In the course of developing advanced data processing and advanced performance models, as presented in companion papers, a number of basic scientific and mathematical questions arose. This paper deals with questions such as uniqueness, convergence, statistical accuracy, training, and evaluation methodologies. The process of bringing together large data sets and utilizing them, with outside data supplementation, is considered in detail. After these questions are focused carefully, emphasis is placed on how the new models, based on highly refined data processing, can best be used in the design world. The impact of this work on designs of the future is discussed. It is expected that this methodology will assist designers to move beyond contemporary design practices.

1.
Japikse, D., and Oliphant, K. N., “Turbomachinery Modeling: Explicit and Implicit Knowledge Capturing (2005A),” presented at the ASME Turbo Expo 2005: Power for Land, Sea and Air, ASME Paper No. GT2005-68099, Reno-Tahoe, Nevada, June 2005.
2.
Pelton, R. J., Japikse, D., Maynes, D., and Oliphant, K. N., “Turbomachinery Performance Models (2005B),” to be presented at the ASME Turbo Expos 2005: Power for Land, Sea and Air, ASME Paper No. IMECE2005-79414, Reno-Tahoe, Nevada, June 2005.
3.
Dubitsky, O. B., and Japikse, D., “Vaneless Diffuser Advanced Model (2005D),” presented at the ASME Turbo Expo 2005: Power for Land, Sea and Air, ASME Paper No. GT2005-68130, Reno-Tahoe, Nevada, June 2005.
4.
Hassoun, Mohamad H., “Fundamentals of Artificial Neural Networks,” The MIT Press, Cambridge, Mass., 1995. pp. 226–230.
5.
Bishop, Christopher M., “Neural Networks for Pattern Recognition,” Clarendon Press, Oxford, 1995. pp. 371–377.
6.
Eckardt D., and Tru¨ltzsch K-J., 1977 “Vergleichende Stro¨mungsuntersuch-ungen an drei Radialverdichter-Laufra¨dern mit konventionellen Meßverfahren,” FVV Research Report (Forschungsberichte) #237.
This content is only available via PDF.
You do not currently have access to this content.