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Results for “Physical Protection”

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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At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Advanced Reactor Safeguards Scenario Timeline Exploration

Slides for the advanced reactor safeguards spring 2022 program review meeting.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2021

Team Hogan - Air Bubble Curtain for Physical Exclusion - Fish Protection Prize (Final Report)

In FY2020, the National Renewable Energy Laboratory (NREL) initiated a Prize competition with support from Pacific Northwest National Laboratory (PNNL), and sponsored by the U.S. Department of Energy Water Power Technologies Office (DOE WPTO), to support the development of innovative methods for excluding fish from water diversions and intakes: the Fish Protection Prize. Proposed solutions can include new ideas for addressing fish exclusion or improvements to existing technologies. Solutions can be applied to river and canal diversions, unscreened diversion pipes, or intakes at dams. In FY20 PNNL provided voucher support in the form of technical reviews and support, as well as graphics and presentation support, in helping the 9 finalists prepare for the Pitch Contest at the American Fisheries Society (AFS) virtual meeting. There were no subject inventions, patent applications, copyrights, and trademarks under this CRADA. The report herein provides an abstract of the presentation that the finalist presented at the American Fisheries Society virtual meeting in September 2020.

42 ENGINEERING↗

DOE OSTI2020

Dynamic Force-on-Force Modeling Research

General presentation of INL dynamic Force-on-Force modeling research.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2020

A physical unclonable neutron sensor for nuclear arms control inspections

Abstract Classical sensor security relies on cryptographic algorithms executed on trusted hardware. This approach has significant shortcomings, however. Hardware can be manipulated, including below transistor level, and cryptographic keys are at risk of extraction attacks. A further weakness is that sensor media themselves are assumed to be trusted, and any authentication and encryption is done ex situ and a posteriori. Here we propose and demonstrate a different approach to sensor security that does not rely on classical cryptography and trusted electronics. We designed passive sensor media that inherently produce secure and trustworthy data, and whose honest and non-malicious nature can be easily established. As a proof-of-concept, we manufactured and characterized the properties of non-electronic, physical unclonable, optically complex media sensitive to neutrons for use in a high-security scenario: the inspection of a military facility to confirm the absence or presence of nuclear weapons and fissile materials.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

Technical and Regulatory Aspects of Integrating Safety and Security at Nuclear Power Plants

This paper provides an overview of lessons learned in applying a dynamic computational framework that links results from a commercially available FOF simulation tool, a commercially available thermal-hydraulic tool, and EMRALD to an operating commercial nuclear power plant. This process of including plant procedures and multiple analysis results is being called Modeling and Analysis for Safety Security using Dynamic EMRALD Framework. It describes how a user could integrate their plant-specific FOF models with safety mitigation actions in EMRALD, and with thermal-hydraulic tools, such as MAAP. The work performed in this paper is based on a generic EMRALD model with actual plant data used for the analysis. However, only the generic model and general results of the analysis are presented for dissemination. No plant’s sensitive information is included in this paper. The discussion shows examples of insights that can be obtained from the proposed methodology.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Advanced Testing of Physical Security Systems through AI/ML

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Science &Technology Review (September 2022)

Computer modeling is essential to scientific research. Models simulate natural phenomena to aid scientists in understanding their underlying principles. While the most complex models running on supercomputers may contain millions of lines of code and generate billions of data points, models never simulate reality perfectly. Experiments—in contrast—have been fundamental to the study of natural phenomena from science’s earliest days. However, some of today’s complex experiments generate too much data for the human mind to interpret.

42 ENGINEERING↗

DOE OSTI2025

FNCL Enhancements Implementation (FY25 Annual Report)

The FNCL investigations team at Lawrence Livermore National Laboratory (LLNL) has completed research and development of hardware, signal processing, and analysis tools to enhance the measurement capabilities of both the current CAEN SyS VeryFuel Fast Neutron Collar (FNCL) instrument and a next-generation FNCL prototype. The team successfully built and commissioned the LLNL Demonstrator System: a fully integrated, three-panel detector system featuring higher segmentation, plastic scintillators (EJ-276D), Silicon Photomultipliers (SiPMs), no high-voltage requirement, a reduced electronic footprint, and the LLNL-developed Gaussian Mixture Model Pulse Shape Discrimination (GMM-PSD) signal processing. An extensive experimental campaign was conducted at LLNL’s Inherently Safe Subcritical Assembly (ISSA) facility using both the baseline FNCL and the LLNL Demonstrator. The campaign results validated system performance, calibration stability, and the effectiveness of advanced signal processing and analysis algorithms in a relevant environment.

and physical protection↗

NASA NTRS1975

Protection against radiation (biological, pharmacological, chemical, physical)

Physical, chemical, and biological protection for astronauts from penetrating radiation on long-term space flights is discussed. The status of pharmacochemical protection, development of protective substances, medical use of protective substances, protection for spacecraft ecologic systems, adaptogens and physical conditioning, bone marrow transplants and local protection are discussed. Combined use of local protection and pharmacochemical substances is also briefly considered.

Saksonov, P. P.↗

DOE OSTI2021

Design and evaluation of a cyber‐physical testbed for improving attack resilience of power systems

Abstract A power system is a complex cyber‐physical system whose security is critical to its function. A major challenge is to model, analyse and visualise the communication backbone of the power systems concerning cyber threats. To achieve this, the design and evaluation of a cyber‐physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) are presented to capture realistic cyber, physical, and protection system features. RESLab is architected to be a fundamental platform for studying and improving the resilience of complex CPPS to cyber threats. The cyber network is emulated using Common Open Research Emulator (CORE), which acts as a gateway for the physical and protection devices to communicate. The physical grid is simulated in the dynamic time frame using Power World Dynamic Studio (PWDS). The protection components are modelled with both PWDS and physical devices including the SEL Real‐Time Automation Controller (RTAC). Distributed Network Protocol 3 (DNP3) is used to monitor and control the grid. Then, the design is exemplified and the tools are validated. This work presents four case studies on cyberattack and defence using RESLab, where we demonstrate false data and command injection using Man‐in‐the‐Middle and Denial of Service attacks and validate them on a large‐scale synthetic electric grid.

Sahu, Abhijeet↗

DOE OSTI2023

Radiation Protection [Slides]

Health Physics is a profession dedicated to the protection of human health and the environment from the dangers of radiation and radioactivity WHILE providing for its beneficial use.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2025

Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors (MRs) pose new challenges for international safeguards. Here, their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Sensor Reduction for Diversion Detection in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors are designed as a smaller, cheaper, and safer alternative to traditional nuclear power plants. Their non-traditional characteristics and prospect of mass production and deployment will likely require new approaches to nuclear safeguards. The primary proliferation concern with microreactors is the diversion of fuel material. Such diversion may produce measurable defects in key physical attributes like neutron flux, which may in turn be detectable using machine learning models. Preliminary work has demonstrated this ability for modeled nominal and diversion scenarios using large quantities of energy integrated neutron flux data. In practice, the number of available sensors for such measurements will be limited and energy integrated flux information will not be available. This work explores the ability of tree-based gradient boosted ensemble models to classify a given microreactor core is nominal or diversion, and determine the number of fuel pins diverted in the case of diversion with reduced numbers of sensors and more realistic detector responses. Classification accuracy of greater than 98% and regression errors as low as 5% of the total number of fuel pins were achieved with as few as 15 sensors, compared to 99% and 4.1% with a maximum of 240 sensors.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024