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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Compound Specific Stable Isotope Signatures of Chemical Threat Agents: Incapacitants
In year 2 of this project (2017), our group focused on determining the causes of isotopic signatures in fentanyl. In order to better predict isotopic signatures in fentanyl, we analyzed the nitrogen isotope compositions of two key intermediates of fentanyl, NPP and ANPP. In addition, we analyzed additional fentanyl products produced using the Valdez route, along with five other alternative fentanyl routes. A new, more sensitive GC-C-IRMS method has been developed, which allows for compound specific isotope analysis of Chemical Attribution Signatures (CAS) in fentanyl.
Model-Based Diagnostics and Mitigation of Cyber Threats
The report summarizes key tasks performed to develop a toolkit for detecting cyber-attack events in instrumentation and control (I&C) systems of nuclear power plants. The toolkit connects the state-of-the-art GPWR Simulator with the RELAP5 code providing best-estimate nuclear steam supply system (NSSS) analyses, via the application programming interface (API), and allows users to introduce potential cyber-attack scenarios into power plant operational simulation. This summary for the project reflects topical reports submitted during the project as well as a journal paper published in 2022. The focus areas of the summary include: (1) modeling I&C systems for the AP1000 Generation III+ nuclear plant and GPWR simulator, (2) simulation and monitoring of plant response to cyber-attack events, (3) API structure for the toolkit interfacing the GPWR simulator and RELAP5 code, and (4) restructuring of the three-loop NSSS software of GPWR to model the two-loop AP1000 structure. Discussed in some details are (a) the attack tree analysis assessing the susceptibility of the AP1000 I&C system, resulting in reactor trips, in terms of the attack possibility and component sensitivity and (b) realistic estimation of the time to steam generator trip due to cyber-intrusions in the GPWR Simulator. Finally, sample demonstrations of the cyber-security tool kit, in the form of the GPWR-RELAP5 API, are summarized.
Cybersecurity in Distributed Energy Resources: threat analysis, interoperability, and dynamic defense of energy delivery systems.
Abstract not provided.
Synthetic threat injection using digital twin informed augmentation .
Abstract not provided.
Optimal Electric Grid Black Start Restoration Subject to Intentional Threats.
Abstract not provided.
Prioritizing Radiological Threats: Frameworks for Assessing Risks to Radioactive Materials.
Abstract not provided.
Novel Approach to Training Law Enforcement for the Radiological Threat ? A Partnership Between the Hungarian National Police and U.S. NNSA Office of Radiological Security.
Abstract not provided.
Electrically Small Antennas with Minimal Broadband Radio Frequency Threat Coupling.
Abstract not provided.
DERIVING A FRAMEWORK FOR INSIDER RISK POTENTIAL USING ARTIFICIAL NEURAL NETWORKS FOR INSIDER THREAT DETECTION & MITIGATION.
Abstract not provided.
FRET Based Biosensors for CBRN Threat Detection
Biosensors are devices used to detect the presence/concentration of a biological analyte. Biosensors consist of three parts: 1) a component that recognizes the analyte and produces a signal, 2) a signal transducer, and 3) a reader device. A multiplexed biosensor combines different biosensors for increased detection capabilities.
Attack Surface Analysis of the Digital Twins interface with Advanced Sensor and Instrumentation Interfaces: Cyber Threat Assessment and Attack Demonstration for Digital Twins in Advanced Reactor Architectures
A digital twin is a virtual representation of a physical system or object using real-time data that can predict and analyze how the system or object performs. This relatively new technology can be applied to the field of nuclear power generation, to aid in the design and development of new nuclear power plants and reduce operation costs using predictive maintenance and other data analytical methods. While there are already companies utilizing simulation software to train operators and technicians in the nuclear industry, some are now transitioning to utilizing their existing technology, software, and methods to develop digital twin solutions for the next generation of nuclear power plants, offering their services to utilities and government organizations around the world.
New Molecular Recognition-Based Nanoscale Sensor Platforms for Trace-Level Threat Detection
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Offline Deep Reinforcement Learning with Recurrent Training for Cyber-Physical Threat Detection
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Synthesizing X-Ray images for training automated threat recognition algorithms
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