DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONS
Modern multi-physics codes, often employed in the simulation and development of thermal nuclear systems, depend heavily on thermal neutron interaction data to determine the space-time distribution of fission events. Therefore, the computationally expensive analysis of such systems motivates the adva...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2021-01-01
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| Series: | EPJ Web of Conferences |
| Subjects: | |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2021/01/epjconf_physor2020_20004.pdf |
| _version_ | 1819152113375117312 |
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| author | Manring C. A. Hawari A. I. |
| author_facet | Manring C. A. Hawari A. I. |
| author_sort | Manring C. A. |
| collection | DOAJ |
| description | Modern multi-physics codes, often employed in the simulation and development of thermal nuclear systems, depend heavily on thermal neutron interaction data to determine the space-time distribution of fission events. Therefore, the computationally expensive analysis of such systems motivates the advancement of thermal scattering law (TSL) data delivery methods. Despite considerable improvements on past strategies, current implementations are limited by trade-offs between speed, accuracy, and memory allocation. Furthermore, many of these implementations are not easily adaptable to additional input parameters (e.g., temperature), relying instead on various interpolation schemes. In this work, a novel approach to this problem is demonstrated with a neural network trained on beryllium oxide thermal scattering data generated by the FLASSH nuclear data code of the Low Energy Interaction Physics (LEIP) group at North Carolina State University. Using open-source deep learning libraries, this approach maps a unique functional form to the S(α,β,T) probability distribution function, providing a continuous representation of the TSL across the input phase space. For a given material, the result is a highly accurate, neural thermal scattering (NeTS) module that enables rapid sampling and execution with minimal memory requirements. Moreover, extension of the NeTS phase space to other parameters of interest (e.g., pressure, radiation damage) is highly possible. Consequently, NeTS modules for different materials under various conditions can be stored together in material “lockers” and accessed on-the-fly to generate problem specific cross-sections. |
| first_indexed | 2024-12-22T14:44:08Z |
| format | Article |
| id | doaj.art-dbe1243e652d4ed18687cc03c9088216 |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-12-22T14:44:08Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-dbe1243e652d4ed18687cc03c90882162022-12-21T18:22:29ZengEDP SciencesEPJ Web of Conferences2100-014X2021-01-012472000410.1051/epjconf/202124720004epjconf_physor2020_20004DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONSManring C. A.Hawari A. I.Modern multi-physics codes, often employed in the simulation and development of thermal nuclear systems, depend heavily on thermal neutron interaction data to determine the space-time distribution of fission events. Therefore, the computationally expensive analysis of such systems motivates the advancement of thermal scattering law (TSL) data delivery methods. Despite considerable improvements on past strategies, current implementations are limited by trade-offs between speed, accuracy, and memory allocation. Furthermore, many of these implementations are not easily adaptable to additional input parameters (e.g., temperature), relying instead on various interpolation schemes. In this work, a novel approach to this problem is demonstrated with a neural network trained on beryllium oxide thermal scattering data generated by the FLASSH nuclear data code of the Low Energy Interaction Physics (LEIP) group at North Carolina State University. Using open-source deep learning libraries, this approach maps a unique functional form to the S(α,β,T) probability distribution function, providing a continuous representation of the TSL across the input phase space. For a given material, the result is a highly accurate, neural thermal scattering (NeTS) module that enables rapid sampling and execution with minimal memory requirements. Moreover, extension of the NeTS phase space to other parameters of interest (e.g., pressure, radiation damage) is highly possible. Consequently, NeTS modules for different materials under various conditions can be stored together in material “lockers” and accessed on-the-fly to generate problem specific cross-sections.https://www.epj-conferences.org/articles/epjconf/pdf/2021/01/epjconf_physor2020_20004.pdfneutronthermal scattering lawflasshdeep learningnets |
| spellingShingle | Manring C. A. Hawari A. I. DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONS EPJ Web of Conferences neutron thermal scattering law flassh deep learning nets |
| title | DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONS |
| title_full | DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONS |
| title_fullStr | DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONS |
| title_full_unstemmed | DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONS |
| title_short | DEVELOPMENT OF NEURAL THERMAL SCATTERING (NeTS) MODULES FOR REACTOR MULTI-PHYSICS SIMULATIONS |
| title_sort | development of neural thermal scattering nets modules for reactor multi physics simulations |
| topic | neutron thermal scattering law flassh deep learning nets |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2021/01/epjconf_physor2020_20004.pdf |
| work_keys_str_mv | AT manringca developmentofneuralthermalscatteringnetsmodulesforreactormultiphysicssimulations AT hawariai developmentofneuralthermalscatteringnetsmodulesforreactormultiphysicssimulations |