Mihai Diaconeasa

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.

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.

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.

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.

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.

In this project we plan to develop a methodology to quantitatively characterize the drug shortages in terms of their frequency, persistence, and intensity by modeling their supply chain. Quantitative metrics, such as relative importance or criticality, will be developed to identify the most important contributors to shortages. With the estimation of the impact of shortages using available public information, we will be able to demonstrate the methodology by developing a risk of shortage profile in terms of frequency and consequences for a number of drug supply chains.