Analysis of transient hydraulic failure mechanisms induced by gate malfunction during shutdown of large axial flow pump systems
Source abstract
Abstract
Large axial flow pump systems (AFPS) are critical components in hydraulic infrastructure, where their reliability directly influences the safety and stability of inter-basin water transfer, agricultural irrigation, and urban flood control projects. Investigating transient hydraulic failures, especially those caused by gate malfunction during system shutdown, is crucial for diagnosing underlying risks, enhancing structural integrity, and preventing catastrophic engineering failures. This study systematically investigates failure mechanisms, including transient hydraulic loads, pressure fluctuations, and structural risks, triggered by gate malfunction during the shutdown phase of AFPS. Employing a comprehensive failure analysis approach, we combine experimental prototype tests with high-fidelity numerical simulations to assess two critical scenarios: gate malfunction (failure case) versus standard gate operation (baseline case). Utilizing Ensemble Empirical Mode Decomposition (EEMD), instantaneous blade loading dynamics under transient conditions are identified, and turbulent kinetic energy (TKE) transport equations elucidate vortexinduced instability phenomena upstream of the malfunctioning gate. Further, wavelet coherence spectrum analysis characterizes pressure fluctuation propagation, distinctly demonstrating the exacerbated structural risks and intensified vortex-shedding behavior resulting from gate malfunction. Our findings explicitly identify the root causes of transient-induced structural vulnerabilities and propose preventive insights for gate operation, emphasizing system resilience and enhanced safety in AFPS.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Hydraulic and Pneumatic Systems · Oil and Gas Production Techniques · Cavitation Phenomena in Pumps
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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