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This paper presents a review of our current experimental and theoretical understanding of icing feathers and the role that they play in the formation of ice accretions. It covers the following areas: a short review of past research work related to icing feathers; a discussion of the physical characteristics and terminology used in describing icing feathers; the presence of feathers on ice accretions formed in unswept airfoils, especially at SLD conditions; the role that icing feathers play in the formation of ice accretion shapes on swept wings; the formation of icing feathers from roughness elements; theoretical considerations regarding feather formation, feather interaction to form complex icing structures, the role of film dynamics in the formation of roughness elements and the formation of feathers. Hypotheses related to feather formation and feather growth are discussed.

An understanding of icing physics is required for the development of both scaling methods and ice-accretion prediction codes. This paper gives an overview of our present understanding of the important physical processes and the associated similarity parameters that determine the shape of Appendix C ice accretions. For many years it has been recognized that ice accretion processes depend on flow effects over the model, on droplet trajectories, on the rate of water collection and time of exposure, and, for glaze ice, on a heat balance. For scaling applications, equations describing these events have been based on analyses at the stagnation line of the model and have resulted in the identification of several non-dimensional similarity parameters. The parameters include the modified inertia parameter of the water drop, the accumulation parameter and the freezing fraction. Other parameters dealing with the leading-edge heat balance have also been used for convenience.

This paper describes an ice-crystal icing experiment conducted at the NASA Propulsion System Laboratory during June 2018. This test produced ice shape data on an airfoil for different test conditions similar to those inside the compressor region of a turbo-fan jet engine. Mixed-phase icing conditions were generated by partially freezing out a water spray using the relative humidity of flow as the primary parameter to control freeze-out. The paper presents the ice shape data and associated conditions which include pressure, velocity, temperature, humidity, total water content, melt ratio, and particle size distribution. The test featured a new instrument traversing system which allowed surveys of the flow and cloud. The purpose of this work was to provide experimental ice shape data and associated conditions to help develop and validate ice-crystal icing accretion models.

This paper will describe two recent modifications to the LEWICE software. The version described is under development and not ready for release. First, a capability for modeling ice crystals and mixed phase icing has been modified based on recent experimental data. Modifications have been made to the ice particle bouncing and erosion model. This capability has been added as part of a larger effort to model ice crystal ingestion in aircraft engines. Comparisons have been made to ice crystal ice accretions performed in the NRC Research Altitude Test Facility (RATFac). Second, modifications were made to the runback model based on data and observations from thermal scaling tests performed in the NRC Altitude Icing Tunnel. The runback model was modified to match film models used in the open literature. An empirical water shedding was also implemented. Comparisons were made to thermal deicing data taken at the NRC Altitude Icing Tunnel.

Many studies have been performed to quantify the formation and evolution of roughness on ice shapes created in Appendix C icing conditions, which exhibits supercooled liquid droplets ranging from 1-50 µm. For example Anderson and Shin (1997), Anderson et al. (1998), and Shin (1994) represent early studies of ice roughness during short-duration icing events measured in the Icing Research Tunnel at the NASA Glenn Research Center. In the historical literature, image analysis techniques were employed to characterize the roughness. Using multiple images of the roughness elements, these studies of roughness focused on extracting parametric representations of ice roughness elements. While the image analysis approach enabled many insights into icing physics, recent improvements in laser scanning approaches have revolutionized the process of ice accretion shape characterization.

An ice shape database has been created to document ice accretions on a 21-inch chord NACA0012 model and a 72-inch chord NACA 23012 airfoil model resulting from an exposure to a Supercooled Large Drop (SLD) icing cloud with a bimodal drop size distribution. The ice shapes created were documented with photographs, laser scanned surface measurements over a section of the model span, and measurement of the ice mass over the same section of each accretion. The icing conditions used in the test matrix were based upon previously used conditions on the same models but with an alternate approach to evaluation of drop distribution effects. Ice shapes resulting from the bimodal distribution as well as from equivalent monomodal drop size distributions were obtained and compared.

The influence of freestream static temperature on roughness temporal evolution and spatial variation was investigated in the Icing Research Tunnel (IRT) at NASA Glenn Research Center. A 53.34 cm (21-in.) NACA 0012 airfoil model and a 152.4 cm (60-in.) HAARP-II business jet airfoil model were exposed to Appendix C clouds for fixed exposure times and thus fixed ice accumulation parameter. For the base conditions, the static temperature was varied to produce different stagnation point freezing fractions. The resulting ice shapes were then scanned using a ROMER Absolute Arm system and analyzed using the self-organizing map approach of McClain and Kreeger. The ice accretion prediction program LEWICE was further used to aid in interrogations of the ice accretion point clouds by using the predicted surface variations of local collection efficiency.