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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

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