Presented here is a novel tool to extract and track believable vortex core lines from unsteady Computational Fluid Dynamics data sets using multiple feature extraction algorithms. Existing work explored the possibility of extracting features concurrent with a running simulation using intelligent software agents, combining multiple algorithms' capabilities using subjective logic. This work modifies the steady-state approach to work with unsteady fluid dynamics and is designed to work within the Concurrent Agent-enabled Feature Extraction concept. Each agent's belief tuple is quantified using a predefined set of information. The information and functions necessary to set each component in each agent's belief tuple is given along with an explanation of the methods for setting the components. This method is applied to the analyses of flow in a lid-driven cavity and flow around a cylinder, which highlight strengths and weaknesses of the chosen algorithms and the potential for subjective logic to aid in understanding the resulting features. Feature tracking is successfully applied and is observed to have a significant impact on the opinion of the vortex core lines. In the lid-driven cavity data set, unsteady feature extraction modifications are shown to impact feature extraction results with moving vortex core lines. The Sujudi-Haimes algorithm is shown to be more believable when extracting the main vortex core lines of the cavity simulation while the Roth-Peikert algorithm succeeding in extracting the weaker vortex cores in the same simulation. Mesh type and time step is shown to have a significant effect on the method. In the curved wake of the cylinder data set, the Roth-Peikert algorithm more reliably detects vortex core lines which exist for a significant amount of time. the method was finally applied to a massive wind turbine simulation, where the importance of performing feature extraction in parallel is shown. The use of multiple extraction algorithms with subjective logic and feature tracking helps determine the expected probability that an extracted vortex core is believable. This approach may be applied to massive data sets which will greatly reduce analysis time and data size and will aid in a greater understanding of complex fluid flows.