Development of a Trajectory Period Folding Method for Burnup Calculations

Stanisz, Przemysław; Oettingen, Mikołaj; Cetnar, Jerzy

doi:10.3390/en15062245

Cited by 17 publications

(16 citation statements)

References 22 publications

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“…The Trajectory Period Folding (TPF) approach can be feasibly utilized to model nuclear transformations numerically. The basis of the technique is the linear chain method, often utilized for modeling isotropic changes in matter [12]. Based on Stanisz, Oettingen, and Cetnar's description, to build linear chain depictions, TPF folds in two successive periods [12].…”

Section: Recently Developed Methods For Burnup Calculations 221 Traje...mentioning

confidence: 99%

“…The basis of the technique is the linear chain method, often utilized for modeling isotropic changes in matter [12]. Based on Stanisz, Oettingen, and Cetnar's description, to build linear chain depictions, TPF folds in two successive periods [12]. Over the cumulative period of interest, what is considered is the linear nuclide-to-nuclide mass flow of transitions following each phase's creation of nuclide transmutation chains.…”

Section: Recently Developed Methods For Burnup Calculations 221 Traje...mentioning

confidence: 99%

“…Over the cumulative period of interest, what is considered is the linear nuclide-to-nuclide mass flow of transitions following each phase's creation of nuclide transmutation chains. This approach promotes the retention of all quantitative data detailing the isotropic transformation for the time-lapse beyond a given computation step [12]. Moreover, from the start of irradiation to a random time step, the evaluation of an isotope's history is facilitated by TPF.…”

Section: Recently Developed Methods For Burnup Calculations 221 Traje...mentioning

confidence: 99%

“…The technique consolidates fuel-performance analysis, Monte Carlo neutron transport, and subchannel thermal hydraulics to simulate depletion in a highly detailed manner. The method can be used to quantify burnup-dependent safety variables at the pin levels for various designs of NPPs [12]. Markedly, an object-oriented setup featuring a mesh-feedback exchange is the basis for the Serpent-SCF-TU coupling system [13].…”

Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

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Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Almteiri¹,

Kim²

2022

Energies

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The United Arab Emirates (UAE) built four nuclear power plants at the Barakah site to supply 25% of the region’s electricity. Among the Barakah Nuclear Power Plants, (BNPPs), their main objectives are to achieve the highest possible safety for the environment, operators, and community members; quality nuclear reactors and energy; and power production efficiency. To meet these objectives, decision-makers must access large amounts of data in the case of a nuclear accident to prevent the release of radioactive materials. Machine learning offers a feasible solution to propose early warnings and help contain accidents. Thus, our study aimed at developing and testing a machine learning model to classify nuclear accidents using the associated release of radioactive materials. We used Radiological Assessment System for Consequence Analysis (RASCAL) software to estimate the concentration of released radioactive materials in the four seasons of the year 2020. We applied these concentrations as predictors in a classification tree model to classify three types of severe accidents at Unit 1 of BNPPs each season. The average accuracy of the classification models in the four seasons was 97.3% for the training data and 96.5% for the test data, indicating a high efficacy. Thus, the generated classification models can distinguish between the three simulated accidents in any season.

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Section: Recently Developed Methods For Burnup Calculations 221 Traje...mentioning

confidence: 99%

Section: Recently Developed Methods For Burnup Calculations 221 Traje...mentioning

confidence: 99%

Section: Recently Developed Methods For Burnup Calculations 221 Traje...mentioning

confidence: 99%

Section: Serpent-subchanflow-transuranusmentioning

confidence: 99%

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Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Almteiri¹,

Kim²

2022

Energies

View full text Add to dashboard Cite

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“…For instance, in the case of new fuel cladding concepts such as ATFs, Monte Carlo has the ability to produce reliable radial distributions without the need for correction formula nor the implementation of approximations to account for fuel self-shielding. In another instance, the geometry approach in Monte Carlo is based on universes, cells, and surfaces, which allows the modelling of practically any two-or three-dimensional reactor configuration [26][27][28][29]. Furthermore, Monte Carlo has the capacity to predict various neutronic characteristics throughout temporal progression.…”

Section: Serpentmentioning

confidence: 99%

Neutronic Analysis of SiC/SiC Sandwich Cladding Design in APR-1400 under Normal Operation Conditions

2022

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Our aim is to study the neutronic behaviour of potential accident-tolerant fuel (ATF) claddings in a pressurised water reactor under normal operations. This work compares ATF silicon carbide composite (SiC/SiC) cladding to conventional ZIRLO™ cladding in APR-1400. Additionally, a “sandwich” cladding design developed by the CEA is used for SiC/SiC. The design structure includes a liner in between two layers of the composite to ensure leak tightness. The two proposed liners are Niobium (Nb) and Tantalum (Ta). Serpent 2, a Monte Carlo reactor physics lattice code, is employed to model both cladding materials in APR-1400 at three different levels: pin cell, fuel assembly, and core. The criticality, neutron spectrum, actinide inventory, and power distribution as a function of burnup are investigated. The simulations show that SiC/SiC with the Nb liner displays a far superior performance than the Ta liner across all examined characteristics. Ta leads to a harder neutron spectrum and increased Pu-239 content throughout the cycle, while Nb presents negligible effects. In fact, SiC/SiC with the Nb liner performs very similarly to ZIRLO™ at all model levels. The results indicate that, in terms of neutronics, the adoption of the SiC/SiC composite would entail little to no changes to current APR-1400 operations.

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Development and validation of a deep learning‐based model for predicting burnup nuclide density

Lei

Yang

Ren

et al. 2022

Intl J of Energy Research

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Summary To address the issue of large inaccuracies in the low‐burnup region of aditonal machine learning algorithms for predicting nuclide density, the DRAGON code is used to produce 9600 samples using the nuclide densities of 235U, 239Pu, 241Pu, 137Cs, and 154Nd as prediction parameters. The mean square error (MSE) was used as the loss function for the deep neural network‐based nuclide density prediction model. The trained model is used to predict the target nuclides in the test set, and the relative error with the multilayer perceptron model are compared. The prediction results demonstrate that the deep neural network‐based prediction model not only overcomes the issue of excessive prediction errors in the low‐burnup region of the traditional machine learning algorithm model, but also has lower prediction errors in the medium‐burnup and high‐burnup regions, demonstrating the feasibility of artificial intelligence in nuclide density prediction.

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Development of a Trajectory Period Folding Method for Burnup Calculations

Cited by 17 publications

References 22 publications

Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Neutronic Analysis of SiC/SiC Sandwich Cladding Design in APR-1400 under Normal Operation Conditions

Development and validation of a deep learning‐based model for predicting burnup nuclide density

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