Typically, computational fluid dynamic (CFD) solutions produce large amounts of data that can be used for analysis. The enormous amount of data produces new challenges for effective exploration. The prototype system EVITA, based on ranked access of application-specific regions of interest, provides an effective tool for this purpose. Automated feature detection techniques are needed to identify the features in the dataset. Automated techniques for detecting shocks, expansion regions, vortices, separation lines, and attachment lines have already been developed. A new approach for identifying the regions of flow separation is proposed. This technique assumes that each pair of separation and attachment lines has a vortex core associated with it. It is based on the velocity field in the plane perpendicular to the vortex core. The present work describes these methods along with the results obtained.

The advancement of computational fluid dynamics to simulate highly complex fluid flow situations have allowed for simulations that require weeks of computation using expensive high performance clusters. These simulations often generate terabytes of data and hinder the design process by greatly increasing the post-processing time. This research discusses a method to extract shock waves and separation and attachment lines as the simulation is calculating and as a post-processing step. Software agents governed by subjective logic were used to make decisions about extracted features in converging and converged data sets. Two different extraction algorithms were incorporated for shock waves and separation and attachment lines and were tested on four different simulations. A supersonic ramp simulation showed two shock waves at 10% of convergence, but did not reach their final spatial locations until 85% convergence. A similar separation and attachment line analysis was performed on a cylinder in a cross flow simulation. The cylinder separation and attachment lines were within 5% of their final spatial locations at 10% convergence, and at 85% convergence, much of the cylinder and trailing separation and attachment lines showed probability expectation values of approximately 0.90 - 1.00. An Onera M6 wing simulation was used to investigate the belief tuples of the two separate shock waves at full convergence. Probability expectation values of approximately 0.90 - 1.00 were displayed within the two shock waves because they are strong shock waves and because they met the physical requirements of shock waves. A separation and attachment line belief tuple analysis was also performed on a delta wing simulation. The forward portions of these lines showed probability expectation values of approximately 0.90 - 1.00, but dropped to approximately 0.60 - 0.75 as a consequence of their respective vortices breaking down and losing their strength. Similar to shock waves, high probability expectation values meant the separation and attachment lines were strong and physically met separation and attachment line physics. The subjective logic process presented in this research was able to determine which shock waves and separation and attachment lines were most probable, making it easier to view and further investigate these important features.