AI-driven adaptive mesh refinement for thermal–hydraulic simulations in nuclear reactors

Abstract

The meshing of complex flow channels is the most time-consuming part of large-scale thermal–hydraulic simulations in nuclear reactors and often struggles to converge. Machine learning is employed to guide the optimization of the wire-wrapped fuel rod channel meshing, which has been successfully applied to large-scale fluid simulations. The main contributions of this paper are as follows: (1) A novel adaptive meshing technology based on ”adaptive meshing + machine learning algorithms” is proposed and successfully applied to predict sensitive channel meshes and achieve automatic refinement in nuclear reactors; (2) By comparing the channel mesh models before and after optimization, mesh quality was improved while maintaining the boundary integrity of the initial channel mesh model; (3) Based on the mesh refinement algorithm, a mesh refinement tool was developed and successfully coupled with classical thermal–hydraulic simulation software, enabling the thermal–hydraulic computation of a two-dimensional axial wire-wrapped flow channel in a nuclear reactor; (4) The performance of the coupled model was evaluated, demonstrating a relative speedup of 144.54 and parallel efficiency of 56.4% when scaled to 256 cores. Since this algorithm is developed based on the general characteristics of physical object discretization simulations, it holds the potential for cross-domain applications.