High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
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Computer Science > Machine Learning
arXiv:2605.24763 (cs)
[Submitted on 23 May 2026]
Title:High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
Authors:Logan A. Burnett, Hyungjun Kim, Hsien-Cheng Chou, Arsha Witoelar, Robert A. Brewster, Benoit Forget, Emilio Baglietto, Majdi I. Radaideh
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Abstract:This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a four-loop pressurized water reactor (PWR). A full lower-plenum and core-inlet domain was constructed using publicly available geometry and operating conditions, enabling transient simulations with pump-induced swirl boundary conditions. The results show that cold-leg swirl and lower-plenum transport generate strongly heterogeneous assembly-wise inlet flow distributions, particularly near the lower core region, while axial resistance and mixing progressively homogenize the flow at higher elevations. These physics-informed datasets were subsequently used to evaluate machine learning (ML) applications for partial field reconstruction and short-term autoregressive prediction. A 3D convolutional-based inpainting model successfully recon-structed missing assembly-level mass flow rates from partial observations, with errors concentrated in the highly turbulent base (bottom) layer and diminishing significantly in upper layers. Comparative analysis across multiple ML models demon-strates that spatially aware architectures, particularly ConvLSTM, significantly outperform sequence-based (LSTM) and operator-learning (DeepONet) approaches by effectively capturing coupled spatio-temporal dynamics. The study also high-lights key challenges, including the sensitivity of inlet flow predictions to turbulence and mesh resolution, as well as the absence of full-scale experimental validation data. Despite these limitations, the results remain consistent with expected physical behavior. Overall, this work establishes high-fidelity CFD as a critical foundation for developing data-driven surrogates, sparse sensing strategies, and future multiphysics coupling frameworks.
| Comments: | 30 pages, 10 figures, and 6 Tables |
| Subjects: | Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn) |
| Cite as: | arXiv:2605.24763 [cs.LG] |
| (or arXiv:2605.24763v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24763
arXiv-issued DOI via DataCite (pending registration)
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