@inproceedings {PUBA286,
	title = {Quantifying Vector Field Distances Based on a Reduced Stream Line Representations},
	author = {Lars Gr{\"a}ning AND Thomas Ramsay},
	year = {2013},
	month = {October},
	abstract = {In the automotive industry Computational Fluid Dynamics (CFD) simulations have become an important technology to support the development process of a new automobile. During that process, individual simulations of the air flow produce a huge amount of information about the design characteristic, where mostly only a minority of information is used. At the same time knowledge about the relationship between design modifications and their aerodynamic consequences provides valuable insight into the entire aerodynamic system. In this work a computational framework is introduced, providing means to identify relevant interactions within the aerodynamic system based on existing design and flow data. For an efficient modeling, the raw flow field data is reduced to a set of relevant flow features or phenomena. Applying interaction graphs to the aerodynamic data set unveils interacting and redundant structures between design variations and observed changes of flow phenomena. The general framework is applied to an exemplary aerodynamic system representing a 2D contour of a passenger car. },
	publisher = {Springer},
	booktitle = {Int. Conf. on Intelligent Data Engeneering and Automated Learning (IDEAL)},
	editor = {H. Yin, K. Tang, Y. Gao, F. Klawonn, M. Lee, B. Li, T. Weise, X. Yao},
	city = {Hefei, China},
	pages = {445-454},
	series = {Lecture Notes in Computer Science}
}