Improved Modal Parameter Identification of Spillway Radial Gate under Discharge Excitation based on COV-SSI

Abstract

This paper aims to improve the accuracy of modal parameter identification for Spillway Radial Gate (SRG) under discharge excitation. An Improved Covariance-driven Stochastic Subspace Identification (COVISSI) method, enhanced by Singular Entropy Increment (SEI) and Potential-based Hierarchical Agglomerative (PHA) clustering, is proposed. This method is designed to effectively eliminate false poles in the identification process. The performance of COV-ISSI is compared against the original stochastic subspace identification method, demonstrating its superior capability in accurately identifying modal parameters. Additionally, both the Additional Mass Method (AMM) and Direct Coupling Method (DCM) are employed to model the fluid-structure interaction system of the SRG. The derived vibration frequencies are compared with those obtained from COV-ISSI and an improved peak-picking method. Results show that the DCM closely aligns with COV-ISSI, with a relative error within 3%, while AMM results show significant deviation from the DCM. The proposed COV-ISSI method provides a reliable and automatic approach for identifying the operating frequencies of SRG structures, offering significant improvements over traditional methods.OPEN ACCESS Received: 24/07/2024 Accepted: 27/09/2024 Published: 07/04/2025

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