Gas-liquid two-phase flow pattern affects the characteristics of flow and heat transfer of a two-phase system

the performance characteristics of such a two-phase system

and the exact measurement of flow parameters. Aimed at the nonlinear and non-stationary characteristics of pressure difference signal

Hilbert-Huang transform(HHT) and wavelet packet transform were used to decompose the pressure-difference fluctuation signals and obtain the wavelet energy (IMF energy and wavelet packet energy) features of various [HJ*3/5]flow patterns. The IMF energy eigenvectors and wavelet packet energy eigenvectors were input into the Elman neural network

and flow regime intelligent identification can be performed. The experimental study showed that these two eigenvectors combined with the Elman neural network could accurately identify the four flow regimes and each method shows its merit and shortcoming. In addition

the result of flow regimes identification by using the Elman neural network was compared with that by using the BP neural network

which showed that the Elman neural network [HJ]had higher identification accuracy than the BP neural network.