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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

An Automated Flying-Insect Detection System

An automated flying-insect detection system (AFIDS) was developed as a proof-of-concept instrument for real-time detection and identification of flying insects. This type of system has use in public health and homeland-security decision support, agriculture and military pest management, and/or entomological research. Insects are first lured into the AFIDS integrated sphere by insect attractants. Once inside the sphere, the insect s wing beats cause alterations in light intensity that is detected by a photoelectric sensor. Following detection, the insects are encouraged (with the use of a small fan) to move out of the sphere and into a designated insect trap where they are held for taxonomic identification or serological testing. The acquired electronic wing-beat signatures are preprocessed (Fourier transformed) in real time to display a periodic signal. These signals are sent to the end user where they are graphically. All AFIDS data are preprocessed in the field with the use of a laptop computer equipped with LabVIEW. The AFIDS software can be programmed to run continuously or at specific time intervals when insects are prevalent. A special DC-restored transimpedance amplifier reduces the contributions of low-frequency background light signals, and affords approximately two orders of magnitude greater AC gain than conventional amplifiers. This greatly increases the signal-to-noise ratio and enables the detection of small changes in light intensity. The AFIDS light source consists of high-intensity Al-GaInP light-emitting diodes (LEDs). The AFIDS circuitry minimizes brightness fluctuations in the LEDs and when integrated with an integrating sphere, creates a diffuse uniform light field. The insect wing beats isotropically scatter the diffuse light in the sphere and create wing-beat signatures that are detected by the sensor. This configuration minimizes variations in signal associated with insect flight orientation. Preliminary data indicate that AFIDS has sufficient sensitivity and frequency measuring capability to differentiate between male and female mosquitoes (Figure 1, bottom panel) and fruit flies (data not shown). Similar studies show that AFIDS can be utilized to detect discrete differences between two mosquito species, Aedes aegypti and Aedes albopictus. When fully deployable, a wireless network of AFIDS monitors could be used in combination with other remotely sensed data and visually displayed in a geographic information system (GIS) to provide real-time surveillance (see Figure 2). More accurate and sensitive insect population forecasts and effective rapid response and mitigation of insect issues would then be possible.

Vann, Timi↗
DOE OSTI2019

Manhattan District: A Lab-Embedded Entrepreneur Program

The goals of the Manhattan District are to attract top scientific innovators to New Mexico to start businesses that focus on breakthrough national security technologies, support them with access to scientific leaders, partner networks, and funding to ensure success, build economic prosperity locally and nationally, leverage national networks with UC, TAMU, Battelle and strategic partners in industry and venture capital, and build on proven models from other DOE National Labs to enhance NNSA missions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Evolution of Volatile Memory Forensics

The collection and analysis of volatile memory is a vibrant area of research in the cybersecurity community. The ever-evolving and growing threat landscape is trending towards fileless malware, which avoids traditional detection but can be found by examining a system’s random access memory (RAM). Additionally, volatile memory analysis offers great insight into other malicious vectors. It contains fragments of encrypted files’ contents, as well as lists of running processes, imported modules, and network connections, all of which are difficult or impossible to extract from the file system. For these compelling reasons, recent research efforts have focused on the collection of memory snapshots and methods to analyze them for the presence of malware. However, to the best of our knowledge, no current reviews or surveys exist that systematize the research on both memory acquisition and analysis. We fill that gap with this novel survey by exploring the state-of-the-art tools and techniques for volatile memory acquisition and analysis for malware identification. For memory acquisition methods, we explore the trade-offs many techniques make between snapshot quality, performance overhead, and security. For memory analysis, we examined the traditional forensic methods used, including signature-based methods, dynamic methods performed in a sandbox environment, as well as machine learning-based approaches. We summarize the currently available tools, and suggest areas for more research.

Nyholm, Hannah↗
DOE OSTI2020

Cyber physical attack detection

A cyber-security threat detection system and method stores physical data measurements from a cyber-physical system and extracts synchronized measurement vectors synchronized to one or more timing pulses. The system and method synthesizes data integrity attacks in response to the physical data measurements and applies alternating parametrized linear and non-linear operations in response to the synthesized data integrity attacks. The synthesis renders optimized model parameters used to detect multiple cyber-attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On-Site Inspection RadioIsotopic Spectroscopy (Osiris) System-Integration and Field-Testing Report

We have designed a system for the acquisition and analysis of high-resolution gamma-ray spectra during on-site inspections under the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The On-Site Inspection RadioIsotopic Spectroscopy—OSIRIS —gamma-ray spectrometer is a mechanically-cooled, battery-powered ORTEC IDM-200V, chosen to avoid the need for liquid nitrogen during on-site inspections. The spectrometer is controlled and the spectral data analyzed by a Panasonic Toughbook notebook computer. Spectral data analysis is similar to that performed by traditional gamma-ray spectrometers, except the OSIRIS gamma-ray spectral data are filtered to display only radioisotopic information relevant to CTBT on-site inspections, for example, the fission-product isotope Zr-95 and its daughter isotope Nb-95. The OSIRIS software assists the user during operation with software checklists for startup and shutdown, and menu-selected display windows during energy calibration and data acquisition. OSIRIS can be operated in either sample-counting mode or the in-situ mode. In this report, the extensive system-integration tests of the OSIRIS system carried out in 2015-2017 are described, including tests at Idaho National Laboratory, at the Nevada National Security Site (formerly, the Nevada Test Site), and at Pacific Northwest National Laboratory in Richland, Washington. We also note recent software enhancements and the steps to our successful commercialization of OSIRIS.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Anomaly detection↗
DOE OSTI2020

Automated Discovery for Emulytics

Sandia has an extensive background in cybersecurity research and is currently extending its state-of-the-art modeling via emulation capability. However, a key part of Sandia's modeling methodology is the discovery and specification of the information-system under study, and the ability to recreate that specification with the highest fidelity possible in order to extrapolate meaningful results. This work details a method to conduct information system discovery and develop tools to enable the creation of high-fidelity emulation models that can be used to enable assessment of our infrastructure information system security posture and potential system impacts that could result from cyber threats. The outcome are a set of tools and techniques to go from network discovery of operational systems to emulating complex systems. As a concrete usecase, we have applied these tools and techniques at Supercomputing 2016 to model SCinet, the world's largest research network. This model includes five routers and nearly 10,000 endpoints which we have launched in our emulation platform.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Learning Solutions for a Stable Grid Recovery

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

CMIP6-based Multi-model Streamflow Projections over the Conterminous US

This dataset presents an ensemble of streamflow projections based on the hydroclimate projections dataset supporting the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). The six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6) downscaled using statistical (i.e., DBCCA) and dynamical (i.e., RegCM) and bias-corrected using two meteorological reference observations (Daymet & Livneh) are driven through two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. This leads to the production of an ensemble of hydroclimate projections, including total runoff (surface runoff and baseflow) for 1980–2019 baseline and 2020–2059 near-term future periods under multiple emission scenarios at 1/24° (~4 km) spatial resolution across the CONUS. The total runoff projections are routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model to produce an ensemble of streamflow projections for both periods across 2.7 million NHDPlusV2 stream reaches in the CONUS.

13 HYDRO ENERGY↗
DOE OSTI2019

Materials Characterization and Interrogation using Terahertz Radiation

Terahertz (THz) radiation is a powerful tool for characterizing materials and interrogating complex systems. The low-energy, non-ionizing nature of THz photons make it ideal for use as a non-destructive, non-contacting probe. Identification of materials by their unique THz response is possible through various absorption mechanisms, including vibrational (phonon) and rotational spectra. The Drude-like response of materials allows for non-contact electrical conductivity measurements as well. In the additive and advanced manufacturing realm, high sampling rates (kHz) and ability to extract electrical conductivity and composition makes in-situ THz imaging and spectroscopy a prime candidate for monitoring melt-pool dynamics in metal 3D printers. The fiberization of pulsed lasers systems enables the use of THz radiation to monitor oxide layer growth and characterize microstructure in challenging environments, whether ex-situ (e.g. post irradiation hot-cells) or in-situ (e.g. Transient Test Reactor, TREAT), through time of flight measurements and unique vibrational spectra of materials, providing crucial data on previously out of reach processes. While security imaging using THz radiation is already employed in airport body scanners, further miniaturization and ruggedization of THz systems enables materials characterization during field operations, adding a necessary and complementary tool to the arsenal of spectroscopic technologies available to the military and first responders. Cybersecurity is also a major focus for the coming decades; the use of THz interrogation methods to monitor integrated circuit operations provides a non-contacting, undetectable probe for system verification.

36 MATERIALS SCIENCE↗
NASA NTRS2005

An Automated Flying-Insect-Detection System

An automated flying-insect-detection system (AFIDS) was developed as a proof-of-concept instrument for real-time detection and identification of flying insects. This type of system has use in public health and homeland security decision support, agriculture and military pest management, and/or entomological research. Insects are first lured into the AFIDS integrated sphere by insect attractants. Once inside the sphere, the insect's wing beats cause alterations in light intensity that is detected by a photoelectric sensor. Following detection, the insects are encouraged (with the use of a small fan) to move out of the sphere and into a designated insect trap where they are held for taxonomic identification or serological testing. The acquired electronic wing beat signatures are preprocessed (Fourier transformed) in real-time to display a periodic signal. These signals are sent to the end user where they are graphically displayed. All AFIDS data are pre-processed in the field with the use of a laptop computer equipped with LABVIEW. The AFIDS software can be programmed to run continuously or at specific time intervals when insects are prevalent. A special DC-restored transimpedance amplifier reduces the contributions of low-frequency background light signals, and affords approximately two orders of magnitude greater AC gain than conventional amplifiers. This greatly increases the signal-to-noise ratio and enables the detection of small changes in light intensity. The AFIDS light source consists of high-intensity Al GaInP light-emitting diodes (LEDs). The AFIDS circuitry minimizes brightness fluctuations in the LEDs and when integrated with an integrating sphere, creates a diffuse uniform light field. The insect wing beats isotropically scatter the diffuse light in the sphere and create wing beat signatures that are detected by the sensor. This configuration minimizes variations in signal associated with insect flight orientation.

Vann, Timi↗
DOE OSTI2020

COMET/ZEUS Activation Foils Report for Irradiations IER-241 and IER-261

The goal of this work was to determine the internal neutron spectrum of the COMET/ZEUS assembly using activation foils. Similar measurements were performed on the COMET assembly when it was located at the Los Alamos Critical Experiments Facility (LACEF) at the Los Alamos National Laboratory (LANL). The present measurements were performed after the COMET assembly was moved to the National Criticality Experiments Research Center (NCERC) located in the Device Assembly Facility (DAF) at the Nevada National Security Site (NNSS). The December 2012 COMET/ZEUS irradiation was the first in a series of experiments, and the COMET/ZEUS assembly was selected because of operational readiness and authorization to run specific targets. The August 2013 irradiation was the second in the series and involved only small changes in the configuration. This report describes the unfolding of the activation foils to determine the internal neutron spectra.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

A Cybersecurity Threat Profile for a Connected Lighting System

In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Networked Microgrid Cybersecurity Architecture Design Guide: A New Jersey TRANSITGRID Use Case

Microgrids require reliable communication systems for equipment control, power delivery optimization, and operational visibility. To maintain secure communications, Microgrid Operational Technology (OT) networks must be defensible and cyber-resilient. The communication network must be carefully architected with appropriate cyber-hardening technologies to provide security defenders the data, analytics, and response capabilities to quickly mitigate malicious and accidental cyberattacks. In this work, we outline several best practices and technologies that can support microgrid operations (e.g., intrusion detection and monitoring systems, response tools, etc.). Then we apply these recommendations to the New Jersey TRANSITGRID use case to demonstrate how they would be deployed in practice.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

ReNew100: Reliable Power System Operation with 100% Renewable Generation

The ReNew100 project has developed and demonstrated an operator support system (OSS) to operate power systems with 100% renewable power generation from inverter-based resources (IBRs) like wind and solar that significantly reduces the risk of widespread power outages in a simulated operational environment at Technology Readiness Level (TRL) 6. The OSS achieves resilient operation for power systems under changing operation modes including varying combinations of conventional and renewable generation, including cases with 100% renewable generation from wind and solar IBRs. The OSS continuously monitors the N-1 security of the power system, which refers to the ability of the power systems to survive single outages of power system elements, like the loss of a generator or a power line, without causing widespread power outages beyond those expected and planned for. If the OSS identifies that the system is not N-1 secure, an automatic Controller Parameter Optimization (CoPO) developed within Renew100 is activated to optimize the N-1 security of the system by tuning controller parameters within different assets like batteries, wind, and solar plants. The core innovation of ReNew100 is the development of this CoPO as well as the demonstration of the OSS for a real-time simulation of the Hawai'i Island power system. Moreover, Renew100 has developed new model calibration techniques for dynamic power system models and demonstrated them for Hawai'i Island.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Blueprint: Open Worldwide Application Security Project Top 10 Common Weakness Enumerations for Electric Vehicle Supply Equipment

This report categorizes the top 10 security weaknesses in electric vehicle supply equipment (EVSE) using a similar approach by the Open Worldwide Application Security Project (OWASP) Top 10, and it describes example exploitations, methods for prevention, and related common weakness enumerations (CWEs) for each category. The weaknesses listed in this report could lead to security issues or vulnerabilities in EVSE if they are exploited by an attacker to compromise the confidentiality, integrity, or availability of a system. This report also recommends EVSE industry best practices and remediations for the identified weaknesses to use as guidelines for their secure products and systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions

In February 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American Artificial Intelligence Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a government-wide strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative called for Federal agencies to: Prioritize AI research and development investments, Enhance access to high-quality cyberinfrastructure and data, Ensure that the US maintains an international leadership role in the development of technical standards for AI, and Provide education and training opportunities to prepare the American workforce for the new era of AI. The mission of the Department of Energy (DOE) is to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. In terms of Science and Innovation, the DOE’s mission is to maintain a vibrant US effort in science and engineering as a cornerstone of our economic prosperity with clear leadership in strategic areas. From July to October in 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four AI for Science Town Hall meetings in Chicago, Oak Ridge, Berkeley, and Washington DC. The four meetings were attended by over 1300 scientists from the 17 DOE Labs, 39 companies, and over 90 universities. The goal of the Town Hall series was ‘to examine scientific opportunities in the areas of artificial intelligence, Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these.’ The discussions at the meetings were captured in the final report of the AI for Science Town Hall meetings.

42 ENGINEERING↗
DOE OSTI2022

Resilience Evaluation and Enhancement for Island City Integrated Energy Systems

Extreme natural hazards, such as hurricanes or earthquakes, have a high probability of threatening energy supply security and causing high-order contingencies to island city-integrated energy systems (IC-IESs). To better evaluate and enhance resilience, a novel approach is proposed in this work for IC-IESs. The resilience of an IC-IES is analyzed from both the system level and the component level. At the system level, the impacts of extreme natural disasters are quantified. At the component level, the importance of individual components is analyzed through pre-failure and post-failure indices. The pre-failure index identifies the system’s weak links before an energy interruption, and the post-failure index determines the optimal repair strategy to restore the service. The proposed indices are solved by the impact increment method (IIM), which significantly improves computational efficiency without much affecting result accuracy. Numerical simulation studies are conducted on the modified Barry Island IES and IES E123-G48-H32 test systems. Furthermore, the results validate the effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗