Analysis Of Super-cooled Droplet Splash Images

The research on super-cooled large droplets (SLD) has been developing dramatically in recent years. It proves that super-cooled large droplets impingement onto airfoil and aircraft materials have a great effect on aircraft icing. In the research facility of icing wind tunnel at Cranfield University, it aims to classify images into wet/dry surface with the intention of sample identification. The droplets ran quicker onto dry samples so that it could protect airfoil from icing. In the past, droplet splashing images were arranged manually to identify the surface condition, which simulated the aerofoil in SLD icing environment. However, due to the inefficiency of physical analysis and inconsistent perspectives on the same droplet splashing image, digital image processing techniques are used to technically improve image classification in accordance with droplet splashing models.A great number of droplets splashing images are captured through aircraft icing experiments. This thesis defines a multi-dimensional feature space as average brightness, average entropy, standard deviation of entropy and Hough line, with a ratio of entropy and Hough lines to characterize the images as criteria of classification. Based on manual analysis of 200 training images, classification group has been defined as four. After the organization by k-means algorithm, images can be categorized into dry surface, wet surface, and ambiguous images.Among 9 samples, eight samples succeed to be identified into wet/dry behavior. Meanwhile, one sample is to false classification since the raw images are insufficient to represent the entire droplets splash impact events. However, there are three samples left to be validated by humans. The droplet splash images can be processed quickly and correctly, due to time consumption by human classification.