A multi-task Transformer-Mamba-Seq framework for real-time estimation of spatiotemporal thermal stratification in passive residual heat exchanger

Abstract

Passive Residual Heat Removal Heat Exchanger (PRHR HX) is a critical component in Generation-III nuclear power systems. Its spatiotemporal thermal stratification characteristics directly influence residual heat removal capacity and serve as key inputs for multiphysics coupling analyses. However, the complexity of input conditions challenges traditional simulation and AI approaches, particularly under abnormal and accident scenarios. To address this, we propose a multi-task Transformer-Mamba-Seq framework that integrates multi-head attention with a selective scan mechanism. Compared to conventional models, it demonstrates superior performance in both 5-fold cross-validation and independent tests. Furthermore, a sequential training strategy significantly reduces computational costs—cutting parameters by ∼90 % and training time by at least 59 %. Our framework enables real-time prediction of the 4D temperature field and thermal stratification characteristics in PRHR HX with high accuracy (RMSE/MAPE/R²: 1.81 K/0.41 %/0.887). It achieves a speedup of over 1500× compared to CFD simulations. This work provides an efficient and accurate tool for real-time thermal analysis of PRHR HX, supporting the thermal safety of Generation-III nuclear systems and could offering low-cost, high-resolution inputs for thermal stress and flow-induced vibration analyses.