Flow Field Data Mining Based on a Compact Streamline Representation

In many engineering domains like aerospace, vehicle or engine design the analysis of flow fields, acquired from computational fluid dynamics (CFD) simulations can reveal important insights on the behavior of the simulated objects. However, the huge amount of flow data produced by each simulation complicates the data processing and limits the application of data mining and machine learning tools for the flow analysis. The paper introduces a compact streamline and feature based representation of three dimensional flow fields, in order to overcome this limitation. The compact representation defines the basis for a subsequent quantification of flow field similarities. In this paper, we exploit the similarity measure to solve typical data mining tasks like the comparison of individual flow field similarities, the retrieval of similar flow fields from a database and the identification of groups of similar flow fields, which provide the basis for a more efficient decision making in the engineering design process. Computational experiments based on realistic data from the simulation of the exterior flow around passenger car shapes reveal the advantages of the compact streamline representation for solving the data mining tasks with respect to computational efficiency and memory consumption.