Based on Wavelet and Windowed Multi-Resolution Dynamic Mode Decomposition, Transient Axial Force Analysis of a Centrifugal Pump under Variable Operating Conditions

Abstract

This study analyzes the transient axial force of a centrifugal pump under variable operating conditions using wavelet analysis and a novel technique called windowed multi-resolution dynamic mode decomposition (wmrDMD). Numerically simulating the sampled time series allows the reconstruction of the impeller’s axial force information, providing validation for this innovative data-driven analysis technique. The comparison between the reconstructed results and the original axial force data demonstrates a remarkable agreement, as all data points exhibit error values below 2.49%. The wmrDMD technique systematically decomposes the impeller’s axial force field into dynamically significant modes across various time scales. Removing the mean flow field in this study resolves the transient motion of the impeller’s axial force, facilitating the identification of positions with high-frequency axial force oscillations and fluctuations in intensity amplitude. The high-frequency axial force of the impeller exhibits stable periodic variations within the operating range of 1.0nr-1.0Qr, whereas the changes are insignificant within the range of 0.4nr-0.4Qr. However, within the operating range of 1.0nr-0.4Qr, both the position and intensity amplitude of the axial force exhibit significant variations without a stable trend. Furthermore, cross-wavelet and wavelet coherence analyses reveal that within the operating range of 0.4nr-0.4Qr, the axial forces on the front and rear cover plates show the strongest correlation at the periodic scale. Within the operating range of 1.0nr-1.0Qr, the next highest correlation is observed, whereas the correlation is lowest within the 1.0nr-0.4Qr operating range.

Research on the Measurement Method of the Prompt Neutron Decay Constant Based on LHS-DMD-Rossi-Alpha

“…The essence of the DMD method is to regard the flow evolution as a linear dynamic process [12][13][14][15]. By analyzing the characteristics of the flow field snapshot of the whole process, the low-order modes and their corresponding eigenvalues representing the flow field information are obtained.…”