Enhancing spatiotemporal resolution in probabilistic risk assessment of nuclear power plants: theoretical foundations and methodological developments

Bui, Ha

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https://hdl.handle.net/2142/115769 Copy

Description

Title

Enhancing spatiotemporal resolution in probabilistic risk assessment of nuclear power plants: theoretical foundations and methodological developments

Author(s)

Bui, Ha

Issue Date

2022-04-21

Director of Research (if dissertation) or Advisor (if thesis)

Mohaghegh, Zahra

Doctoral Committee Chair(s)

Mohaghegh, Zahra

Committee Member(s)

• Meidani, Hadi
• Podofillini, Luca
• Reihani, Seyed
• Stubbins, James
• Uddin, Rizwan

Department of Study

Nuclear, Plasma, & Rad Engr

Discipline

Nuclear, Plasma, Radiolgc Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Integrated Probabilistic Risk Assessment
• Risk Analysis
• Nuclear Power Plant
• Probabilistic Validation
• Uncertainty Analysis
• Human-Physics Coupling.

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? The answers to these two questions set the theoretical stage for the algorithm introduced in the next contribution. 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. The applicability of the algorithm is demonstrated in the context of Fire PRA. 3. Spatiotemporal modeling of coupled human-physics to support External Control Room (ExCR) Human Reliability Analysis (HRA) in PRAs of NPPs: The existing simulation of human performance in PRA (i.e., existing simulation-based HRA) 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 is the first HRA study to explicitly incorporate space (in addition to time) into the human performance model and to generate 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. 4. Creating a theoretical foundation and a methodological platform for the Probabilistic Validation (PV) and computationalizing it 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 advances the scientific usage of epistemic uncertainty and risk-informed acceptability criteria to facilitate the validity evaluation for simulation predictions. It 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 criteria to evaluate the acceptability of the simulation prediction; (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. Characteristics “iii” and “v” are uniquely developed for the PV methodology. Although each of the other characteristics (i, ii, iv) exists in some of the current studies, the integration of these characteristics under one methodology is a unique contribution of this research. Characteristic “i” is not available in the PRA domain and is adopted from other disciplines such as computational fluid dynamics and structural analysis. Characteristic “ii” is common in PRA of NPPs for uncertainty analysis, but not for the validation purpose as seen in other disciplines. Regarding characteristic “iv”, literature outside the PRA domain acknowledges the importance of sensitivity analysis for the validity improvement but lacks a formal, quantitative screening technique to make it computationally feasible; and this gap is addressed in the PV methodology. An NPP Fire PRA case study is conducted in this research by creating an automated computational platform that integrates the plant PRA scenario, underlying fire simulation, and the PV methodology.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Ha Bui

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