Researchers at North Carolina State University are embracing risk technologies to enable and ensure safe, secure, and cost-competitive operations for the current and advanced nuclear reactors.
A research group led by Assistant Professor Mihai A. Diaconeasa at the North Carolina State University is developing and reimagining the use of probabilistic risk assessment technology to support the continued operation of existing nuclear reactors and deployment of advanced nuclear reactors.
Risk-informing the design and licensing of advanced nuclear reactors
The philosophy of defence-in-depth calling for multiple layers of protection to prevent and mitigate accidents has contributed to the excellent safety record of the nuclear industry, even if at a higher cost compared to other energy sources. In the United States, conservative deterministic methods were initially used to analyse and license nuclear power plant designs, such as the 10 CFR Part 50 rules. Inconsistencies and subjectivity of modelling, the release of the Reactor Safety Study, and the nuclear accidents at Three Mile Island, Chernobyl, and Fukushima led to a gradual shift to probabilistic risk assessment currently used today. Eventually, 10 CFR Part 52 allowed the use of probabilistic risk assessment insights into the safety case based on design basis accidents and defence-in-depth assurance. Nowadays, 10 CFR Part 53 is envisioned to fully embrace probabilistic risk assessment in most licensing requirements targeting advanced reactors. At the same time, the use of probabilistic risk assessment information enabled by improved capability to evaluate the nuclear power plants as integrated systems is changing how we design advanced reactors moving towards a rationalist approach that hopefully will lead to as safe and enable cost-competitive operations.
Probabilistic risk assessment models are developed to answer three fundamental questions: 1) What can go wrong? 2) How likely is it to go wrong? and 3) What are the consequences? This idea is at the forefront of a project supported by the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) on the “Advanced Operation & Maintenance Techniques Implemented in the Xe-100 Plant Digital Twin to Reduce Fixed O&M Cost.” X-energy’s digital twin project aims to reduce the fixed operations and management (O&M) cost of its advanced nuclear reactor design to $2 per MWh. X-energy’s Xe-100 probabilistic risk assessment model development is used to inform the design of systems, human factors engineering program, and regulatory engagement in which uncertainties in the quantitative risk insights are explicitly accounted for in the evaluation of necessary and sufficient defence-in-depth measures. For example, risk insights are used to define the appropriate control room staffing levels by evaluating the procedures and timing of events under normal and abnormal conditions obtained from computer simulations and physical control room simulator exercises.
Advancing multi-hazard risk and safety considerations for ageing nuclear facilities
The 2011 Fukushima Daiichi accident and its precursor, the 1999 Blayais flood event, have highlighted the importance of considering the cascading impacts of multi-hazards on the nuclear power plant’s safety. Moreover, many current reactors are already operating well beyond their initially planned design life, and applications for further extensions to operating licenses are being considered. Therefore, modelling multi-hazard effects and ageing deterioration in the natural phenomena hazards probabilistic risk assessment process will contribute to the safety of both existing and future nuclear facilities. In a project supported by the Office of Nuclear Safety’s Nuclear Safety Research and Development (NSR&D) Program, we intend to demonstrate a multi-hazard time-dependent probabilistic risk assessment approach for nuclear facilities considering the ageing-related deterioration of structures.
A generic pressurised water reactor (PWR) subjected to seismic mainshock-aftershock sequences considering the ageing of the containment structure will be used as a case study to demonstrate the multi-hazard probabilistic risk assessment approach. Seismic mainshock-aftershock fragility functions are simulated for the containment structure, considering ageing effects using advanced modelling and simulation techniques. A multi-hazard probabilistic risk assessment model for a generic PWR reactor is built to quantify the multi-hazard core damage frequency (CDF) and large early release frequency (LERF) with explicit time-dependent modelling of event sequences. By advancing multi-hazard considerations accounting for ageing effects, this project will contribute to an improved understanding of the safety of ageing nuclear facilities. Project outcomes such as the multi-hazard CDF and LERF will allow facility owners to optimise upgrade and retrofit protocols.
Modernising the probabilistic risk assessment software
The development and use of probabilistic risk assessment in the nuclear industry have revolutionised our approach to design, reliability, and safety. The probabilistic risk assessment applications have diversified and become more computationally demanding. Nevertheless, the tools that we use to perform such assessments have not kept up with the technological advancements in high-performance computing and computer science applications. Now, although there is a need for supporting real-time decisions at the nuclear plants using probabilistic risk assessment, this is not practical using the computationally strained legacy probabilistic risk assessment tools currently available since they were developed and are maintained using technologies that are nowadays deprecated.
An example of a currently used legacy tool is SAPHIRE, funded by the U.S. Nuclear Regulatory Commission (NRC) and developed by the Idaho National Laboratory (INL). Although these tools still perform well on internal events probabilistic risk assessment models with reasonable sizes, they suffer in terms of speed and memory requirements when challenged by more complex models, such as single hazard probabilistic risk assessment, and especially multi-hazard probabilistic risk assessment models. Therefore, a major redesign of the probabilistic risk assessment tools is necessary, starting from the computational engine capabilities, backend services to handle large probabilistic risk assessment models, and a user-friendly probabilistic risk assessment frontend that can automatically generate results and documentation necessary to inform non-probabilistic risk assessment experts.
This effort is supported by the U.S. Department of Energy’s Nuclear Energy University Program (NEUP) to develop, demonstrate, and evaluate a parallel and distributed web-based probabilistic risk assessment software platform needed to address the major challenges of the current legacy probabilistic risk assessment tools, such as better quantification speed, integration of multi-hazard models into traditional probabilistic risk assessment, and model modification simplification and documentation automation. Quantification speed, accuracy, the credibility of the results, and documentation affect all aspects of risk-informed design and operational decisions. When probabilistic risk assessment models require a long time to be developed and quantified, they delay decisions and create bottlenecks in extracting valuable risk insights. Thus, this research will significantly benefit the nuclear industry and the probabilistic risk assessment community at large, given the open-source nature of the research and development effort.
Simulating the reliability of control systems for autonomous microreactors
Established by Nuclear Science and Technology Directorate at Idaho National Laboratory, the fission battery initiative aims to define, focus, and coordinate research and development of technologies that can fully achieve battery-like functionality for nuclear energy systems. The fission batteries are nuclear reactor systems (e.g., microreactors) that are envisioned to be cost-competitive, standardised, easy to install and dispose of, operate securely and safely while unattended, and reliable for wide-scale deployment with secure remote monitoring. In a project funded by Idaho National Laboratory under the Laboratory Directed Research and Development (LDRD) program, we are developing a simulation-based reliability methodology to inform probabilistic risk assessments for autonomous controls and adversarial human actions involved in fission battery designs.
Reliability analysis for fission batteries is challenging since the systems incorporate distributed and networked heterogeneous software, hardware, and physical components that operate and interact in tandem under intermittent remote monitoring by operators. Human adversarial actions can also play a significant role and need to be considered in the design process. These ingredients yield high structural and behavioural complexity for the reliability models of fission batteries, making them computationally expensive to predict, model, and test. Consequently, complex failure scenarios emerge, revealing new challenges for state-of-the-art quantitative reliability metrics and evaluation methods.
Solving the completeness problem in safety analysis
While probabilistic risk assessment provides the probabilities and combinations of component-level failures that can cause system failure, how well we cover the space of possible sequences of events still needs to be determined. This lack of understanding creates distrust in the current probabilistic risk assessment models and insights, leading to overly conservative designs and safety margins. A solution to the completeness problem is long overdue, and it is critical for developing realistic event sequences with dynamic behaviours to design and maintain cost-competitive nuclear reactors. Fortunately, what has been mostly hypothesised on theoretical grounds is now within our grasp through dynamic probabilistic risk assessment techniques to model realistic plant responses in which deterministic transient models are supported by time-dependent failure models for realistic success criteria.
Dynamic probabilistic risk assessment methods offer several advantages over conventional approaches, including time-dependent prediction, improved representation of the thermal-hydraulic success criteria, and considerable reduction in analyst-to-analyst variability of the results. Dynamic probabilistic risk assessment simulation methods are showing promise in improving nuclear power plant probabilistic risk assessment by providing rich contextual information and explicit consideration of feedback arising from complex equipment dependencies and operator actions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) is one such computational method that can be used for nuclear power plant probabilistic risk assessment. ADS-IDAC is one of the most mature dynamic probabilistic risk assessment platforms of technological systems with an evolution that spans more than 30 years. Given the current state of the art described above and the ongoing work, it is the right time to embrace dynamic probabilistic risk assessment.
