Determination of PWR Control Rod Position by Core Physics and Neural Network Methods

Garis, N.S.; Pázsit, Imre; Sandberg, Urban; Andersson, T.L.

doi:10.13182/nt98-a2899

Cited by 32 publications

(5 citation statements)

References 4 publications

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“…The motivation of the present study was the development a computational code that reconstructs the noise source with high accuracy and it requires low computational cost. As shown in tables 2 and 3, the accuracy of the unfolded noise source in the present study is more better than the similar published works (Garis et al, 1998;Tambouratzis and Antonopoulos-Domis, 2002). In fact, the noise source of type absorber of variable strength was localized with good accuracy in the present study, while the average error of the localization of the noise source in the similar published work is almost 6 cm (Tambouratzis and Antonopoulos-Domis, 2002).…”

Section: Discussionsupporting

confidence: 47%

“…The literature review of the works performed in the noise source unfolding field of study shows that the neural network or combination of neural network and the scanning method is more robust in comparison to other aforementioned algorithms. In summary, the artificial neural network is proper selection to reconstruct the instability and vibration localization (Garis et al, 1998;Tambouratzis and Antonopoulos-Domis, 2002). In the mentioned studies, the neutron noise sources of type absorber of variable strength or vibrating absorber were just localized using the neural network without any attempt to reconstruct the noise source strength and/or its frequency.…”

Section: Introductionmentioning

confidence: 99%

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Neutron noise source reconstruction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the VVER-1000 reactor core

Hosseini

Afrakoti

2017

Annals of Nuclear Energy

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The neutron noise is defined as the stationary fluctuation of the neutron flux around its mean value due to the induced perturbation in the reactor core. The neutron noise analysis may be useful in many applications like noise source reconstruction. To identify the noise source, calculated neutron noise distribution of the detectors is used as input data by the considered unfolding algorithm. The neutron noise distribution of the VVER-1000 reactor core is calculated using the developed computational code based on Galerkin Finite Element Method (GFEM). The noise source of type absorber of variable strength is considered in the calculation. The computational code developed based on An Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to unfold the neutron noise source. Complex neutron noise distribution (real and imaginary parts) in the detectors is considered as input data onto the developed computational code based on the ANFIS algorithm. All the characteristics of the neutron noise source, including strength, frequency and position (X and Y coordinates) are unfolded with excellent accuracy using the developed computational code.

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Section: Discussionsupporting

confidence: 47%

Section: Introductionmentioning

confidence: 99%

Neutron noise source reconstruction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the VVER-1000 reactor core

Hosseini

Afrakoti

2017

Annals of Nuclear Energy

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“…The practical applicability of the proposed NNs depends on whether or not the flux shape is a sufficiently sensitive function of the control bank position [24], i.e. there is actually a dependence to fit.…”

Section: Sensitivity Of the Neutron Flux Profile To Bank Movementmentioning

confidence: 99%

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

Moloko¹,

Bokov²,

Xu³

et al. 2022

Preprint

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Artificial Neural Networks (ANNs) have been successfully used in various nuclear engineering applications, such as predicting reactor physics parameters within reasonable time and with a high level of accuracy. Despite this success, they cannot provide information about the model prediction uncertainties, making it difficult to assess ANN prediction credibility, especially in extrapolated domains. In this study, Deep Neural Networks (DNNs) are used to predict the assembly axial neutron flux profiles in the SAFARI-1 research reactor, with quantified uncertainties in the ANN predictions and extrapolation to cycles not used in the training process. The training dataset consists of copper-wire activation measurements, the axial measurement locations and the measured control bank positions obtained from the reactor's historical cycles. Uncertainty Quantification of the regular DNN models' predictions is performed using Monte Carlo Dropout (MCD) and Bayesian Neural Networks solved by Variational Inference (BNN VI). The regular DNNs, DNNs solved with MCD and BNN VI results agree very well among each other as well as with the new measured dataset not used in the training process, thus indicating good prediction and generalization capability. The uncertainty bands produced by MCD and BNN VI agree very well, and in general, they can fully envelop the noisy measurement data points. The developed ANNs are useful in supporting the experimental measurements campaign and neutronics code Verification and Validation (V&V).

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“…More complex systems include a position detector, a transducer, and an indicator. Advanced systems are also emerging with self-encoding control rod position detectors (Garis et al, 1998).…”

Section: Rod Cluster Control Assembliesmentioning

confidence: 99%

PWR control rods position monitoring

Eissa

Naguib

Badawi

2015

Annals of Nuclear Energy

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Determination of PWR Control Rod Position by Core Physics and Neural Network Methods

Cited by 32 publications

References 4 publications

Neutron noise source reconstruction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the VVER-1000 reactor core

Neutron noise source reconstruction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the VVER-1000 reactor core

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

PWR control rods position monitoring

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