Ensuring safe and secure operations, as well as reliable energy production, storage, and delivery infrastructure are top priorities for the global energy industry. The nuclear industry has a long history of developing and implementing cutting-edge methodologies for ensuring system reliability, safety, and security. Both Defense-In-Depth (DID) and Probabilistic Risk Assessment (PRA) methodologies have contributed to the excellent safety record of the nuclear industry. As such, nuclear power plants are among the safest and most secure industrial facilities in the world. Nevertheless, U.S. Nuclear Regulatory Commission���s licensee event reports, the serious nuclear power plant accidents to date (SL-1, 1961, Three Mile Island, 1979, Chernobyl, 1986, and Fukushima Daiichi, 2011), or cyber-security threats to critical infrastructure have illustrated the ongoing importance of the need for vigilance and total commitment to enhancing nuclear reliability, safety, and security. While traditional PRA provides the probabilities and combinations of component-level or basic- event failures that can cause system or overall failure, the central concept of dynamic PRA is the focus on accident progression. Dynamic PRA methods, being a natural extension to traditional PRA methods, are not conceptually new, yet the simulation-based approach gives us the capabilities to generate much more detailed event sequences. Moreover, the applications and research to solve challenging problems have expanded in recent years. Dynamic PRA methods offer several advantages over conventional approaches, including (1) time-dependent prediction, (2) improved representation of the thermal- hydraulic success criteria, and (3) considerable reduction in analyst-to-analyst variability of the results. The main objective of the proposed work is to develop and demonstrate an open-source, web-based, technology-inclusive, full-scope dynamic probabilistic risk assessment platform to support the selection of licensing basis events and risk evaluation based on a frequency-consequence target. In developing the proposed framework, we will leverage already existing computational technologies and current serial dynamic PRA computational engines, such as the Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC), and existing transient analysis, mechanistic source term analysis, and radiological consequence analysis codes. The proposed platform will be demonstrated on a generic SMR inspired from X-energy���s design, Xe-100, which is a high-temperature gas-cooled reactor (HTGR) with inherently passive safety functions, such as a negative temperature coefficient over the full operational range of the reactor. It is fueled by Uranium Oxycarbide (UCO) TRISO-coated particles embedded in spherical graphite elements known as pebbles. The pebble bed (PB) design makes online refueling possible, allowing the addition of fresh fuel and removal of spent fuel during normal operation without having to lower the power or shut the reactor down. The 80 MWe Xe-100 reactors can be sited into a four-module 320 MWe plant.

Advances that support the licensing case to enable the transition from analog instrumentation and control (I&C) technologies to digital technologies in the U.S. nuclear industry are greatly needed. The objective of this research is to offer a capability of design architecture evaluation of various digital I&C (DI&C) systems to support system design decisions and diversity and redundancy applications; assure the long-term safety and reliability of safety-critical DI&C systems; provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the safety-critical DI&C systems; support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk-informed and performance-based evidence; and, reduce uncertainty in costs and support integration of DI&C systems at nuclear power plants. The project expects to develop the capability of quantitative assessment to fill the technical gaps and follows the trend and the need for the digital modernization of existing nuclear power plants.

This project is part of X-energy's award from the U.S. Department of Energy���s Advanced Reactor Development Program (ARDP). Under the NCSU���s sub-award, we are supporting X-energy���s efforts in the project by supporting the development of the Xe-100 PRA 24-25 model to inform the design process, human factors engineering program, and regulatory engagement.

Much of security analysis for nuclear power plants is concerned with core damage, offsite radiological release, or theft of special nuclear material; but attacks on facilities can target other consequences, including adverse effects on worker safety, physical plant, generation risk, financial risk, environmental risks, and reputational risk. It has long been understood that risks are best viewed and managed within a broad perspective. Mitigating one type of risk may well affect another, either tending to reduce it or tending to increase it. A well-known example is that in developing a scheme for mitigation of fire risk, it is important not to increase seismic risk by adding fire barriers that are seismically fragile and whose collapse could adversely affect key components. In this work we will perform case studies using a realistic nuclear plant model to illustrate the benefits of addressing multiple and qualitatively different considerations within a given analysis, in the expectation that the resulting analysis method will lead to better mitigation decisions.

This project is part of X-energy's award from the U.S. Department of Energy���s Advanced Reactor Development Program (ARDP). Under the NCSU���s sub-award, we are supporting X-energy���s efforts in the project by supporting the development of the Xe-100 PRA-23 model to inform the design process, human factors engineering program, and regulatory engagement.

Provide technical support to the Office of Nuclear Safety���s Nuclear Safety Research and Development (NSR&D) Program in demonstrating a multi-hazard time-dependent probabilistic risk assessment (PRA) approach for nuclear facilities considering aging-related deterioration of structures. A generic pressurized water reactor (PWR) reactor subjected to seismic mainshock-aftershock sequences considering the aging of the containment structure will be used as a case study to demonstrate the multi-hazard PRA approach. Using advanced modeling and simulation, seismic mainshock-aftershock fragility functions will be simulated for the containment structure considering aging effects. A multi-hazard PRA model for a generic PWR reactor will be built to quantify the multi-hazard core damage frequency (CDF) and large early release frequency (LERF) with explicit time-dependent modeling of event sequences. To date, the cascading impacts of multi-hazards are not adequately accounted for in the PRA models for nuclear facilities. In addition, for both initial and periodic evaluation of facilities to withstand natural phenomena hazards (NPH), deterioration of the SSCs due to aging and other effects is not adequately considered. By advancing multi-hazard considerations accounting for aging effects, this project will contribute to improved understanding of the safety of aging nuclear facilities. Project outcomes such as the multi-hazard CDF and LERF will also allow facility owners to optimize upgrade and retrofit protocols.

The main objective of the proposed work is to develop, demonstrate, and evaluate a probabilistic risk assessment (PRA) software platform needed to address the major challenges of the current legacy PRA tools, such as better quantification speed, integration of multi-hazard models into traditional PRAs, and model modification simplification and documentation automation. To achieve the main objective, we will first perform benchmarking and profiling of current PRA tools, such as SCRAM and SAPHIRE, to investigate the current bottlenecks in the quantification speed and memory requirements. Secondly, we will design, implement, and benchmark a PRA software platform based on a web-based stack using the latest technologies available to overcome the mentioned challenges. Finally, we will evaluate the performance gains of this framework by modeling and quantifying large PRA models that would have been too expensive to run using the legacy PRA tools.

NCSU will develop a pre-conceptual design limited scope probabilistic risk assessment model supported by existing computational modeling and simulation calculations of the Aalo Atomics microreactor based on scale-up and iterations upon the MARVEL Test Microreactor.

Fission batteries are unique from a technological standpoint given their proposed autonomous operation and tamper-proof features. Also, the actual operation of fission batteries will be novel in that local users may only have simple on/off control capabilities, while the manufacturer will need to be able to remotely monitor a fleet of units spread across different geographical regions. Furthermore, it must be self-detecting/protecting from human adversaries. The burden of local monitoring and control is therefore shifted to the autonomous technology and limited remote-monitoring capability of the manufacturer. Reliability analysis for cyber-physical systems (CPS) such as fission batteries is challenging since modern CPS incorporate distributed and networked heterogeneous software, hardware, and physical components that operate and interact in tandem. Human actions, such as those of the adversary, can also play an important role and need to be considered in the design process. All these ingredients yield highly structural and behavioral complexity for CPS models, making them computationally expensive to predict, model, and test. Consequently, highly sophisticated failure scenarios emerge, revealing new challenges for state-of-the-art quantitative reliability metrics and evaluation methods. To execute our proposal, the risk modeling needed for autonomous operations will first require newly developed dynamic PRA methods, due to the self-diagnosis, self-adjustment, and duration-prediction capabilities needed for autonomous operations. Second, reliability modeling will require the ability to integrate autonomous control, associated error-detection algorithms, and human actions for both cyber and tamper-proof designs. Finally, to perform the reliability/resilience evaluations, we will use Dual-Graph Error Propagation Models (DEPM) based on discrete-time Markov chain (DTMC) models.

This project is part of X-energy's grant from 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. Under the NCSU���s subaward for 2021, we are supporting X-energy���s efforts in the project by co-leading the Xe-100 PRA model development to inform the human factors engineering program and regulatory engagement on the control room staffing. We are supporting 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.