An Evaluation of Computational Methods to Model Large Droplet Secondary Breakup

Download or read book An Evaluation of Computational Methods to Model Large Droplet Secondary Breakup written by Jason Turner and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Ice accretion on aircraft has been, and remains, a long-standing problem in the safe operation of flight vehicles. Ice can cause structural damage when ingested in engines and ruins the aerodynamic properties of lifting surfaces when it attaches to them. Ice accretion is typically simulated using a large scale model of an aircraft, or wing, with droplets treated as a dispersed phase. The dynamics of water droplets in the atmosphere are, thus, approximated with models. These models are tuned to match experimental data from in-flight and wind tunnel tests. Historically, icing from water droplets up to 50 micrometers in Mean Volumetric Diameter (MVD) has been considered. However, safety concerns have risen over the presence of droplets exceeding this size. Supercooled Large Droplets (SLD) are a class of droplets exceeding the 50-micrometer MVD limit. Increased droplet diameter complicates the physics of droplet deposition and breaks some of the assumptions enforced in models. This work attempts to provide a means of investigating the physics of an individual droplet, belonging to SLD regime, as it approaches a body in the most computationally efficient manner possible. A Galilean transformation is employed to isolate an individual droplet from a full model. Streamline data for this droplet is collected and then used as an input for an isolated droplet in a compact fluid domain. The droplet inside this domain is captured using a Volume of Fluid formulation of the Navier-Stokes equations. Early results suggest that assumptions of the stability of large droplets is not as certain as previous literature has suggested. This process can be used in any scenario where it is possible to capture a droplet streamline from an averaged data set.