• Title/Summary/Keyword: Nuclear Data

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STATUS AND PERSPECTIVE OF NUCLEAR DATA PRODUCTION, EVALUATION AND VALIDATION

  • TRKOV A.
    • Nuclear Engineering and Technology
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    • v.37 no.1
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    • pp.11-24
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    • 2005
  • A very important feature in the development of nuclear technology has been and will continue to be the flow of information from nuclear data production to the various applications fields in nuclear technology. Both, nuclear data and this communications flow are defined in this paper. Nuclear data result from specific technical activities including their production, evaluation, processing, verification, validation and applications. These activities are described, focusing on nuclear reactor calculations. Mathematical definitions of different types of nuclear data are introduced, and international forums involved in nuclear data activities are listed. Electronic links to various sources of information available on the web are specified, whenever possible.

Integral nuclear data validation using experimental spent nuclear fuel compositions

  • Gauld, Ian C.;Williams, Mark L.;Michel-Sendis, Franco;Martinez, Jesus S.
    • Nuclear Engineering and Technology
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    • v.49 no.6
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    • pp.1226-1233
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    • 2017
  • Measurements of the isotopic contents of spent nuclear fuel provide experimental data that are a prerequisite for validating computer codes and nuclear data for many spent fuel applications. Under the auspices of the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) and guidance of the Expert Group on Assay Data of Spent Nuclear Fuel of the NEA Working Party on Nuclear Criticality Safety, a new database of expanded spent fuel isotopic compositions has been compiled. The database, Spent Fuel Compositions (SFCOMPO) 2.0, includes measured data for more than 750 fuel samples acquired from 44 different reactors and representing eight different reactor technologies. Measurements for more than 90 isotopes are included. This new database provides data essential for establishing the reliability of code systems for inventory predictions, but it also has broader potential application to nuclear data evaluation. The database, together with adjoint based sensitivity and uncertainty tools for transmutation systems developed to quantify the importance of nuclear data on nuclide concentrations, are described.

NUCLEAR DATA UNCERTAINTY PROPAGATION FOR A TYPICAL PWR FUEL ASSEMBLY WITH BURNUP

  • Rochman, D.;Sciolla, C.M.
    • Nuclear Engineering and Technology
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    • v.46 no.3
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    • pp.353-362
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    • 2014
  • The effects of nuclear data uncertainties are studied on a typical PWR fuel assembly model in the framework of the OECD Nuclear Energy Agency UAM (Uncertainty Analysis in Modeling) expert working group. The "Fast Total Monte Carlo" method is applied on a model for the Monte Carlo transport and burnup code SERPENT. Uncertainties on $k_{\infty}$, reaction rates, two-group cross sections, inventory and local pin power density during burnup are obtained, due to transport cross sections for the actinides and fission products, fission yields and thermal scattering data.

PROPAGATION OF NUCLEAR DATA UNCERTAINTIES FOR PWR CORE ANALYSIS

  • Cabellos, O.;Castro, E.;Ahnert, C.;Holgado, C.
    • Nuclear Engineering and Technology
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    • v.46 no.3
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    • pp.299-312
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    • 2014
  • An uncertainty propagation methodology based on the Monte Carlo method is applied to PWR nuclear design analysis to assess the impact of nuclear data uncertainties. The importance of the nuclear data uncertainties for $^{235,238}U$, $^{239}Pu$, and the thermal scattering library for hydrogen in water is analyzed. This uncertainty analysis is compared with the design and acceptance criteria to assure the adequacy of bounding estimates in safety margins.

Machine learning of LWR spent nuclear fuel assembly decay heat measurements

  • Ebiwonjumi, Bamidele;Cherezov, Alexey;Dzianisau, Siarhei;Lee, Deokjung
    • Nuclear Engineering and Technology
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    • v.53 no.11
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    • pp.3563-3579
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    • 2021
  • Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.