Browsing by Author "Sahin, Elvan"

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    Discrete-Time Bayesian Networks Applied to Reliability of Flexible Coping Strategies of Nuclear Power Plants
    Sahin, Elvan (Virginia Tech, 2021-06-11)
    The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risky situations in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by Nuclear Energy Institute (NEI) to manage the challenge of BDBEEs and to enhance reactor safety against extended station blackout (SBO). To assess the effectiveness of FLEX strategies, probabilistic risk assessment (PRA) methods can be used to calculate the reliability of such systems. Due to the uniqueness of FLEX systems, these systems can potentially carry dependencies among components not commonly modeled in NPPs. Therefore, a suitable method is needed to analyze the reliability of FLEX systems in nuclear reactors. This thesis investigates the effectiveness and applicability of Bayesian networks (BNs) and Discrete-Time Bayesian Networks (DTBNs) in the reliability analysis of FLEX equipment that is utilized to reduce the risk in nuclear power plants. To this end, the thesis compares BNs with two other reliability assessment methods: Fault Tree (FT) and Markov chain (MC). Also, it is shown that these two methods can be transformed into BN to perform the reliability analysis of FLEX systems. The comparison of the three reliability methods is shown and discussed in three different applications. The results show that BNs are not only a powerful method in modeling FLEX strategies, but it is also an effective technique to perform reliability analysis of FLEX equipment in nuclear power plants.
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    Machine Learning-Driven Uncertainty Quantification and Parameter Analysis in Fire Risk Assessment for Nuclear Power Plants
    Sahin, Elvan (Virginia Tech, 2025-01-27)
    Fire poses a critical risk to the safe operation of nuclear power plants (NPPs), with electrical cabinet and liquid spill fires being among the most frequent and challenging scenarios to address. Traditional fire risk assessment models often lack precision due to complex physics and inherent uncertainties, especially in predicting the heat release rate (HRR) — a key parameter for assessing fire severity. This dissertation presents an innovative framework that integrates machine learning (ML) models, particularly neural networks and tree-based algorithms, with uncertainty quantification (UQ) techniques to enhance fire modeling and risk assessment in NPPs. The framework is applied to electrical enclosure cabinets and spill fires that represent about 50% of challenging fire scenarios in NPPs. By leveraging extensive experimental datasets, this study develops ML models that capture the influence of critical fire parameters on HRR, enabling more accurate predictions of fire behavior. Key features are evaluated to establish their influence on peak HRR. Advanced UQ tools, including Monte Carlo sampling and sensitivity analysis are applied to quantify uncertainties and identify parameters with the greatest impact on model output variability. The resulting ML-driven insights allow for a refined understanding of fire dynamics, guiding experimental planning and uncertainty reduction efforts. For electrical enclosure fires, the models highlight the importance of cable surface area, heat release rate per unit area of the cable, ignition source heat release rate, ventilation area, and cabinet volume in determining peak HRR. Sensitivity analysis revealed that HRRPUA is the most significant parameter. For spill fires, the models underscore the significance of substrate thermal conductivity and slope, ignition delay time, and fuel properties, showing that fuel amount and properties are key in fixed quantity spills, while fuel discharge rate and properties are most influential in continuous spills.