Final Exam Announcement (Dissertation Defense) of Ha Bui

Ha Bui, Ph.D. Candidate, Presents Enhancing Spatiotemporal Resolution in Probabilistic Risk Assessment of Nuclear Power Plants: Theoretical Foundations and Methodological Developments

Date/time and location: April 8, 2022 | 9:00am – 12:00 pm CT through Zoom – Meeting ID: 859 4524 5332, Password:  640715

ABSTRACT: To address emergent safety concerns in commercial nuclear power plants (NPPs), the use of advanced modeling and simulation is often required in order to improve the spatial and/or temporal resolution of Probabilistic Risk Assessment (PRA). Advanced modeling and simulation are also needed to accelerate the risk-informed design, licensing, and operationalization of advanced nuclear reactors. This research makes four key contributions toward the enhancement of spatiotemporal resolution in PRAs of NPPs:

  1. Establishing a theoretical foundation for the incorporation of time and space into PRA: This study provides scientific answers for two research questions: (a) why do we need to explicitly incorporate time and space into PRA? and (b) into which features of PRA can time and space be explicitly incorporated?
  2. Developing an algorithm to efficiently enhance the spatiotemporal resolution in PRAs of NPPs: The algorithm is guided by both classical and advanced importance measure analyses to facilitate the ranking of risk contributors not only at the PRA component level but also at the level of underlying failure mechanisms. The algorithm enables existing plant PRA to be connected to a wider range of underlying failure models with various degrees of fidelity, giving NPPs more flexibility in targeting different levels of realism for the simulation of underlying failure mechanisms.
  3. Creating a theoretical foundation and a methodological platform for the Probabilistic Validation (PV) to facilitate validation of advanced simulation models that are required for PRA: Common empirical validations become challenging when validation data are limited. The PV methodology uniquely combines five key characteristics: (i) a multi-level multi-model-form validation analysis that can integrate data and uncertainty analysis at multiple levels of the system hierarchy; (ii) the separation of aleatory and epistemic uncertainties and, when possible, differentiation between two forms of epistemic uncertainty (statistical variability and systematic bias); (iii) the use of risk-informed acceptability criteria in evaluating the validity; (iv) combination of uncertainty analysis with a two-layer sensitivity analysis to streamline the validity assessment and to efficiently improve the degree of confidence in the simulation prediction; and (v) a theoretical causal framework that supports the comprehensive identification and traceability of uncertainty sources influencing simulation predictions. An NPP Fire PRA case study is conducted by creating an automated computational platform that integrates the plant PRA scenario, the underlying fire simulation, and the PV methodology.
  4. Spatiotemporal modeling of coupled human-physics to support External Control Room (ExCR) Human Reliability Analysis (HRA) in PRAs of NPPs: Existing simulation-based HRA in PRA is temporal and lacks a spatial dimension. Although temporal HRA is adequate for modeling human error inside the Main Control Room (MCR) of NPPs, it is inadequate for modeling the human-physics interactions in ExCR scenarios where spatial analysis is necessary. This research explicitly incorporates space (in addition to time) into the human performance model and generates a human-physics coupling that can bidirectionally transfer spatial and temporal information. The human performance model is developed using an Agent-Based Modeling (ABM) technique and is bidirectionally coupled with the physical hazard propagation model utilizing a Geographic Information System (GIS)-based spatial environment. The coupled human-physics model is applied for a switchgear room fire scenario within the NPP Fire PRA context.
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