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At least 1,117 records · Page 62

All sources NASA NTRS DOE OSTI

DOE OSTI2015

The cementitious barriers partnership experimental programs and software advancing DOE's waste disposal/tank closure efforts - 15436

The U.S. Department of Energy Environmental Management (DOE-EM) Office of Tank Waste Management sponsored Cementitious Barriers Partnership (CBP) is chartered with providing the technical basis for implementing cement-based waste forms and radioactive waste containment structures for long-term disposal. DOE needs in this area include the following to support progress in final treatment and disposal of legacy waste and closure of High-Level Waste (HLW) tanks in the DOE complex: - Long-term performance predictions and - Flow sheet development and flow sheet enhancements - Conceptual designs for new disposal facilities The DOE-EM Cementitious Barriers Partnership is producing software and experimental programs resulting in new methods and data needed for end-users involved with environmental cleanup and waste disposal. Both the modeling tools and the experimental data have already benefited the DOE sites in the areas of performance assessments by increasing confidence backed up with modeling support, leaching methods, and transport properties developed for actual DOE materials. In 2014, the CBP Partnership released the CBP Software Toolbox - 'Version 2.0' which provides concrete degradation models for: 1) sulfate attack, 2) carbonation, 3) chloride initiated rebar corrosion, and includes constituent leaching. These models are applicable and can be used by both DOE and the Nuclear Regulatory Commission (NRC) for service life and long-term performance evaluations and predictions of nuclear and radioactive waste containment structures across the DOE complex (Figure 1) including Future SRS salt-stone and HLW tank performance assessments and special analyses - Hanford site HLW tank closure projects and other projects in which cementitious barriers are required - The Advanced Simulation Capability for Environmental Management (ASCEM) project which requires source terms from cementitious containment structures as input to their flow simulations - Regulatory reviews of DOE performance assessments - Nuclear Regulatory Commission reviews of commercial nuclear power plant (NPP) structures which are part of the overall US Energy Security program to extend the service life of NPPs In addition, the CBP experimental programs have had a significant impact on the DOE complex by providing specific data unique to DOE sodium salt wastes at Hanford and SRS which are not readily available in the literature. Two recent experimental programs on cementitious phase characterization and on technetium (Tc) mobility have provided significant conclusions as summarized below: Recent mineralogy characterization discussed in this paper illustrates that sodium salt waste form matrices are somewhat similar to but not the same as those found in blended cement matrices which to date have been used in long-term thermodynamic modeling and contaminant sequestration as a first approximation. Utilizing the CBP generated data in long-term performance predictions provides for a more defensible technical basis in performance evaluations. - In addition, recent experimental studies related to technetium mobility indicate that conventional leaching protocols may not be conservative for direct disposal of Tc- containing waste forms in vadose zone environments. These results have the potential to influence the current Hanford supplemental waste treatment flow sheet and disposal conceptual design. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Active High Assurance Authentication Protocol (AHAAP)

The AHAAP Maturation Project involves maturation and evaluation of a patented zero-trust tamper-resistant high-assurance session-less dynamic and active device authentication protocol that simultaneously authenticates identity and provides integrity verification in a single step, substantially reducing the risk of cyberattack, and eliminating the need for costly and complex conventional communication security systems requirements (i.e., cryptography, Public Key Infrastructure (PKI), and key management). These cybersecurity attributes of the technology must be preserved when applying the technology to different cybersecurity solutions, including Command & Control (C&C), Over-the-Air (OTA) update, Common Access Card (CAC), and distributed energy resource (DER) implementations, among others. The technology research objective is to test and verify that the cybersecurity attributes of the technology are not degraded in different cybersecurity applications. The primary technology development objective is to build minimum viable products to demonstrate the technology addresses today’s cybersecurity threats so that prospective investors, strategic partners, regulatory agencies, and commercial customers can interact with and assess the protection assured by the technology. The AHAAP Maturation Project goal is to develop, test, and validate one or more AHAAP implementations. The AHAAP Maturation Project tasks are: (i) engineer AHAAP implementation software, (ii) build a functional prototype that implements the AHAAP software for demonstration, testing, analysis, and evaluation purposes, and (iii) generate a report detailing the results of the AHAAP C&C software and hardware implementation. The final project deliverables are: (i) AHAAP software implementation and prototype, (ii) a report from Sandia National Laboratories detailing the results of the AHAAP implementations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Fast Combinatorial Nonnegative Least Squares v 1.0

Software that employs an algorithm that rapidly and rigorously solves nonnegative least squares problems with many right hand sides. This is the latest internal version of MATLAB pseudocode that was described in a published in M. H. Van Benthem and M. R. Keenan, "Fast algorithm for the solution of largescale non-negativity-constrained least squares problems," J. Chemom. 18(10), 441-450 (2004). Example code of this algorithm was included in the publication. This algorithm was also patented in M. H. Van Benthem and M. R. Keenan, "Fast combinatorial algorithm for the solution of linearly constrained least squares problems,"USPTO, Ed., Sandia Corporation, USA (2008). SAND2019-10098 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Van Benthem, Mark↗

NASA NTRS1991

The AI Bus architecture for distributed knowledge-based systems

The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.

Schultz, Roger D.↗

DOE OSTI2013

Spectroscopic and physicochemical measurements for on-line monitoring of used nuclear fuel separation processes

Separation processes for used nuclear fuel are often complicated and challenging due to the high constraints in purity of the products and safeguards of the process streams. In order to achieve a safe, secure and efficient separation process, the liquid streams in the separation process require close monitoring. Due to the high radiation environment, sampling of the materials is difficult. Availability of a detection technique that is remote, non-destructive and can avoid time-delay caused by retrieving samples would be beneficial and could minimize the exposure to personnel and provide material accountancy to avoid diversion (non-proliferation). For example, Ultra Violet (UV), Visible (Vis), Near-Infrared (NIR) and Raman spectroscopy that detect and quantify elements present in used nuclear fuel, e.g. lanthanides, actinides and molecules such as nitrate, can be used. In this work, we have carried out NIR and Raman spectroscopy to study aqueous solutions composed of different concentrations of nitric acid, sodium nitrate, and neodymium at varied temperatures. A chemometric model for online monitoring based on the PLS-Toolbox (MATLAB) software has been developed and validated to provide chemical composition of process streams based on spectroscopic data. In conclusion, both of our NIR and Raman spectra were useful for H{sup +} and NO{sub 3} prediction, and only NIR was helpful for the Nd{sup 3+} prediction.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2014

Disease Prediction Models and Operational Readiness

INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the results are presented in this analysis.

biosecurity↗

DOE OSTI2018

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap'' between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

Anomaly detection, Attention, Recurrent Neural Net↗

DOE OSTI2019

CasCollect: Detecting cas genes and CRISPR arrays in raw sequence data v1

This software examines raw Illumina sequence data files for cas genes and CRISPR arrays, either to be used as evidence of genome editing or for basic analysis of bacterial genomes. It translates sequencing reads into peptide sequences, and runs these against sensitive hidden Markov models for numerous Cas protein families. The set of hit read seeds is then expanded leftward and rightward by sequence matching to remaining reads, and after multiple cycles, this small fraction of the original reads is assembled to produce Cas operons. A separate mode using known CRISPR repeat sequences for a genus are used to identify CRISPR arrays that may be in genomic locations that are isolated from Cas genes.SAND2019-11770 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Williams, Kelly↗

NASA NTRS2017

Non-Maximally Decimated Filter Banks Enable Adaptive Frequency Hopping for Unmanned Aircraft Vehicles

In the last few years, radio technologies for unmanned aircraft vehicle (UAV) have advanced very rapidly. The increasing need to fly unmanned aircraft systems (UAS) in the national airspace system (NAS) to perform missions of vital importance to national security, defense, and science has pushed ahead the design and implementation of new radio platforms. However, a lot still has to be done to improve those radios in terms of performance and capabilities. In addition, an important aspect to account for is hardware cost and the feasibility to implement these radios using commercial off-the-shelf (COTS) components. UAV radios come with numerous technical challenges and their development involves contributions at different levels of the design. Cognitive algorithms need to be developed in order to perform agile communications using appropriate frequency allocation while maintaining safe and efficient operations in the NAS and, digital reconfigurable architectures have to be designed in order to ensure a prompt response to environmental changes. Command and control (C2) communications have to be preserved during "standard" operations while crew operations have to be minimized. It is clear that UAV radios have to be software-defined systems, where size, weight and power consumption (SWaP) are critical parameters. This paper provides preliminary results of the efforts performed to design a fully digital radio architecture as part of a NASA Phase I STTR. In this paper, we will explain the basic idea and technical principles behind our dynamic/adaptive frequency hopping radio for UAVs. We will present our Simulink model of the dynamic FH radio transmitter design for UAV communications and show simulation results and FPGA system analysis.

UAV↗

DOE OSTI2023

Logical Activation Functions v.1.1

SAND2024-01501O Logical Activation Functions software is a PyTorch implementation from the paper, "Logical Activation Functions for Training Arbitrary Probabilistic Boolean Logic." The activation functions approximate logit-space marginalization of probabilistic truth tables from probabilistic interpretations of inputs. They also provide a general methodology to approximate logical relationships between abstract antecedents and consequents for machine learning architectures. They do not target any specific application or use-case. By training probabilistic truth tables, these activation functions can capture more expressive relationships in a neural network than typical elementwise activation functions. This code is only designed for a single compute node with a GPU and is limited to machine learning architectures than can fit within the memory of a single GPU. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Duersch, Jed↗

DOE OSTI2013

Timing is everything :

People save for retirement throughout their career because it is virtually impossible to save all you ll need in retirement the year before you retire. Similarly, without installing incremental amounts of clean fossil, renewable or transformative energy technologies throughout the coming decades, a radical and immediate change will be near impossible the year before a policy goal is set to be in place. Therefore, our research question is, To meet our desired technical and policy goals, what are the factors that affect the rate we must install technology to achieve these goals in the coming decades? Existing models do not include full regulatory constraints due to their often complex, and inflexible approaches to solve for optimal engineering instead of robust and multidisciplinary solutions. This project outlines the theory and then develops an applied software tool to model the laboratory-to-market transition using the traditional technology readiness level (TRL) framework, but develops subsequent and a novel regulatory readiness level (RRL) and market readiness level (MRL). This tool uses the ideally-suited system dynamics framework to incorporate feedbacks and time delays. Future energy-economic-environment models, regardless of their programming platform, may adapt this software model component framework or module to further vet the likelihood of new or innovative technology moving through the laboratory, regulatory and market space. The prototype analytical framework and tool, called the Technology, Regulatory and Market Readiness Level simulation model (TRMsim) illustrates the interaction between technology research, application, policy and market dynamics as they relate to a new or innovative technology moving from the theoretical stage to full market deployment. The initial results that illustrate the model s capabilities indicate for a hypothetical technology, that increasing the key driver behind each of the TRL, RRL and MRL components individually decreases the time required for the technology to progress through each component by 63, 68 and 64%, respectively. Therefore, under the current working assumptions, to decrease the time it may take for a technology to move from the conceptual stage to full scale market adoption one might consider expending additional effort to secure regulatory approval and reducing the uncertainty of the technology s demand in the marketplace.

Kobos, Peter Holmes↗

DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2023

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

In order to accelerate the digital transformation of airspace operations, a foundational framework and infrastructure for providing sustainable, data-driven, and cohesive decision-making digital services for both traditional and emergent air vehicles is being developed. The reference implementation of Digital Information Platform builds an ecosystem for the aviation community by providing access to a secure and trusted source of aviation data and services. Several key features and services have been implemented to enable secure data sharing, communication, and service registration on the Platform. The technical approach used to implement these features is presented here. NASA-developed integrated aviation data and machine learning based prediction services to optimize airspace operations are available on the Platform. These services are being evaluated in an operational environment by flight operators and the real-world benefits are being captured. The Platform fosters collaboration among industry and researchers to develop complex aviation services and the aim is to make it publicly accessible for consumption by the aviation community.

Digital Transformation↗

NASA NTRSDate not supplied

NASA’s Digital Information Platform to Accelerate the Transformation of the National Airspace System

Digital Transformation↗

NASA NTRS2019

NASA IV&V's Cyber Range for Space Systems

A cyber range is a virtual environment that is used for cyberwarfare training and cyber technology development. It provides tools that help strengthen the stability, security and performance of cyber infrastructures and IT systems. NASA IV&V has created an adaptable virtual environment that is representative of a typical NASA mission system of systems environment that serves multiple purposes: Training of network defenders. Ability to perform simulated red vs blue training events. Ability to deploy mission specific technologies in a cyber contested environment. Ability to demonstrate vulnerabilities and their impact to ground and space systemsThe overall system is comprised of more than 70 virtual machines and physical infrastructure to demonstrate real-world cyber attacks against: email users, wireless networks, and physically implanted devices with command and control call-back. The cyber range was built to emulate a NASA precipitation satellite infrastructure.This presentation will give an overview of the virtual cyber range and how it was used to conduct a simulate red vs blue exercise with the goal of compromising the simulated spacecraft that utilizes real NASA flight software.

test and evaluation DT&E↗

DOE OSTI2021

DAKOTA (Design Analysis Kit for Optimization and Terascale) v.6.14

Sandia's Dakota software supports science and engineering transformation through advanced exploration of simulations. Specifically, Dakota manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to: (1) enhance understanding of risk, (2) improve products, and (3) assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a computational model. Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, risk analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. The algorithms implemented in Dakota aim to address challenges in performing these analyses with complex science and engineering models from desktop to high performance computers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 SAND2021-10468 O

Adams, BrianM.↗

DOE OSTI2021

DAKOTA (Design Analysis Kit for Optimization and Terascale) v.6.16

Adams, BrianM.↗

DOE OSTI2016

Cybersecurity Assessment Methodology for International Safeguards

International nuclear safeguards are technical measures implemented by the International Atomic Energy Agency (IAEA) to verify the correctness and completeness of declarations made by States about their nuclear activities. The systems used to verify such activities include electronic and digital hardware and software components capable of data collection, processing, analysis, storage and transmission. Despite increasing efforts to protect digital systems against unauthorized access or attack through cybersecurity measures, these systems are not immune to cyber exploitation that could compromise their integrity or reliability. Previous versions of these systems did not include capabilities that exist today, such as BluetoothTM and GPS. The inclusion of these new capabilities, as well as new data processing and storage mechanisms, adds new attack vectors and opportunities for adversaries to exploit the devices that did not previously exist. As mentioned in the above referenced Cybersecurity for Safeguards study, cyber-domain vulnerabilities present risks to the equipment used to perform the international nuclear safeguards mission. The IAEA has produced guidance on the protection of nuclear facilities and their computer systems against cyber threats, but these documents do not specifically address the risks to safeguards or safeguards equipment. In response, the U.S. Department of Energy National Nuclear Security Administration (DOE/NNSA) Office of International Nuclear Safeguards/Safeguards Technology Development (NA-241) sponsored Sandia National Laboratories (Sandia, SNL) and the Idaho National Laboratory (Idaho, INL) to conduct a one-year study to evaluate cyber related vulnerabilities in safeguards equipment and develop recommendations for the mitigation of any identified risks.

97 MATHEMATICS AND COMPUTING↗