Mixture loss coefficient of safety valves used in nuclear plants

Benbella, Shannak

doi:10.1016/j.nucengdes.2009.05.013

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…The inverse behaviors between the loss coefficient and the minor loss concerning the Reynolds number could be due to the condition of fully developed flow in the valve for values of flow velocities between 1.4 m/s and 1.8 m/s according to Figures 4 and 5, respectively. Therefore, according to the point of convergence between these variables, it is believed that a Reynolds number close to 20,000 could minimize both the effect of the energy loss of this check valve and the loss coefficient, K [21]. Figure 7 shows the behavior of the flow that circulates through the hydraulic bench under study when the recirculation flow was measured with and without a check valve under the stipulated working relative pressures below 147 kPa.…”

Section: Resultsmentioning

confidence: 99%

“…In this sense, it is confirmed that the loss coefficient is generally expressed using a mathematical function from a correlation analysis between this indicator and a hydraulic variable. Benbella [21] carried out an experimental study that used Darcy's equations and an experimental setup to estimate a correlation that defined the pressure drop and its loss coefficient in a relief valve that controls the passage of a gas-liquid mixture to preserve safety in a nuclear plant. In his study, he analyzed the influence of the Reynolds number on the accuracy of his experimental model for the practice and safe design of nuclear power plants.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, they related flow speeds with the generation of vortices and how this hydraulic phenomenon influences the mobility of the valve inside the equipment. Benbella [21] experimentally studied the loss coefficient of a relief valve used in a nuclear power plant for gas-liquid two-phase flow. In this study, the Darcy equation and the collection of experimental data were used to estimate, through correlations, the coefficient of loss and the pressure drop of the flow.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hydraulic Characterization of a Check Valve for Low-Pressure Potable Water Distribution Applications

Carpintero

Martínez

Fábregas

et al. 2023

Water

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The potable water in residential hydraulic networks is measured using volumetric meters. However, when the water carries air bubbles or pockets through the pipes, the accuracy of the meter readings is reduced, which can negatively impact the billing that users pay for their water consumption. A check valve accessory exists that reduces the size of these air bubbles to correct the meter readings and improve the service experience. However, the device has only been tested for networks with relative pressures higher than 275.79 kPa. This research proposes to characterize the hydraulic behavior of the accessory through an experimental procedure in which the operating conditions are similar to those found in water networks in Latin America, where the networks have relative pressures lower than 275.79 kPa. The study found that the accessory significantly reduces the coefficient of loss for velocities greater than 1 m/s. The use of the device is suggested in flow regimes with Reynolds numbers close to 20,000 for operating conditions of temperatures close to 25 °C and residential pipes with an internal diameter of 20.9 mm and a flow velocity between 1.3 m/s and 1.78 m/s. This condition allows it to operate with minimal local energy loss and a low coefficient of loss, providing an improved service experience.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hydraulic Characterization of a Check Valve for Low-Pressure Potable Water Distribution Applications

Carpintero

Martínez

Fábregas

et al. 2023

Water

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“…where t con is sampled from a Gaussian distribution t con ∼ N (50 • C, 20 • C), and W(t) is the white noise function accounting temperature fluctuations. The value of the temperature is set considering the real environment of the safety regulating valves, which is linked to the cooling systems in NPPs [39].…”

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

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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.

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

Zong

Zheng

et al. 2022

International Journal of Pressure Vessels and Piping

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Mixture loss coefficient of safety valves used in nuclear plants

Cited by 4 publications

References 21 publications

Hydraulic Characterization of a Check Valve for Low-Pressure Potable Water Distribution Applications

Hydraulic Characterization of a Check Valve for Low-Pressure Potable Water Distribution Applications

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

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

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