2012 Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies
No abstract prepared.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
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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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NIF Monthly Highlights for July 2019
July was a productive month for scientists at the National Ignition Facility, with a significant number of target shots performed, including many developing new platforms and taking first-of-their-kind data. The HED program, the ICF program, the National Security Applications Program and the Discovery Science users each achieved significant results. The facility itself also made tremendous progress towards commissioning several new capabilities for laser diagnostics, and target-physics diagnostic systems that measure x-ray and neutron data.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Health Assessment and Performance Monitoring of Large Machine Diagnostics
Diagnostic machines are a crucial component of the Stockpile Stewardship Program for data collection, toward the ultimate goal of improving our understanding of nuclear physics. These systems consist of interactions between many complex components, and require regular maintenance for acceptable performance. The Cygnus X-ray diagnostic located at the Nevada National Security Site's U1a underground facility which provides radiographic data for subcritical experiments, is a prime example of these systems. Component degradation and failures within Cygnus can result in system downtime and data loss, affecting schedules and increasing experiment costs. To evade such failures, years worth of data on machine performance has been collected on the two Cygnus axes in the form of voltage and current measurements of Cygnus' various components. Using these as input, we have developed machine learning techniques for assessing the health of Cygnus, with the ultimate goal of predicting declining performance and machine failure.
97 MATHEMATICS AND COMPUTING↗
Information security management system planning for CBRN facilities
The focus of this document is to provide guidance for the development of information security management system planning documents at chemical, biological, radiological, or nuclear (CBRN) facilities. It describes a risk-based approach for planning information security programs based on the sensitivity of the data developed, processed, communicated, and stored on facility information systems.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Assessment of Data-Management Infrastructure Needs for Production Use of Advanced Machine Learning and Artificial Intelligence: Tri-Lab Level II Milestone (8554)
A robust data-management infrastructure is a key enabler for National Security Enterprise (NSE) capabilities in artificial intelligence and machine learning. This document describes efforts from a team of researchers at Sandia National Laboratories, Los Alamos National Laboratory, and Livermore National Laboratory to complete ASC Level II milestone #8854 “Assessment of Data-Management Infrastructure Needs for Production use of Advanced Machine learning and Artificial Intelligence.”
97 MATHEMATICS AND COMPUTING↗
Networked Gamma Radiation Detection System for Rapid Tactical Response
A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2' × 4' × 16' sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart realtime localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We are testing a collection of algorithms and analysis on the problem of radiation detection with a distributed sensor network, examining such a network’s associated characteristics, and also the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, are being examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.
false alarm rate↗
Estimating soybean yields from high-temporal-resolution multi-source data using deep learning
Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.
Deep learning↗
Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine
Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.
97 MATHEMATICS AND COMPUTING↗
Validating automated resonance evaluation with synthetic data
The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Genesis Data Card Schema, Template and Supporting Tools
Genesis Data Cards provide a standardized template and schema for documenting scientific datasets in support of discovery, access, interoperability, reusability, governed use, and AI usability. This release of the Genesis Data Card repository includes a versioned Markdown template, a LinkML schema with generated Pydantic and JSON artifacts, schema documentation, and example completed data cards. Validation tooling is provided to ensure that completed data cards conform to the schema prior to submission. Accompanying documentation for the structured metadata is provided as a Field Reference Guide. The schema and accompanying template provided in this repository address the call for actionable context that enables humans and AI systems to find, access, interpret, cite, and reuse data, and, when appropriate, integrate it into AI and machine learning workflows. The data card is intended to serve as a common metadata artifact intended to support standardized, cross-program dataset documentation across Department of Energy (DOE)-aligned efforts, including but not limited to Genesis Mission-related implementations, the Office of Science, National Nuclear Security Administration (NNSA), and Advanced Simulation and Computing (ASC) data governance and stewardship initiatives.
data card↗
Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Overview
This paper is an overview of the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) project, giving some history on the project, various applications of the atmospheric data, and future ideas and plans. As part of NASA's Aviation Safety and Security Program, the TAMDAR project developed a small low-cost sensor that collects useful meteorological data and makes them available in near real time to improve weather forecasts. This activity has been a joint effort with FAA, NOAA, universities, and industry. A tri-agency team collaborated by developing a concept of operations, determining the sensor specifications, and evaluating sensor performance as reported by Moosakhanian et. al. (2006). Under contract with Georgia Tech Research Institute, NASA worked with AirDat of Raleigh, NC to develop the sensor. The sensor is capable of measuring temperature, relative humidity, pressure, and icing. It can compute pressure altitude, indicated and true air speed, ice accretion rate, wind speed and direction, peak and average turbulence, and eddy dissipation rate. The overall development process, sensor capabilities, and performance based on ground and flight tests is reported by Daniels (2002), Daniels et. al. (2004) and by Tsoucalas et. al. (2006). An in-service evaluation of the sensor was performed called the Great Lakes Fleet Experiment (GLFE), first reported by Moninger et. al. (2004) and Mamrosh et. al. (2005). In this experiment, a Mesaba Airlines fleet was equipped to collect meteorological data over the Great Lakes region during normal revenue-producing flights.
Daniels, Taumi S.↗
Using Cosmic Ray Muons to Assess Geological Characteristics in the Subsurface
Cosmic rays are energetic nuclei and elementary particles that originate from stars and intergalactic events. The interaction of these particles with the upper atmosphere produces a wide range of secondary particles that reach the surface of the earth, of which muons are the most prominent. With enough energy, muons can travel up to a few kilometers beneath the surface of the earth before being stopped completely. The terrestrial muon flux profile and associated zenith angle can be utilized to determine geological characteristics of a location (e.g., rock overburden and density) without having to use conventional methods such as boreholes. This work uses a low-power plastic scintillator-based muon detection system as a prototype for this non-destructive geological assay methodology. Four custom designed 102 cm x 51 cm x 5 cm plastic scintillation panels are used to realize two orthogonal detection planes. Optical photons from each scintillation panel are read using OnSemi J-Series 4x4 silicon photomultiplier (SiPM) arrays in conjunction with preamplifiers. Simultaneous triggers between detectors from two planes indicate a coincidence event which is recorded using the QuarkNet data acquisition system (DAQ) from Fermi National Accelerator Laboratory. A custom detector holder was designed to securely mount the detection system and rotate the panels along the zenith to collect data at variable angles. In order to quantify the systematic uncertainties associated with the detector, such as energy depositions and angular resolution of the detector design, a Monte Carlo (MC) simulation using Geant4 is being developed. Cosmic ray flux prediction will be included in the project by adding the CORSIKA MC code to the simulation toolchain. Simulated and experimental data will drive the development and validation of a reconstruction algorithm that, upon completion, is expected to predict average overburden and rock density. Extended detector exposure to muons can be used as a means to understand changes in the surrounding environment like rock porosity. On the experimental front, muons will initially be measured at the surface, establishing the baseline flux. This is followed by recording the muon flux at variable depths and zenith angles, where the data will be used by the reconstruction algorithm to predict the overburden. The result will be benchmarked against geological surveys. The measured flux data will also be used to benchmark independent and established models. Successful proof-of-concept demonstration of this technology can open doors for long term non-invasive geological monitoring. The detector design, experimental methodology, and the benchmarking efforts are detailed in this work.
Gadey, Harish Reddy↗
Networked Gamma Radiation System for Tactical Deployment
A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, Remote Sensing Laboratory to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2" × 4" × 16" sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart real-time localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We will test a collection of algorithms and analysis that centers on the problem of radiation detection with a distributed sensor network. We will study the basic characteristics of a radiation sensor network and focus on the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, will be examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Methodology and Tool for the Physical Security Analysis of Micro and Advanced Reactors
Powerpoint presentation for INSTAR annual meeting. Data and results are hypothetical and do not represent any actual nuclear plant.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
FFTF Passive Safety Test Data for Benchmarks for New LMR Designs
Liquid Metal Reactors (LMRs) continue to be considered as an attractive concept for advanced reactor design. Software packages such as SASSYS are being used to im-prove new LMR designs and operating characteristics. Significant cost and safety im-provements can be realized in advanced liquid metal reactor designs by emphasizing inherent or passive safety through crediting the beneficial reactivity feedbacks associ-ated with core and structural movement. This passive safety approach was adopted for the Fast Flux Test Facility (FFTF), and an experimental program was conducted to characterize the structural reactivity feedback. The FFTF passive safety testing pro-gram was developed to examine how specific design elements influenced dynamic re-activity feedback in response to a reactivity input and to demonstrate the scalability of reactivity feedback results to reactors of current interest. The U.S. Department of En-ergy, Office of Nuclear Energy Advanced Reactor Technology program is in the pro-cess of preserving, protecting, securing, and placing in electronic format information and data from the FFTF, including the core configurations and data collected during the passive safety tests. Benchmarks based on empirical data gathered during operation of the Fast Flux Test Facility (FFTF) as well as design documents and post-irradiation examination will aid in the validation of these software packages and the models and calculations they produce. Evaluation of these actual test data could provide insight to improve analytical methods which may be used to support future licensing applications for LMRs
Wootan, David W.↗
Performance Analysis: Issues Tracking System Data through June 2013
This report presents the results from an analysis of issues and assessments in LLNL’s Issues Tracking System (ITS). The analysis is conducted to identify issues that may require additional management attention and noncompliances that may not have been previously identified that meet the threshold for reporting to the DOE Noncompliance Tracking System (NTS) or to the DOE Safeguards and Security Information Management System (SSIMS). This report includes ITS data within the DOE Office of Enforcement regulated subjects through June 2013. The analysis in this report primarily focuses on deficiencies identified in the last twelve months. The analysis of issues concluded that data for 17 of the 25 Office of Enforcement regulated safety/security subjects were within expected variation. The data for eight met a common test and were discussed further. Three of the eight safety subjects met an action limit and were analyzed to resolution. Analysis of data from one of the safety subjects where a common test was met identified a potential safety related significant or programmatic (systemic) noncompliance with the LLNL work planning and control process. Additional analysis is needed to make a final determination. Note: Since the analysis of WPC deficiencies (through June 2013) was completed, a noncompliance evaluation was also completed in November 2013 and included more recent WPC deficiencies. The analysis of ITS data through June 2013, and the results of the noncompliance evaluation completed in November 2013 led to LLNL filing a programmatic noncompliance report to the NTS in January 2014 titled, Programmatic noncompliance with topical areas within LLNL’s Work Planning and Control process.
99 GENERAL AND MISCELLANEOUS↗
Safety Analytics Forecasting and Evaluation Reporting (SAFER)
The SAFER Sandbox is a web application for testing various types of data visualization tools that analyze safety data across national laboratories. By enabling the user to visualize this data through visual and textual analyses, the SAFER Sandbox helps to facilitate the research and support for the National Nuclear Security Administration (NNSA) Office of Safety, Infrastructure and Operations (NA-50). Safety data across the national laboratory complex is not currently analyzed in one location. This prohibits administrators from viewing safety data trends and making large scale decisions based on a holistic view of the data. By implementing data analytics web services such as representative text summary, noun phrase extraction and graph exploration, users can view and explore safety data based on search queries. The data visualizations in the SAFER Sandbox reveal trends and point to items of concern based on safety data. The SAFER Sandbox is a testing ground for these visualizations.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Earth Observations for Global Water Security
The combined effects of population growth, increasing demands for water to support agriculture, energy security, and industrial expansion, and the challenges of climate change give rise to an urgent need to carefully monitor and assess trends and variations in water resources. Doing so will ensure that sustainable access to adequate quantities of safe and useable water will serve as a foundation for water security. Both satellite and in situ observations combined with data assimilation and models are needed for effective, integrated monitoring of the water cycle's trends and variability in terms of both quantity and quality. On the basis of a review of existing observational systems, we argue that a new integrated monitoring capability for water security purposes is urgently needed. Furthermore, the components for this capability exist and could be integrated through the cooperation of national observational programmes. The Group on Earth Observations should play a central role in the design, implementation, management and analysis of this system and its products.
Earth↗