Project Description

Small Modular Reactors (SMRs) represent a vital paradigm shift in the global transition toward clean, sustainable energy, relying heavily on simplified integral layouts and passive safety features like natural circulation loops to eliminate active component failures. However, housing primary safety and generation components, such as helical-coil steam generators and pressurizers, entirely within a single, integrated Reactor Pressure Vessel (RPV) introduces highly complex, multi-dimensional thermal-hydraulic phenomena. Real-world operations generate intricate, non-preferential fluid paths, localized thermal stratification, and complex two-phase flow transitions that challenge classical 1D system codes, creating over-conservative engineering safety margins that hinder commercial optimization and rapid licensing.

To resolve these challenges, this doctoral research will develop an advanced, multi-scale computational framework capable of accurately simulating SMR thermal-hydraulic performance under both normal and transient operations, such as a Station Blackout (SBO). The project will bridge the gap between low-order macro-system tracking and high-fidelity physics by dynamically coupling 1D System Thermal-Hydraulic (STH) codes with 3D Computational Fluid Dynamics (CFD). This multi-scale approach may be augmented by Physics-Informed Neural Networks (PINNs) and data-assimilation techniques to automate parameter tuning, accelerate multi-dimensional boundary layer tracking, and reconcile simulation models with high-fidelity experimental validation.