Education

Research Description

Dr. Diaconeasa's research focus includes theories, applications, and simulation-based techniques in risk sciences such as traditional and dynamic probabilistic risk assessment, reliability analysis, resilient systems design, probabilistic physics of failure modeling, and Bayesian inference.

Grants

Model-based Software CCF Modeling and Error Propagation for Digital Instrumentation and Control Systems

US Dept. of Energy (DOE)(2/16/23 - 9/30/23)

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Model-based Software CCF Modeling and Error Propagation for Digital Instrumentation and Control Systems

Sponsor: US Dept. of Energy (DOE)

Start Date: 2/16/23

End Date: 9/30/23

Abstract

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.

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Probabilistic Risk Assessment - Phase 1 (2022) (ARPA-E WP 3.2.2)

US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)(11/16/20 - 12/30/23)

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Probabilistic Risk Assessment - Phase 1 (2022) (ARPA-E WP 3.2.2)

Sponsor: US Dept. of Energy (DOE) - Advanced Research Projects Agency - Energy (ARPA-E)

Start Date: 11/16/20

End Date: 12/30/23

Abstract

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.

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An Open Source, Parallel, and Distributed Web-Based Probabilistic Risk Assessment Platform to Support Real Time Nuclear Power Plant Risk-Informed Operational Decisions

US Dept. of Energy (DOE)(10/01/21 - 9/30/24)

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An Open Source, Parallel, and Distributed Web-Based Probabilistic Risk Assessment Platform to Support Real Time Nuclear Power Plant Risk-Informed Operational Decisions

Sponsor: US Dept. of Energy (DOE)

Start Date: 10/01/21

End Date: 9/30/24

Abstract

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.

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Simulation-based reliability methodology development for autonomous controls and adversarial human actions involved in fission battery designs

US Dept. of Energy (DOE)(12/10/20 - 9/30/23)

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Simulation-based reliability methodology development for autonomous controls and adversarial human actions involved in fission battery designs

Sponsor: US Dept. of Energy (DOE)

Start Date: 12/10/20

End Date: 9/30/23

Abstract

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.

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A Quantitative Approach to Assessing Drugs Supply Chain Disruptions Leading to Shortages

US Food & Drug Administration(9/28/20 - 3/27/22)

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A Quantitative Approach to Assessing Drugs Supply Chain Disruptions Leading to Shortages

Sponsor: US Food & Drug Administration

Start Date: 9/28/20

End Date: 3/27/22

Abstract

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.

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