神经算子代理模型实现小堆螺旋管蒸汽发生器CFD实时仿真
Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
做核反应堆数字孪生或CFD代理模型的团队，这篇给出了针对特定几何的完整框架对比和选型指南，可以直接参考其多尺度L-DeepONet方案。
该研究提出一种结合降阶模型与神经算子的集成框架，用于小型模块化反应堆中螺旋管蒸汽发生器的CFD级瞬态分析。研究比较了两种降阶策略（MLP自编码器与卷积自编码器）分别耦合DeepONet构建潜在DeepONet，并引入多尺度技术缓解频谱偏差，成功预测了卡门涡街的瞬时周期动力学。
FNO及其多尺度变体则能可靠预测时均流场和压降。该工作为数字孪生场景下根据CFD数据类型和所需流场分辨率选择合适架构提供了实用指南。
Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
Real-time thermal-hydraulic simulation is essential for digital twin (DT) technology that supports the safe and efficient operation of small modular reactors (SMRs). Computational fluid dynamics (CFD) provides high-fidelity flow analysis, but its computational cost prevents direct use in DT applications. AI-based surrogate modeling has been actively investigated to address this limitation, yet neural operator--based surrogates for CFD-level transient analysis of SMR-specific geometries have not been reported. This study presents an integrated framework that combines a reduced-order model (ROM) with neural operators, applied to the helical coil steam generator (HCSG) of the System-integrated Modular Advanced Reactor (SMART). Two ROM strategies tailored to each CFD data type were compared, an MLP-based autoencoder (AE) for unstructured mesh data and a convolutional autoencoder (CAE) for structured mesh data, and each was coupled with the deep operator network (DeepONet) to construct the latent DeepONet (L-DeepONet). The Fourier neural operator (FNO) was additionally adopted for comparison. A multi-scale technique was incorporated into both frameworks to mitigate spectral bias and improve the prediction of Kármán vortex streets developing inside the HCSG. The multi-scale L-DeepONet captured the instantaneous periodic vortex dynamics in both velocity and pressure fields, while the FNO and its multi-scale variant predicted the time-averaged mean flow and provided reliable pressure drop estimates. These complementary characteristics provide a practical model-selection guideline that links each architecture to specific DT objectives based on CFD data type and the required level of flow resolution.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include 神经算子, CFD代理模型, 降阶模型, 数字孪生.
Source attribution: curated from arXiv cs.LG (@Minseo Lee, Seongmin Oh, Chaehyeon Song, Bumjin Cho, Shilaj Baral, Sangam Khanal, Minseop Song, Joongoo Jeon).