Design and development of innovative passive valves for Nuclear Power Plant applications

Sapra, M.K.; Kundu, Snehasis; Pal, A. K.; Vijayan, P.K.; Vaze, K.K.; Sinha, R.K.

doi:10.1016/j.nucengdes.2015.02.022

Cited by 12 publications

(1 citation statement)

References 6 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where a p1 , b p1 , a p2 , and b p2 are randomly sampled from their respective Uniform distributions: a p1 ∼ U(0Pa, 5e5Pa), b p1 ∼ U(3e6Pa, 4e6Pa), a p2 ∼ U(0Pa, 2e5Pa), and b p2 ∼ U(1.5e6Pa, 2e6Pa). The pressure values are set considering the actual conditions in NPPs [38]. This ensures that the parameters are simulated within a realistic envelop of conditions.…”

Section: Data Generationmentioning

confidence: 99%

A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants

Lai,

Ahmed,

Zio

et al. 2024

Energies

View full text Add to dashboard Cite

In Nuclear Power Plants (NPPs), online condition monitoring and the fault detection of structures, systems and components (SSCs) can aid in guaranteeing safe operation. The use of data-driven methods for these tasks is limited by the requirement of physically consistent outcomes, particularly in safety-critical systems. Considering the importance of regulating valves (e.g., safety relief valves and main steam isolation valves), this work proposes a multistage Physics-Informed Neural Network (PINN) for fault detection in such components. Two stages of the PINN are built by developing the process model of the regulating valve, which integrates the basic valve sizing equation into the loss function to jointly train the two stages of the PINN. In the 1st stage, a shallow Neural Network (NN) with only one hidden layer is developed to estimate the equivalent flow coefficient (a key performance indicator of regulating valves) using the displacement of the valve as input. In the 2nd stage, a Deep Neural Network (DNN) is developed to estimate the flow rate expected in normal conditions using inputs such as the estimated flow coefficient from the 1st stage, the differential pressure, and the fluid temperature. Then, the residual, i.e., the difference between the estimated and measured flow rates, is fed into a Deep Support Vector Data Description (DeepSVDD) to detect the occurrence of faults. Moreover, the deviation between the estimated flow coefficients of normal and faulty conditions is used to interpret the consistency of the detection result with physics. The proposed method is, first, applied to a simulation case implemented to emulate the operating characteristics of regulating the valves of NPPs and then validated on a real-world case study based on the DAMADICS benchmark. Compared to state-of-the-art fault detection methods, the obtained results from the proposed method show effective fault detection performance and reasonable flow coefficient estimation, thus guaranteeing the physical consistency of the detection results.

show abstract

Section: Data Generationmentioning

confidence: 99%