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International Journal of Rotating Machinery
Volume 2007, Article ID 76476, 10 pages
Research Article

Analysis of Geometries' Effects on Rotating Stall in Vaneless Diffuser with Wavelet Neural Networks

Key Lab of Power Machinery and Engineering of Ministry Education, Shanghai Jiao Tong University, Shanghai 200030, China

Received 17 September 2007; Accepted 28 November 2007

Academic Editor: Yoshinobu Tsujimoto

Copyright © 2007 Chuang Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wavelet neural network (WNN), which combines the capability of neural network in learning from process and that of wavelet decomposition, was used to study geometry factors on rotating stall in vaneless diffusers. A new error function called cross entropy squared (CSE) function was derived and put forward for the purpose of convergence acceleration. WNN was trained and validated with experimental data from literature. Comparison results showed the reliability. With the trained WNN, detailed investigation was carried out mainly to understand the effects of impeller blade number, blade-exit angle, impeller rotating speed, diffuser radius ratio, and width ratio on stall inception and cell speed of vaneless diffuser. Network results clearly show the existence of distinct stall mechanisms for narrow and wide diffusers, which also make different responses to variation of the above- mentioned parameters.