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Blake, Camden E.

Publications and source records attributed to Blake, Camden E..

Adaptation of Temperature Dependent Thermal Neutron Libraries for Fast On-The-Fly Monte Carlo Sampling at Arbitrary Temperatures

Thermal neutron scattering data is necessary for reactor simulations of thermal reactors, but the data storage required can be unwieldy. This is exacerbated when simulations require a large temperature range, as is the case for accident scenarios and reactor start-up. Previous work conducted at RPI has explored and demonstrated a proof of concept of On-The-Fly (OTF) data storage with light water and crystalline graphite based on ENDF/B-VII.1. This provides an updated data storage paradigm for incoherent inelastic cross-sections that allow the data to be used to generate necessary sampling Cumulative Density Functions (CDFs) for any desired temperature. The work presented in this paper extends the previous work by implementing the proof of concept based on ENDF/B-VIII.0, instead of ENDF/B-VII.1, and to include additional materials, such as beryllium and oxygen in beryllium oxide and the new reactor grades of porous graphite. Completion of this work allows a single thermal data file to represent any thermal scattering event for a given material OTF for the temperature of interest, which greatly reduces the data storage needed with only a marginal cost in the calculation of the CDFs OTF. This paper will review the basic methodologies in generating the OTF data, as well as present the updates and improvements made in the generation process. Application of the methodologies covered in this paper show a significant reduction in the storage requirements for inelastic incoherent scattering events. In which, the evaluated ENDF/B-VIII.0 thermal scattering library can be reduced from 563.1 MB to 10.9 MB for light water and 339.3 MB to 13.6 MB for Be in BeO.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Enhancing Monte Carlo Workflows for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo codes are essential components of many reactor physics simulation workflows as high-fidelity continuous-energy neutron transport solvers. Among Monte Carlo radiation transport codes, MCNP is particularly notable due to its diverse simulation capabilities, large user base, and long validation history. Despite being a powerful simulation tool, MCNP provides limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions that limit its compatibility with modern workflows. Here, to better integrate MCNP into a modern scientific workflow, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named MCNPy, which provides a specialized set of classes for MCNP input development. Moreover, to guarantee that our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable), we have adopted a strategy of model-driven software development in which a generalized model of the MCNP input format has been created. From this generalized model, or “metamodel,” problem-specific implementations such as an engine for input validation or a codebase for programmatic operations may be automatically generated. Since MCNPy primarily acts as a Python front-end to the underlying Java API that directly interfaces with the metamodel, it is intrinsically linked to the metamodel and thus remains maintainable. With MCNPy, users can programmatically read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like design optimization and model translation for nuclear systems. As examples, this work demonstrates the use of MCNPy to find the critical radius of a plutonium sphere and to translate a 9000+ line MCNP input file into a corresponding OpenMC model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗