Numerical and experimental analysis of fluid force for nuclear valve

Pressure relief valves are widely used in hydraulic systems but often fail when operating in harsh environments. Developing effective and robust fault diagnosis models is essential for ensuring the reliable operation of hydraulic systems. In that study, we proposed a novel method for diagnosing clamping faults of pressure relief valve and addressing catastrophic forgetting under deep learning multi-task scenarios. Specifically, we combined the Elastic Weight Consolidation (EWC) algorithm with Deep Residual Shrinkage Network (DRSN) to achieve high diagnostic accuracy. Multiple time-series samples of pressure relief valve were collected and tested under hydraulic media with different pollutant particles. The proposed model achieves an average accuracy of 98.8% and an average loss of 0.095, which proves its effectiveness in the fault diagnosis of pressure relief valve. The main contribution of this study is the effective integration of the EWC algorithm with the DRSN, which provides an effective solution for the fault diagnosis of the pressure relief valve. 

Fault diagnosis of pressure relief valve based on improved deep Residual Shrinking Network

Thermo-mechanical stress analysis of feed-water valves in nuclear power plants

Fluid-structure coupling analysis of a pressure vessel-pipe-safety valve system with experimental and numerical methods

Thermal-fluid-structure analysis of fast pressure relief valve under severe nuclear accident

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Thermal-fluid-structure analysis of fast pressure relief valve under severe nuclear accident