Recent Trends in Nuclear Accident Events Prediction: A Review

Severe accidents (SAs) continue to pose a significant threat to the nuclear industry despite advancements in reactor design. This paper provides a comprehensive review of research on SA prediction, focusing on the limitations of traditional modeling approaches and the potential of machine learning (ML).
We analyzed the evolution of nuclear reactor generations, considering economic viability, safety, lifespan, and fuel reprocessing. Existing predictive models, primarily based on experimental data and computational fluid dynamics (CFD) tools like RELAP5 and MELCOR, have been effective for certain conditions but struggle to accurately capture complex multiphase flow phenomena during
SAs. To address these challenges, we explored interface-capturing techniques and higher-order multiphase models as promising avenues for enhancing CFD simulations. Additionally, we surveyed the role of ML in improving model accuracy, particularly for predicting flow parameters during phase changes.
This review highlights the need for integrated models combining CFD, interface-capturing, and ML techniques to achieve robust SA prediction. By incorporating ML into computational multifluid dynamics (CMFD) frameworks, we aim to enhance numerical stability, computational efficiency, and predictive capabilities for multi-component systems. Ultimately, this research contributes to the development of advanced tools for SA prevention and mitigation, ultimately improving nuclear reactor safety