Abstract

In-cloud icing of objects is caused by supercooled microscopic water droplets carried by the wind. To estimate the icing rate of objects in such conditions, the liquid water content (LWC) of the icing cloud and the median volume diameter (MVD) of the droplets are measured. Mixed-phase clouds also contain ice crystals that must be ruled out in order to avoid the overestimation of the icing rate. Typically, cloud droplet instruments are not able to do this. A particle imaging instrument icing condition evaluation method (ICEMET) was used to observe in-cloud icing conditions. This lensless device uses a computational imaging method to reconstruct the shadow images of the microscopic objects. The size, position, and shape descriptors of each particle are measured. These data are then used to filter out the ice crystals. The droplet size distribution and the size of the measurement volume are used to determine the LWC and MVD. The performance of the instrument was tested under mixed-phase icing conditions in a wind tunnel and on a wind turbine. The measured LWC and MVD values were used to model the ice accretion on a cylinder-shaped object according to the ISO 12494:2017 icing standard. In the wind tunnel, the modeled ice mass was compared with the weighed ice mass collected by a cylinder. According to our results, ice accretion rates were overestimated by 65.6% on average without filtering out the ice crystals. Thus, the ability to distinguish between droplets and ice crystals is essential for estimating the icing rate properly.