Identification model of an accidental drop of a control rod in PWR reactors using thermocouple readings and radial basis function neural networks

Souza, T.J.; Medeiros, J.A.C.C.; Gonçalves, Alessandro C.

doi:10.1016/j.anucene.2017.01.004

Cited by 23 publications

(9 citation statements)

References 6 publications

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“…4 and Fig. 5, which were consistent with the real flow parameters in PWR[13][14][15][16][17].Before the experiment, we carried on the numerical simulation about various working conditions by CFD software. The simulation results showed that this model was sufficiently valid to support optional simulation.…”

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confidence: 75%

Experimental investigation of flow-induced vibration in the control rod guide cylinder of pressurized-water reactor

Lu¹,

Zhang²,

Wang

2019

Can. J. Phys.

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An experiment was conducted to investigate flow-induced vibration (FIV) in the control rods of a pressurized-water reactor (PWR). Control rods and a guide cylinder (full scale compared with the real structure) were installed on an FIV experiment platform, with which flow distributions were simulated according to actual situations. The vibration displacements of the control rods were observed in different flow rate ranges of transverse and vertical flows. Several FIV characteristics of the control rods under different transverse and vertical flows were determined by analyzing the experimental results. A formula was also proposed to predict vibration.

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supporting

confidence: 75%

Experimental investigation of flow-induced vibration in the control rod guide cylinder of pressurized-water reactor

Lu¹,

Zhang²,

Wang

2019

Can. J. Phys.

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“…RBFN has been applied in many fields. Some include a comparison of RBFN and Elman Neural Network (ENN) in the fault diagnostics of NPP [9] and accidental drop of a control rod identification using RBFN algorithm [33]. RBFN…”

Section: Rbfn Outline Radial Basis Function Networkmentioning

confidence: 99%

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

Mwaura

Liu

2021

Science and Technology of Nuclear Installations

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Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.

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“…56 In addition to the mentioned cases, one of the most important applications of system identification-based models is that in control systems design and testing. 57–59 A controller not only contributes to production stability and preserves quantitative criteria, but also can impact qualitative characteristics of the system such as efficiency and operating lifetime. In this respect, it will be of importance to identify a system precisely and design controllers based on more precise models.…”

Section: Introductionmentioning

confidence: 99%

Linear Transfer Functions Extraction for a Power Generation Three-Shaft Gas Turbine Based on Actual Specifications

Mehrpanahi

Payganeh

Arbabtafti

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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The design and testing of various types of controllers require accurate and reliable transfer functions that are compatible with the performance condition of the system. In this paper, the linear and the nonlinear methods have been utilized for extracting the transfer functions for a three-shaft industrial gas turbine. The main variables are the fuel input and environment temperature, while the main outputs are the LPT exhaust gas temperature and the generated power. According to the nonlinear structure of the system and the input variation intervals, the quasi-amplitude-modulated pseudo random binary sequence signals have been utilized for generating input parameters. Finally, the results extracted from the selected methods were compared to the outputs of real performance data under loading and unloading conditions. Based on the comparison between the accuracies obtained by the results, the auto-regressive moving average with eXogenous, try and error transfer function and linearized Hammerstein model methods are proposed, respectively. The outcome of using the controller indicated higher compatibility from transfer functions as compared to the reference dynamic model.

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Identification model of an accidental drop of a control rod in PWR reactors using thermocouple readings and radial basis function neural networks

Cited by 23 publications

References 6 publications

Experimental investigation of flow-induced vibration in the control rod guide cylinder of pressurized-water reactor

Experimental investigation of flow-induced vibration in the control rod guide cylinder of pressurized-water reactor

Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques

Linear Transfer Functions Extraction for a Power Generation Three-Shaft Gas Turbine Based on Actual Specifications

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