Design engineers rely on quality performance models to establish the physical relationship between diverse thermodynamic, geometric, and fluid dynamic parameters that govern turbomachinery performance. If these models are based on a rigorous, scientific foundation, they permit the designer to thoroughly optimize a new configuration and establish with confidence the performance levels to be expected when the product is introduced in the market. The process of developing advanced models has endured more than a full century, and models of increased complexity have been introduced. However, many aspects of model development have not received thorough scientific evaluation. In the turbomachinery field, meanline performance models for axial turbines have been well developed and widely published; nearly the same can be said for the field of axial compressors. Beyond these two examples, there is a need for more model development and improvement, particularly emphasizing radial and mixed-flow turbomachines. This paper shows a systematic method, now fully integrated into a computerized methodology with optimization search techniques, for extracting the greatest useful knowledge from diverse datasets suitable for subsequent model development. The process focuses on modeling eight dependent variables based on five or six independent variables that have been found to be essential for understanding the performance of these machines. This paper emphasizes the scientific and numerical approach taken to process the data such that advanced models can be developed. The actual model development is presented in subsequent papers.

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