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 analyze 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 explore interface capturing techniques and higher-order multiphase models as promising avenues for enhancing CFD simulations. Additionally, we survey 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 potentially incorporating ML into computational multifluid dynamics frameworks, we aim to enhance numerical stability, computational efficiency, and predictive capabilities for multicomponent systems. Ultimately, this research contributes to the development of advanced tools for SA prevention and mitigation, improving nuclear reactor safety.