Abstract:

To deal with the complex nonlinear relation between the charateristic parameters and the flow regime of the gas-liquid 2phase flow, a flow regime identification method for this kind of flow was proposed based on the principal component analysis(PCA) of the wavelet packet and the least square support vector machine(LS-SVM).The 4-layer wavelet packet was decomposed for the acquired differential pressure fluctuation signal at 3 pressure measure intervals, and the characteristic vector matrix of the wavelet packet was formed. The input dimension of the characteristic vector matrix was reduced by the PCA. The dimension reduced vector matrix was applied to train identification of the LSSVM. The identification results showed that the principal component features fusing the informations from 3 sensors can identify the flow regine of the gasliquid 2phase flow more efficient than the condition of only one sensor.

Key words: fluid mechanics, hydromechanics, gas-liquid two-phase flow, flow regime identification, wavelet packet, principal component analysis(PCA), least square support vector machine(LS-SVM)