We 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 rapid development of advanced manufacturing and its application to advanced reactor design in the Transformational Challenge Reactor (TCR) program1 have presented the opportunity to explore the potential revolutionary benefits of the arbitrary geometry design of nuclear systems. Nuclear engineering design is no longer bound to the simple geometries manufacturable by traditional methods, slabs, cylinders, and spheres (e.g., fuel plates, fuel pellets, fuel pebbles). However, the increased freedom of designing an arbitrary geometry system comes at the cost of an increased complexity in design optimization. Dimensionality quickly makes the design problem overwhelming for engineers. To address this issue, the authors implemented an artificial intelligence (AI)-based optimization algorithm and established a challenge problem to demonstrate the application.
Citation and DOI:
Sobes, V., Hiscox, B., Popov, E. et al. AI-based design of a nuclear reactor core. Sci Rep 11, 19646 (2021). https://doi.org/10.1038/s41598-021-98037-1
