing performed on the Embraer Phenom 300 platform during early 2023.
The work in the programme is built on previous studies performed by AeroTex UK into a probe-based sensor that operates using the same approach. The patch approach was finally adopted as it minimizes heat losses and therefore draws significantly less power than the equivalent probe system. It is better suited to the detection and differentiation of small and large droplet conditions through the application of an array of patches. The aircraft plays a key role in the sensor function as the fuselage is used to inertially separate the droplets allowing some patches to be located where only large droplets would impinge whilst others are in locations where droplets of all sizes impinge. The fuselage installation means that variability in sensor response with angle-of-attack and sideslip is negligible compared to a lifting surface installation.
The testing conducted by Embraer on the Phenom 300 successfully demonstrated the system capabilities through the detection of icing conditions and differentiation between small and large droplet distributions. The sensor system also demonstrated the ability to estimate the Liquid Water Content (LWC), but further work is required to improve this correlation.
