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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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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Neutron Activation Foil and Thermoluminescent Dosimeter Responses to a Lead Reflected Pulse of the CEA Valduc SILENE Critical Assembly

This benchmark experiment was conducted as a joint venture between the US Department of Energy (DOE) and the French Commissariat à l'Energie Atomique (CEA). Staff at the Oak Ridge National Laboratory (ORNL) in the US and the Centre de Valduc in France planned this experiment. The experiment was conducted on October 13, 2010 in the SILENE critical assembly facility at Valduc. Several other organizations contributed to this experiment and the subsequent evaluation, including CEA Saclay, Lawrence Livermore National Laboratory (LLNL), the Y-12 National Security Complex (NSC), Babcock International Group in the United Kingdom, and Los Alamos National Laboratory (LANL). The goal of this experiment was to measure neutron activation and thermoluminescent dosimeter (TLD) doses from a source similar to a fissile solution critical excursion. The resulting benchmark can be used for validation of computer codes and nuclear data libraries as required when performing analysis of criticality accident alarm systems (CAASs). A secondary goal of this experiment was to qualitatively test performance of two CAAS detectors similar to those currently and formerly in use in some US DOE facilities. The detectors tested were the CIDAS MkX and the Rocky Flats NCD-91. The CIDAS detects gammas with a Geiger-Muller tube, and the Rocky Flats detects neutrons via charged particles produced in a thin 6 LiF disc, depositing energy in a Si solid-state detector. These detectors were being evaluated to determine whether they would alarm, so they were not expected to generate benchmark quality data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Neutron Activation Foil and Thermoluminescent Dosimeter Responses to a Polyethylene Reflected Pulse of the CEA Valduc SILENE Critical Assembly

This benchmark experiment was conducted as a joint venture between the US Department of Energy (DOE) and the French Commissariat à l'Energie Atomique (CEA). Staff at the Oak Ridge National Laboratory (ORNL) in the US and the Centre de Valduc in France planned this experiment. The experiment was conducted on October 19, 2010 in the SILENE critical assembly facility at Valduc. Several other organizations contributed to this experiment and the subsequent evaluation, including CEA Saclay, Lawrence Livermore National Laboratory (LLNL), the Y-12 National Security Complex (NSC), Babcock International Group in the United Kingdom, and Los Alamos National Laboratory (LANL). The goal of this experiment was to measure neutron activation and thermoluminescent dosimeter (TLD) doses from a source similar to a fissile solution critical excursion. The resulting benchmark can be used for validation of computer codes and nuclear data libraries as required when performing analysis of criticality accident alarm systems (CAASs). A secondary goal of this experiment was to qualitatively test performance of two CAAS detectors similar to those currently and formerly in use in some US DOE facilities. The detectors tested were the CIDAS MkX and the Rocky Flats NCD-91. The CIDAS detects gammas with a Geiger-Muller tube and the Rocky Flats detects neutrons via charged particles produced in a thin 6 LiF disc depositing energy in a Si solid state detector. These detectors were being evaluated to determine whether they would alarm, so they were not expected to generate benchmark quality data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

First Annual Progress Report on Transmission of Information by Acoustic Communication along Metal Pathways in Nuclear Facilities

The objective of this project is to develop and demonstrate methods for transmission of information in nuclear facilities by acoustic means along existing in-place metal piping infrastructure. Pipes are omnipresent in a nuclear facility, and penetrate enclosures and partitions, such as the containment building wall. In the envisioned acoustic communication (AC) system, packets of information will be transmitted as guided acoustic waves along pipes. Performance of AC hardware and network protocols for efficient and secure communications under development in this project will be eventually evaluated in a representative nuclear power plant environment. Research efforts in the first year of this project have been focused on identification of appropriate transducers, and evaluation of their performance for information transmission along nuclear-grade metallic pipes. COMSOL computer simulations were performed to study acoustic wave generation, propagation, and attenuation on pipes. An experimental benchtop system was used to evaluate signal attenuation and spectral dispersion using piezo-electric transducers (PZTs) and electromagnetic acoustic transducers (EMATs). Communication protocols under evaluation consisted on-off keying (OOK) signal modulation, in particular amplitude shift keying (ASK) and phase shift keying (PSK). Tradeoffs between signal power and communication data rate were considered for ASK and PSK coding schemes.

42 ENGINEERING↗
DOE OSTI2022

ExaSGD: 2022 Kernel Thrust Activities

The Kernel Thrust milestone ADSE22-407 covers the development of device-capable optimization algorithms and solvers technologies required by the ExaSGD project’s software stack in order to solve security-constrained alternating current optimal power flow (SC-ACOPF) problems on emerging exascale architectures. To this extent, in FY22 the main objective of the Kernel Thrust was (i) provide sparse optimization solver that runs efficiently on hardware accelerator devices (i.e., NVIDIA and AMD GPUs) to perform intra-node computations, (ii) strengthen the reliability and increase the performance of the mixed-dense sparse (MDS) solver of HiOp for deployment on the FY22 target architectures, Summit and Crusher, and (iii) increase performance by improving the mathematical algorithm and refining the parallel MPI-based implementation of the coarse-grain parallel solver HiOp-PriDec for capabilities deployment on the FY22 target architectures, Summit and Crusher. This document presents the developments and contributions done by the Kernels Thrust Team in FY22 toward completion of the above-mentioned objectives. These contributions progressed along four main development (sub)thrusts: (1) Design and implementation of a sparse optimization solver for use on hardware accelerators; (2) Improvement of the mathematical algorithm and of the parallel implementation of HiOp-PriDec to ensure readiness and efficient coarse-grain parallelism for FY23 target exascale machine; and (3) Support Software and Application Development Thrusts of the exaSGD project in their deployment of the project’s software stack on AMD- and NVIDIA-based architectures. The development of the sparse optimization solver (thrust 1 above) was new in FY22 and resulted in a new sparse solver in HiOp (available as of version 0.6). The second development thrust was a continuation of the efforts from FY21 and improved the mathematical algorithm and the communication strategy of the HiOp-PriDec solver. The last developement thrust is a large collaborative effort. Namely, the project’s teams from multiple labs (LLNL, PNNL, ORNL, and NREL) performed large-scale demonstration of the ExaSGD software stack, namely the optimization solvers of HiOp interfaced with the modeling front-end ExaGO and the stochastic sampler PowerScenarios. These demonstration efforts solved large-scale instances of the SC-ACOPF challenge problem of medium network sizes (10, 000-bus system) and large number of contingencies on Summit (NVIDIA accelerators) and Crusher (AMD accelerators) systems at ORNL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

2015 LLNL Nuclear Forensics Summer Program

The Lawrence Livermore National Laboratory (LLNL) Nuclear Forensics Summer Program is designed to give graduate students an opportunity to come to LLNL for 8–10 weeks for a hands-on research experience. Students conduct research under the supervision of a staff scientist, attend a weekly lecture series, interact with other students, and present their work in poster format at the end of the program. Students also have the opportunity to meet staff scientists one-on-one, participate in LLNL facility tours (e.g., the National Ignition Facility and Center for Accelerator Mass Spectrometry) to gain a better understanding of the multidisciplinary, on going science at LLNL. Currently called the Nuclear Forensics Summer Program, this program began 15 years ago as the Actinide Sciences Summer Program. The program is run within the Glenn T. Seaborg Institute in the Physical and Life Sciences Directorate at LLNL. The goal of the Nuclear Forensics Summer Program is to facilitate the training of the next generation of nuclear scientists and engineers to solve critical national security problems in the field of nuclear forensics and have the students participate in conducting research at LLNL. We select students who are majoring in physics, chemistry, geology, mathematics, nuclear engineering, chemical engineering and environmental sciences. Students engage in research projects in the disciplines of actinide chemistry, radiochemistry, isotopic analysis, computational analysis, radiation detection, and nuclear engineering in order to strengthen the “pipeline” for future scientific disciplines critical to DHS (DNDO), NNSA.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

An Open Benchmark of One Million High-Fidelity Cislunar Trajectories

Cislunar space spans from geosynchronous altitudes to beyond the Moon and will underpin future exploration, science, and security operations. We describe and release an open dataset of one million numerically propagated cislunar trajectories generated with the open-source Space Situational Awareness Python package (SSAPy). The model includes high-degree Earth/Moon gravity, solar gravity, and Earth/Sun radiation pressure; other planetary gravities are omitted by design for computational efficiency. Initial conditions uniformly sample commonly used osculating-element ranges, and each trajectory is propagated for up to six years under a single, fixed start epoch. The dataset is intended as a reusable benchmark for method development (e.g., space domain awareness, navigation, and machine-learning pipelines), a reference library for statistical studies of orbit families, and a starting point for community-driven extensions (e.g., alternative epochs). We report empirically observed stability trends (e.g., a band near ~5 GEO and persistence of some co-orbital classes including L4/L5 librators) as dataset descriptors rather than new dynamical results. The chief contribution is the scale, fidelity, organization (CSV/HDF5 with full state time series and metadata), and open availability, which together lower the barrier to comparative and data-driven studies in the cislunar regime.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Rotational Millimeter-Wave Shoe Scanner Using the Discrete Fourier Transform for Backprojection-Based Image Reconstruction

An active 3D microwave / millimeter-wave shoe scanner was previously developed at the Pacific Northwest National Laboratory (PNNL) using two linear arrays scanned over a rectilinear aperture. The radar system chirps a frequency sweep from 10-40 GHz. These frequencies allow imaging through optically opaque material such as leather, rubber, plastics, and other dielectrics. The system was designed to detect concealed items in the soles of shoes while allowing people to leave their shoes on through a security checkpoint. To shrink the footprint of the system, a new iteration of the design has been developed that scans the two linear arrays over a circular aperture. This new footprint opens the possibility of it being installed in the floor of a cylindrical millimeter-wave body scanner. The backprojection-based multilayer dielectric image reconstruction developed at PNNL can easily handle arbitrary spatial sampling, accommodating the new rotational shoe scanner design. Commonly, the fast Fourier transform (FFT) is used to efficiently compute the range response from the data collected by the system as a preprocessing step to the backprojection algorithm. It was found that converting to range using the discrete Fourier transform (DFT) directly has some advantages over the FFT. For example, nonlinear and non-uniform frequency sweeps can easily be compensated for during the computation of the DFT and only the range bins of interest need to be computed and their spacing can be chosen arbitrarily. Because the range conversion step of the image reconstruction is the fastest part of the process there is very little speed penalty for using the DFT over the FFT and it can even increase the speed of image reconstruction when the ranges of interest are fewer than the total span that is calculated in the FFT.

Millimeter-wave imaging, microwave imaging, shoe s↗
DOE OSTI2024

2023 Operational Assessment Report: Argonne Leadership Computing Facility

In 2004, the U.S. Department of Energy’s (DOE’s) Advanced Scientific Computing Research (ASCR) program established the Leadership Computing Facility (LCF) with a mission to provide the world’s most advanced computational resources to the open science community. The LCF is a huge investment in the nation’s scientific and technological future, inspired by a growing demand for large-scale computing and its impact on science and engineering. The LCF operates two world-class centers in support of open science at Argonne National Laboratory (Argonne) and at Oak Ridge National Laboratory (Oak Ridge) and deploys diverse machines that are among the most powerful systems in the world today. The LCF ranks among the top U.S. scientific facilities delivering impactful science. The work performed at these centers informs policy decisions and advances innovations in far-reaching areas such as energy assurance, ecological sustainability, and global security. The leadership-class systems at Argonne and Oak Ridge operate around the clock every day of the year. The high level of services these centers provide and the exceptional science they produce justify their existence to the DOE Office of Science and the U.S. Congress

97 MATHEMATICS AND COMPUTING↗
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 OSTI2023

KinCat v.1.0

SAND2024-02099O The software is designed to allow researchers to perform kinetic Monte Carlo (KMC) simulations of catalytic reactions on a 2D lattice. The code is written efficiently to run on a variety of shared memory computing architectures (e.g. GPU, multi-core) and to natively express the full complexity of lateral interactions on reaction rates. The software allows researchers to perform KMC simulations of catalytic reactions on a 2D lattice. It uses parallel shared-memory computing architectures to reduce run-times and allows for simultaneous simulation of multiple independent runs. 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.

Najm, Habib↗
DOE OSTI2021

Studying visual search without an eye tracker: an assessment of artificial foveation

Abstract Eye tracking is a useful tool for studying human cognition, both in the laboratory and in real-world applications. However, there are cases in which eye tracking is not possible, such as in high-security environments where recording devices cannot be introduced. After facing this challenge in our own work, we sought to test the effectiveness of using artificial foveation as an alternative to eye tracking for studying visual search performance. Two groups of participants completed the same list comparison task, which was a computer-based task designed to mimic an inventory verification process that is commonly performed by international nuclear safeguards inspectors. We manipulated the way in which the items on the inventory list were ordered and color coded. For the eye tracking group, an eye tracker was used to assess the order in which participants viewed the items and the number of fixations per trial in each list condition. For the artificial foveation group, the items were covered with a blurry mask except when participants moused over them. We tracked the order in which participants viewed the items by moving their mouse and the number of items viewed per trial in each list condition. We observed the same overall pattern of performance for the various list display conditions, regardless of the method. However, participants were much slower to complete the task when using artificial foveation and had more variability in their accuracy. Our results indicate that the artificial foveation method can reveal the same pattern of differences across conditions as eye tracking, but it can also impact participants’ task performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

ExaSGD: 2021 Kernel Thrust Activities

The Kernel Thrust milestone ADSE22-214 covers the development of device-capable optimization algorithms and solvers technologies required by the ExaSGD project’s software stack in order to solve security-constrained alternating current optimal power flow (SC-ACOPF) problems on emerging exascale architectures. To this extent, in FY21 the main objective of the Kernel Thrust was (i) provide robust optimization solver(s) that run efficiently on hardware accelerator devices (i.e., NVIDIA and AMD GPUs) to perform intra-node computations and (ii) provide coarse-grain parallel optimization capabilities that exploit the decomposition opportunities present in the SC-ACOPF challenge problems to provide exascale-capable solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reinforcement Learning Approach to Cybersecurity in Space (RELACSS)

Securing satellite groundstations against cyber-attacks is vital to national security missions. However, these cyber threats are constantly evolving. As vulnerabilities are discovered and patched, new vulnerabilities are discovered and exploited. In order to automate the process of discovering existing vulnerabilities and the means to exploit them, a reinforcement learning framework is presented in this report. We demonstrate that this framework can learn to successfully navigate an unknown network and detect nodes of interest despite the presence of a moving target defense. The agent then exfiltrates a file of interest from the node as quickly as possible. This framework also incorporates a defensive software agent that learns to impede the attacking agents progress. This setup allows for the agents to work against each other and improve their abilities. We anticipate that this capability will help uncover unforeseen vulnerabilities and the means to mitigate them. The modular nature of the framework enables users to swap out learning algorithms and modify the reward functions in order to adapt the learning tasks to various use cases and environments. Several algorithms, viz., tabular Q learning, deep Q networks, proximal policy optimization, advantage actor-critic, generative adversarial imitation learning, are explored for the agents and the results highlighted. The agent learns to solve the tasks in a light-weight abstract environment. Once the agent learns to perform sufficiently well, it can be deployed in a minimega virtual machine environment (or a real network) with wrappers that map abstract actions to software commands. The agent also uses a local representation of the actions called a ‘slot-mechanism’. This allows the agent to learn in a certain network and generalize it to different networks. The defensive agent learns to predict the actions taken by an offensive agent and uses that information to anticipate the threat. This information can then either be used to raise an alarm or to take actions to thwart the attack. We believe that with the appropriate reward design, a representative environment, and action set, this framework can be generalized to tackle other cybersecurity tasks. By sufficiently training these agents, we can anticipate vulnerabilities leading to robust future designs. We can also deploy automated defensive agents that can help secure satellite groundstation and their vital national security missions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Cybersecurity Center for Offshore Wind Energy (Final Project Report)

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models with SCADA infrastructure, and deployed a scaled physical turbine and associated sensors. High-resolution operational and side-channel data streams were collected and used to refine machine-learning (ML)-based attack detection systems and to extend the WindCRAFT framework to multi-turbine threat scenarios. The project demonstrated a realistic, scalable environment for evaluating cyber threats, validated attack detection approaches using enriched datasets, and identified new multi-turbine and inter-turbine communication attack vectors. The resulting testbed, models, and security mechanisms provide a foundation for ongoing R&D and deployment of cyber-resilient offshore wind energy systems.

17 WIND ENERGY↗
DOE OSTI2020

FALCON: Framework for Anomaly Detection in Industrial Control Systems

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Cyberspace security system

A system evaluates reliability, performance and/or safety by automatically assessing the targeted system's requirements. A cost metric quantifies the impact of failures as a function of failure cost per unit of time. The metrics or measurements may render real-time (or near real-time) outcomes by initiating active response against one or more high ranked threats. The system may support or may be executed in many domains including physical domains, cyber security domains, cyber-physical domains, infrastructure domains, etc. or any other domains that are subject to a threat or a loss.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Role-based access control permissions

Devices, systems, and methods for role-based access control permissions are disclosed. One method includes a policy decision point that receives up-to-date security context information from one or more outside sources to determine whether to grant access for a data client to a portion of the system and creates an access vector including the determination; receiving, via a policy agent, a request by the data client for access to the portion of the computing system by the data client, wherein the policy agent checks to ensure there is a session established with communications and user/application enforcement points; receiving, via communications policy enforcement point, the request from the policy agent, wherein the communications policy enforcement point determines whether the data client is an authorized node, based upon the access vector received from the policy decision point; and receiving, via the user/application policy enforcement point, the request from the communications policy enforcement point.

42 ENGINEERING↗
DOE OSTI2021

Resilient Information Architecture Platform for Smart Grid (RIAPS)

A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. To address these challenges, the future “Smart Grid” management will need to transition from centralized to coordinated distributed control paradigm. Reliable operation of the Smart Grid depends on distributed intelligence realized through software applications that run on distributed computing devices attached to the power system to collect data and collaboratively manage resources. However, much of the existing software for Smart Grid-enabled devices is either proprietary or developed with custom solutions, which limits interoperability among the heterogeneous devices and hinders the ability to manage system-level reliability, security, and resiliency requirements. Additionally, this approach makes Smart Grid applications hard to maintain, evolve, verify, and replace; resulting in high development and deployment costs. Further development of the Smart Grid requires a reusable software base-layer to move from hard-coded functionality to a plug-and-play architecture capable of managing system-level objectives and constraints in addition to providing consistent common services across heterogeneous devices and applications. Vanderbilt University, in collaboration with North Carolina State University and Washington State University has developed a foundation ‘software platform’ for developing and deploying robust, reliable, effective and secure software applications for the Smart Grid. The Resilient Information Architecture Platform for the Smart Grid (RIAPS) provides core services for building effective and powerful smart grid applications. It offers unique services for real-time data dissemination, fault tolerance, and coordination across apps distributed over the network.

24 POWER TRANSMISSION AND DISTRIBUTION↗