Enhancing monte carlo modeling workflows with a metamodel-driven approach for nuclear reactor analysis

Abstract

Nuclear simulation programs are diverse and copious; one can easily find a myriad of programs for modeling applications ranging from neutronics to thermal-hydraulics to species transport and corrosion. Furthermore, it is becoming increasingly common to see such programs, or codes, bundled together to tackle the multiphysics analyses required for advanced reactor concepts. As the ensemble of nuclear simulation programs continues to grow and individual programs become more expansive and complex, it is necessary to consider how to efficiently interact with such programs. Thus, the individual programs, tools, and operations required as part of an analysis problem must be considered together, or as a workflow. A workflow encompasses each piece of the modeling process such as input preparation, simulation execution, and even results processing. Components of a workflow will often be automated or assisted as much as possible to mitigate the need for one to manually manage each individual step or program. The user-facing implementation can take many forms, such as a programming library or a user-friendly graphic interface, but should ultimately alleviate usability challenges of its constituent parts. Given the complexity of nuclear reactor modeling and analysis tools, one would expect smooth and coherent workflows to be intrinsic to the field. Therefore, it is counterintuitive that Monte Carlo neutronics codes, a cornerstone of reactor physics modeling and simulation, have antiquated workflows for both standalone and coupled operations. With their continuous energy simulation capabilities, Monte Carlo codes are irreplaceable sources for both high-fidelity reference solutions and multigroup cross-section generation for deterministic solvers. For Monte Carlo codes such as MCNP, Serpent, and KENO, their most glaring usability obstacle is their expansive and intricate input syntax. When modeling a reactor or other system in such codes, one must specify everything from geometry, to physical conditions, to their simulation quantities of interest in a terse text-based input format. Consequently, users must understand an expansive input syntax that appears arcane to the uninitiated and still remains tedious for veterans. This directly impedes manual input development as navigating the syntactic obstacles is time-consuming and error-prone. At the same time, there are limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions which would circumvent manual operations or permit workflow enhancements. Thus, developing Monte Carlo simulation inputs is a belabored process with limited workflow integration. In this work, these impediments to Monte Carlo modeling workflows are addressed through a model-driven development approach to provide modernization and unification across workflows. Following this strategy, a comprehensive ``model of the model'', or metamodel, is created for codes of interest, including MCNP, which fully describes all syntactic and semantic elements of their input formats. From the metamodel representation, editor-services (syntax highlighting, error-checking, reference finding, etc.) and Application Program Interface (API) capabilities are established. By being derived from the same underlying model, both of these avenues for input development become inherently interoperable with significantly less development effort than independent solutions. These functions are leveraged to create modern editing environments for Monte Carlo codes and more importantly, to support full-featured Python APIs. Through the developed APIs, all input features described by a code's metamodel can be managed programmatically. This enables advanced operations such as transforming and translating input files between Monte Carlo codes. These capabilities are demonstrated on applications including criticality searches, processing models for 3D viewing, modifying cross-section libraries, and iteratively translating between input formats. Collectively, these applications demonstrate the viability of a metamodel-driven approach towards workflow unification and modernization for Monte Carlo codes.