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Oct-tree representation of data sets to identify relational attributes subject to well-defined constraints

The physical interpretation of turbulent flow characteristics continues to be a major obstacle in the understanding and modelling of turbulence effects in the field of fluid mechanics. Turbulence modeling plays a major role in the predictive capabilities in engineering applications. Development of new and improved models require better understanding of the mechanism associated with turbulence. Some of the requirements of improved data interpretation include a systematic approach to establishing the relationship among various turbulence quantities at many different scales of interaction. In this paper is discussed some avenues that are thought to be appropriate methods of examining high Reynolds number turbulence in a manner that allows illustration and interpretation of both large and small scale phenomena. The general principle is the requirement to examine data at different levels of abstraction based on the functional relationships that vary through a designated hyper-space. Illustration is made of the enstrophy distribution which is concentrated in regions of high wave numbers. Results are shown for a three dimensional turbulent channel flow.