Nuclear data evaluation augmented by machine learning
| Autor: | Massimiliano Fratoni, Pedro Vicente-Valdez, L. A. Bernstein |
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| Rok vydání: | 2021 |
| Předmět: |
Cross-sectional data
Uranium benchmark Energy business.industry Decision tree Nuclear data Experimental data Machine learning computer.software_genre EXFOR Modeling and simulation Other Physical Sciences Nuclear Energy and Engineering Benchmark (surveying) Cross section evaluation Neutron Sensitivity (control systems) Artificial intelligence Interdisciplinary Engineering business computer |
| Popis: | The accuracy of neutrons modeling and simulation tools strongly depends on the quality of the nuclear data. Data libraries are generated by evaluators combining physics-based model codes and experimental data. There are many instances where experimental data are not available, are not reported rigorously or are discordant. In such cases, the evaluators need to make an expert judgment exposing the generated data to human bias and large uncertainties. This work proposes to support the evaluators’ complex tasks by leveraging Machine Learning (ML) and Artificial Intelligence (AI). Two proof-of-concept ML models, a Decision Tree and K-Nearest-Neighbor, were developed to fit nuclear data from the EXFOR database in order to infer neutron induce reaction cross sections. Both models were used to predict nuclear data for 233 U , a well-characterized isotope in literature, and 35 Cl , a less studied but important nuclide for some advanced nuclear reactors. The predicted values for 233 U were validated using the 233 U Jezebel benchmark in Serpent2 model. The predicted values for 35 Cl (n,p) cross section were compared against recent new measurement not available in EXFOR. The predicted ML/AI values matched more accurately the new measurements than any of the evaluated data libraries, which overestimate experimental results by up to a factor of five. In turn, the proof-of-concept models explored in this work, reliant on learning underlying patterns of cross section data from other radionuclides, demonstrate evidence that ML models can aid traditional physics-guided models and have a role to play in nuclear data evaluations. Furthermore, incorporating ML models in the nuclear data pipeline can allow evaluators to make faster bias-free decisions in areas of uncertainty as well as better inform future data measurement campaigns on areas of greatest sensitivity in EXFOR. |
| Databáze: | OpenAIRE |
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