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.

翻译：实时热工水力仿真对于支持小型模块化反应堆（SMR）安全高效运行的数字孪生（DT）技术至关重要。计算流体力学（CFD）能够提供高保真度的流动分析，但其计算成本限制了在数字孪生应用中的直接使用。基于人工智能的代理建模已被积极研究以解决这一局限，然而基于神经算子的、针对SMR特定几何结构的CFD级瞬态分析代理模型尚未见报道。本研究提出了一个将降阶模型（ROM）与神经算子相结合的集成框架，并将其应用于一体化模块化先进反应堆（SMART）中的螺旋管蒸汽发生器（HCSG）。针对每种CFD数据类型定制了两种降阶模型策略——面向非结构化网格数据的基于多层感知机（MLP）的自编码器（AE）和面向结构化网格数据的卷积自编码器（CAE），并将每种策略与深度算子网络（DeepONet）耦合以构建潜在空间深度算子网络（L-DeepONet）。此外，还采用傅里叶神经算子（FNO）进行对比。在两个框架中均引入多尺度技术以缓解频谱偏差，并改善对HCSG内部形成的卡门涡街的预测。多尺度L-DeepONet能够捕捉速度和压力场中瞬态周期性涡旋动力学，而FNO及其多尺度变体则预测了时间平均的时均流，并提供了可靠的压降估算。这些互补特性为基于CFD数据类型和所需流动解析度、将各架构与特定数字孪生目标关联起来的实用模型选择指南提供了依据。