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The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

The mission of the Neutron Sciences Directorate (NScD) at the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) is the undertaking of high-impact research into the structure and properties of materials across the spectrum of biology, chemistry, physics, materials science, and engineering. NScD operates two world-leading neutron scattering facilities: the High Flux Isotope Reactor (HFIR) and the Spallation Neutron Source. HFIR achieved full power in 1966, and over a half century later, it continues to serve a variety of national missions. HFIR provides one of the highest steady-state neutron fluxes of any research reactor in the world to support scientific missions including cold and thermal neutron scattering, isotope production, and materials irradiation research. To sustain leadership in neutron sciences into the future, ORNL is exploring areas in which HFIR can be improved to enhance its performance. Many improvement areas are being explored including upgrading the cold source and neutron scattering facilities. The improvement areas discussed herein include replacing the reactor pressure vessel, upgrading the neutron reflector, and ensuring that reactor performance is maintained or enhanced after converting from high-enriched uranium to low-enriched uranium fuel.

Engineering design studies are underway to assess the feasibility of converting the High Flux Isotope Reactor (HFIR) to operate with low-enriched uranium silicide dispersion (LEU3Si2-Al) fuel. These studies are supported by the U.S. Department of Energy National Nuclear Security Administration’s Office of Material Management and Minimization. A systematic approach employing neutronic and thermal-hydraulic analyses has been performed with the ORNL Shift and HFIR Steady State Heat Transfer Code tools, respectively, to predict reactor performance and thermal safety margins for proposed LEU3Si2-Al fuel designs. The design process was initiated by generating an optimized design with fabrication features identified from previous studies that result in excellent performance and safety metrics. The approach continued by substituting a single fabrication feature anticipated to be difficult to manufacture with another feature expected to perform an analogous function to that of the removed feature. Four conceptual fuel element design candidates, with various fabrication features, for conversion of HFIR with 4.8 gU/cm3 LEU3Si2-Al fuel have been generated and shown to meet pre-defined performance and safety metrics. Results to date indicate that HFIR could convert with the subject fuel system and meet performance and safety requirements if, among other considerations, fabrication of the specific design features are demonstrated and qualification of the fuel is complete under HFIR-specific conditions.

Fluid-fueled nuclear reactors, such as molten salt reactors (MSRs), have recently gained significant interest. These advanced reactors represent a potential revolutionary shift in the implementatio...

The low enriched uranium (LEU) conversion project for the High Flux Isotope Reactor (HFIR) requires that the converted core design perform as well as or better than the current high enriched uranium core design with respect to key performance metrics, such as isotope production, while maintaining sufficient safety margins. Various designs and fuel shapes have been explored in previous optimization studies. A suite of scripts has been developed for HFIR LEU design and analysis to simplify the reactor physics and thermal hydraulics (TH) analyses. The scripts include generating a high-fidelity 3D HFIR model to perform core depletion simulations with the SHIFT Monte Carlo code, performing an essential rod criticality search during depletion, parsing SHIFT output to determine HFIR key metrics, and performing TH analysis with the HFIR Steady-State Heat Transfer Code. Previously, these scripts were separated and required human interaction between simulation stages. These scripts have been modernized and integrated into a single Python package (the Python HFIR Analysis and Measurement Engine or PHAME) to streamline execution and avoid potential human error. After modernizing the suite of scripts into a single, automated workflow, the tool set was wrapped into an in-house metaheuristic optimization driver that enables different optimization methods, such as simulated annealing and particle swarm. The optimization driver samples a fuel shape, runs PHAME, calculates the cost function with the metrics returned from PHAME, and repeats those steps until it finds an optimal fuel shape. This work demonstrates the workflow of a comprehensive, automated reactor design study and how metaheuristic optimization methods can be leveraged to fine-tune a design parameter like fuel shape. This workflow of wrapping an optimization driver on a full-scale reactor analysis suite increases design and analysis efficiency.

Implicit in any engineering design is an underlying optimization problem, although the exact objective function to be optimized is rarely stated explicitly. Nuclear systems optimization is as old as the discipline of nuclear engineering. Advanced manufacturing in the nuclear industry has opened the door for the re-examination of optimization in a way in which it was not possible before, namely, determining the optimal geometry for a given objective function. A trivial example is the sphere as the shape that minimizes the volume (or mass) of bare fissile material in a critical configuration. However, the problem becomes less trivial under even the simplest of multiphysics considerations. In this work, we develop the solution methodology for finding the minimum volume geometric configurations under the multiphysics constraints of 1,500 pcm excess reactivity and maximum fuel temperature of 618°C under forced-flow cooling conditions. Constraining the solution geometry only to right cylinders, surprisingly yields two disjoint solution regions. Flat, wide (disk-like) cylinders and tall, narrow (rod-like) cylinders both satisfy the constraints and yield very similar minimal volumes. However, the ultimate pursuit of this work is truly arbitrary geometry.