High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
Researchers have developed a high-fidelity modeling framework combining computational fluid dynamics (CFD) with machine learning to characterize flow fields in pressurized water reactors. This approach uses physics-informed datasets to train ML models for reconstructing missing flow rate data and predicting future flow patterns. Spatially aware architectures like ConvLSTM proved more effective than sequence-based or operator-learning models due to their ability to capture complex spatio-temporal dynamics. AI
IMPACT This research demonstrates the potential for ML to improve the accuracy and efficiency of modeling complex physical systems like nuclear reactors.