构建高精度反应堆流场数据，用于机器学习建模与预测。
High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
- 基于真实几何构造全尺度流场模型，模拟泵旋流边界条件。
- 3D卷积修复缺失流量数据，底部误差大但上层显著降低。
- 时空感知模型优于传统LSTM和DeepONet，适合核电场景。
本文提出一种面向四环路压水反应堆（PWR）组件级流场表征的高保真计算流体动力学（CFD）与数据驱动建模框架。利用公开几何与运行参数构建了完整的下部集流区及堆芯入口区域模型，实现带泵诱导旋流边界条件的瞬态模拟。结果表明，冷管段旋流与下部集流区传输导致组件入口流场高度不均，尤其在堆芯下部区域；而轴向阻力与混合效应使上部流场逐渐趋于均匀。基于物理信息的数据集被用于评估机器学习（ML）在部分场重建与短期自回归预测中的应用。基于3D卷积的修补模型成功从局部观测重构组件级质量流量，误差主要集中在高度湍流的底部层，上层显著减小。多模型对比显示，具有空间感知能力的架构（如ConvLSTM）显著优于序列型（LSTM）与算子学习型（DeepONet）方法，能有效捕捉耦合的时空动态。研究还指出入口流预测对湍流和网格分辨率敏感，且缺乏全尺度实验验证数据。尽管存在局限，结果仍符合预期物理行为。总体而言，该工作确立高保真CFD作为发展数据驱动代理模型、稀疏传感策略与未来多物理场耦合框架的关键基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。