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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Gramian-Based Characterization of Network Vulnerability to Nodal Impulse Inputs: Preprint

Impulsive inputs applied at influential nodes of a network system may result in undesirable behavior and degrade performance. This paper proposes the notion of vulnerability matrix (VM) to characterize the effect of impulse inputs on a network following either discrete-time or continuous-time dynamics. The VM describes the first-order effects of impulse inputs on edge flows and is based on the controllability Gramian. We provide explicit expressions for the elements of the vulnerability matrix for the class of directed line networks in terms of the edge weights. Simulations validate our results and highlight the utility of the proposed metric in capturing the transient effects of nodal impulse inputs on edge flows.

controllability Gramian↗
DOE OSTI2018

Lung tissue destruction by proteinase 3 and cathepsin G mediated elastin degradation is elevated in chronic obstructive pulmonary disease

Highlights: • Identification of elastin fragments specifically generated by cathepsin G (CG) and proteinase 3 (PR3) respectively. • Development of the two assays EL-CG and ELP-3 which are specific for the CG or PR3 degraded elastin neo-epitopes. • Significant higher concentrations of EL-CG and ELP-3 in patients diagnosed with COPD compared to healthy subjects. Chronic obstructive pulmonary disease (COPD) is characterized by high levels of protease activity leading to degradation of elastin followed by loss of elasticity of the lung and the development of emphysema. Elastin is an essential structural component of the lung parenchyma to support the expansion and recoil of the alveoli during breathing. The lung extracellular matrix is vulnerable to pathological structural changes upon upregulation of serine proteases, including cathepsin G (CG) and proteinase 3 (PR3). In this study, we explored the diagnostic features of elastin neo-epitopes generated by CG and PR3. Two novel competitive enzyme-linked immunosorbent assays (ELISA) measuring CG and PR3 generated elastin fragments (EL-CG and ELP-3 respectively) were developed for assessment in serum. Both assays were technically robust and biologically validated in serum from patients with COPD. Serological levels of both elastin fragments were significantly elevated in patients with COPD compared to healthy controls. These data suggest that EL-CG and ELP-3 may serve as plausible biologic markers of destructive changes in COPD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Oxidation behaviors of matrix-grade graphite during water vapor ingress accidents for high temperature gas-cooled reactors

Water ingress into cores of high temperature gas-cooled reactors (HTGRs) can cause serious safety problems in graphitic components, especially for matrix graphite, which is more vulnerable than nuclear graphite. Here in this study, oxidation behaviors of a matrix-grade graphite, ARB-B1, were investigated under simulated water ingress accidental conditions. The oxidation tests were conducted in water vapor/helium mixed atmospheres at elevated temperatures up to 1200 °C. Oxidation rates and activation energy were evaluated by mass loss measurements. The activation energy was 121.5 kJ/mol in the considered temperature range, indicating only one oxidation regime-in-pore diffusion assisted by cracks. The cracks originated from the fabrication process, acting as penetration paths for the oxidant, played an important role in the oxidation behaviors. Ungraphitized binder oxidation occurred at all temperatures, while apparent oxidation of filler particles started from 1100 °C. Moreover, oxidation effects on compressive properties and hardness of the matrix graphite were discussed in terms of oxidized microstructures. This first detailed study on matrix graphite oxidation in water vapor provides important kinetics knowledge and microstructural and mechanical data for accidental scenarios of HTGRs as well as for development of new matrix graphite materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimental Characterization of Hygrothermal Aging: Competition Between Thermo-Oxidation and Hydrolysis Phenomena

Abstract In this work, we have presented a comparison between effects of accelerated thermo-oxidative, hydrolytic and hygrothermal aging on the mechanical and chemical properties of a polyurethane based (PUB) adhesive. The adhesive is a flexible adhesive which is extensively used in automobile industry as a glass sealant. During service life it gets exposed to the hygrothermal environment and the integrity of matrix interface is most vulnerable. This study focuses on weighing and comparing effects of different aging environments on material behavior; and it shows that hygrothermal aging environment results as a competition between two sub-aging phenomena i.e., thermo-oxidation and hydrolytic aging. Samples were exposed to 0%RH, 80%RH and submerged condition in distilled water. Uni-axial tensile tests, and cross-link density analysis by swelling test were carried out on as-received and aged samples. The aging process was conducted at three different temperatures (60°C, 80°C and 95°C) and for four different exposure durations of 1, 10, 30, and 90 days. This work confirms that the effect of hygrothermal aging is a result of competition between thermo-oxidation and hydrolytic aging environments. The total mechanical and environmental damage incurred by specimens is a collaborative effect of all exposure environments involved i.e., aging time, temperature, oxygen, water and humidity. The results are in agreement with the chemical test outcomes as well.

Shaafaey, Mamoon↗
DOE OSTI2025

The Future of X-ray Irradiation: Addressing Supply Chain Risks and Opportunities (UUR Edition)

This study supports the Office of Radiological Security’s (ORS) mission of eliminating cesium irradiators by analyzing the supply chain for self-shielded X-ray irradiators (SSXIs), identifying potential risks, and proposing mitigation measures. The research focuses on the primary components of SSXIs, including X-ray tubes, controllers, generators, and coolers or chillers, and evaluates their vulnerabilities using a comprehensive risk matrix framework. The methodology includes subject matter expert (SME) interviews with relevant manufacturers and major stakeholders, a deep literature review, and a meta-analysis of maintenance reports provided by SSXI end users. Results show that while the SSXI market is small, it’s growing, and the highly global nature of the supply chain may create vulnerabilities for critical SSXI components (X-ray tubes are the most vulnerable, followed by generators and controllers). This research communicates necessary information to address concerns of current and future end users, especially those interested in transitioning away from radioactive sources, and informs future policy aimed at supporting the irradiation industry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Coupling of the Electricity and Transportation Sectors - Part I: Sector Overviews

“Sector coupling” is a concept that addresses potential designs for the future power industry. Traditionally, the energy sectors, i.e., electricity supply, transport, and industry, have functioned largely independently from one another. Thus, the concept of sector coupling means that the electricity sector would become the central pillar of the energy system, supplying the other sectors – transportation and industry – with energy in various ways, a scheme described by the term “Power-to-X”. This report delves into the ambitious goal set by the United States to achieve net-zero emissions by 2050, with a pivotal milestone of making half of all passenger vehicles sold in America zero-emission by 2030. Central to achieving this objective is the electrification of the transportation sector, resulting in complex interactions between this sector and the electric industry. As electric vehicles become more prevalent, a shift in the dependency on electricity for vehicle charging emerges, alongside a reduction in the electricity demand associated with maintaining the fossil fuel supply chain. Both the electricity and fossil fuel have been identified as critical infrastructure sectors, the transportation sector's historical reliance on fossil fuels, and the mature supply chain supporting them are explored. This study emphasizes the existing sectoral coupling between the electric energy and transportation sectors, extending beyond vehicle charging to essential operations at refineries, pipeline facilities, storage, and fuel extraction. Recognizing the critical dependency of fossil fuel supply chain operations on the electric sector, this research underscores the need for a comprehensive view of the evolving dynamics between the transportation and electric sectors. This study has been split into two segments, Part-I provides an overview of the existing fossil fuel supply chain architecture and the electrical sector. We provide an in-depth analysis of the grid-transportation sectoral coupling, its interactions, the vulnerabilities and essential take-aways that would help us to address them and apply towards an electrified transportation sector in the future. We engaged with stakeholders to understand their expectations in enabling an electrified transportation. We accumulate the understandings from the current fossil fuel-based system in developing a risk matrix, by identifying all the threats and vulnerabilities and how would they apply to both the current and future transportation systems in Part-II of this report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Effects of Jacobian Matrix Regularization on the Detectability of Adversarial Samples

The well-known vulnerability of Deep Neural Networks to adversarial samples has led to a rapid cycle of increasingly sophisticated attack algorithms and proposed defenses. While most contemporary defenses have been shown to be vulnerable to carefully configured attacks, methods based on gradient regularization and out-of-distribution detection have attracted much interest recently by demonstrating higher resilience to a broad range of attack algorithms. However, no study has yet investigated the effect of combining these techniques. In this paper, we consider the effect of Jacobian matrix regularization on the detectability of adversarial samples on the CIFAR-10 image benchmark dataset. We find that regularization has a significant effect on detectability, and in some cases can make an undetectable attack on a baseline model detectable. In addition, we give evidence that regularization may mitigate the known weaknesses of detectors to high-confidence adversarial samples. The defenses we consider here are highly generalizable, and we believe they will be useful for further investigations to transfer machine learning robustness to other data domains.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

< c+a > dislocations in {101 - 2} twins in Mg: A kinematic and energetic requirement

Consideration of published TEM and molecular dynamics studies of interactions between matrix dislocations and {101¯2} twin boundaries (TB) in hexagonal close packed metals inspires a hypothesis regarding the formation of dislocations with < c+a > Burgers vectors inside the twins. It was previously reported that when basal < a > dislocations from the matrix are swept by an advancing TB, they are converted into partial dislocations bounding basal I 1 stacking faults (SFs) within the twin. In the present work, we show that this dislocation configuration near TB renders it energetically favorable for a pair of partial dislocations to constrict into a dislocation within the twin. New in situ TEM observations confirm this hypothesis by revealing long < c+a > dislocation dipoles terminated by the TB at one end and by I1 SFs within the twin at the other end. Finally, the resulting near-screw < c+a > dislocation dipoles are observed to be glissile, enabling self-annihilation in some circumstances. Thus, rather than requiring a high energy dislocation pile-up to press two < a > dislocations through a TB into a single < c+a > dislocation, it is demonstrated that < c+a > dislocations naturally form, within {101¯2} twins in Mg, as TB migrates into a matrix, which inevitably contains dislocations with < a > Burgers vectors. Furthermore, these observations help to explain how the twins themselves may subsequently deform and why they may be particularly vulnerable to plastic, shear localization.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Detecting False Data Injection Attacks Against Power System State Estimation With Fast Go-Decomposition Approach

State estimation is a fundamental function in modern energy management system, but its results may be vulnerable to false data injection attacks (FDIAs). FDIA is able to change the estimation results without being detected by the traditional bad data detection algorithms. In this paper, we propose an accurate and computational attractive approach for FDIA detection. In this work, we first rely on the low rank characteristic of the measurement matrix and the sparsity of the attack matrix to reformulate the FDIA detection as a matrix separation problem. Then, four algorithms that solve this problem are presented and compared, including the traditional augmented Lagrange multipliers (ALMs), double-noise-dual-problem (DNDP) ALM, the low rank matrix factorization, and the proposed new “Go Decomposition (GoDec).” Numerical simulation results show that our GoDec algorithm outperforms the other three alternatives and demonstrates a much higher computational efficiency. Furthermore, GoDec is shown to be able to handle measurement noise and applicable for large-scale attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

pnnl/IMIC

The first step toward the design of good error detection/correction techniques involves an understanding of an application’s vulnerability to soft errors. To study the behavior of iterative methods in the presence of soft errors, we inject errors during the execution of these methods. In particular, we study the impact of one error (single- or multi-bit) on the execution of iterative methods. We use real life datasets from the SuiteSparse Matrix Collection (https://sparse.tamu.edu) and widely used iterative solver library (Iterative Methods Library, IML++ v1.2a). We instrument the iterative solver implementations so that our error injection methodology can control the iteration, vector, position, number of bits and position of the bits of the error injection. We employed 6 solvers and 28 datasets, performed a total of 1,744,800 error injection runs and collected more than 2.5TB data.

Mutlu, Burcu↗
DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Investigation of the Tribological Properties of AlxSi-1.2Fe(Mn) (x = 5-13 wt.%) Alloys

The effect of Fe-impurity and Mn modification on the sliding wear behavior of Al-xSi-1.2Fe(Mn) (x = 5-13 wt.%) alloys was investigated in dry sliding against AISI 52100 steel. According to the results, in the Si range of 5-9 wt.%, Fe-impurity promotes the formation of finely distributed β-Al{sub 5}FeSi intermetallics in the matrix increasing its hardness and potential to support the tribolayer leading to the improved wear characteristics. The presence of Fe in the composition of Al-(9-13)Si alloys, however, leads to the formation of primary β-platelets that their sizes and volume fractions increase with Si concentration. The formation of primary compounds increases the hardness, but makes the alloys less ductile and more vulnerable to microcracking and wear. Manganese modification converts the β-Fe platelets to the α-Al{sub 15}(Fe,Mn){sub 3}Si{sub 2} compounds with different morphologies. The substitution of fine β-platelets by α-Chinese scripts in Al-(5-9)Si-1.2FeMn alloys reduces the wear resistance, but the formation of primary α-compounds with star-like or polyhedral morphologies in Al-(9-13)Si-1.2FeMn alloys improves their wear characteristics. Moreover, it was found that the formation of Fe(Mn)-rich compounds affects the friction coefficient which is likely due to the fracture of particles in the subsurface region, their interaction with contacting surfaces, and their effect on the stability of the tribolayer.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Evaluation and Qualification of U.S.-Based Research Reactors for Irradiation Capabilities Supporting Advanced Nuclear Systems

Irradiation experiments are a prerequisite for evaluating nuclear reactor system designs, analyzing the performance of these systems, and obtaining licenses. Likewise, irradiation facilities are necessary for producing the radioisotopes used in industrial and medical applications. Recent developments in modeling and simulation capabilities and advancements in computational resources have further enabled the design of irradiation experiments for evaluating radiation-induced phenomena and determining nuclear fuel, material, and system design and safety criteria pertaining to both normal and accident scenarios. These computational tools and models require comprehensive experimental datasets acquired under prototypic radiation conditions—for exploring material and system performance under the uniquely harsh environments found in nuclear reactors—to enable verification and validation for qualification and licensing purposes. However, qualification of irradiation experimental facilities, primarily research and test reactors (RTRs), necessitates that their performance be evaluated based on the irradiation environment (e.g. flux, power, testing capabilities) using an appropriate scoring matrix. Although many university campus RTRs are available for research and development (R&D) activities and initiatives, this study focuses on evaluating and qualifying the irradiation facilities (mostly RTRs) within the United States that are suitable for advanced nuclear fuel, material, and system irradiation experiments aimed at establishing operational-performance limits and informing component and fuel designs so as to improve operational efficiencies and mitigate proliferation vulnerabilities, as well as for radioisotope production aimed at multipurpose applications. As a result, the findings of the present study support the acceleration of nuclear fuel and material qualifications, thus hastening new and advanced nuclear energy system demonstrations and radioisotope production efforts by using extended R&D.

irradiation experiment↗
DOE OSTI2021

Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks

In this project we developed and validated algorithms for privacy-preserving linear regression using a new variant of Secure Multiparty Computation (MPC) we call "Hybrid MPC" (hMPC). Our variant is intended to support low-power, unreliable networks of sensors with low-communication, fault-tolerant algorithms. In hMPC we do not share training data, even via secret sharing. Thus, agents are responsible for protecting their own local data. Only the machine learning (ML) model is protected with information-theoretic security guarantees against honest-but-curious agents. There are three primary advantages to this approach: (1) after setup, hMPC supports a communication-efficient matrix multiplication primitive, (2) organizations prevented by policy or technology from sharing any of their data can participate as agents in hMPC, and (3) large numbers of low-power agents can participate in hMPC. We have also created an open-source software library named "Cicada" to support hMPC applications with fault-tolerance. The fault-tolerance is important in our applications because the agents are vulnerable to failure or capture. We have demonstrated this capability at Sandia's Autonomy New Mexico laboratory through a simple machine-learning exercise with Raspberry Pi devices capturing and classifying images while flying on four drones.

42 ENGINEERING↗
DOE OSTI2023

Connecting the Dots: An Assessment of Cyber-risks in Networked Building and Municipal Infrastructure Systems

The buildings and city streets we walk down are changing. Driven by various data-driven use cases, there is increased interest in networking and integrating lighting and other building systems (e.g., heating, ventilation, and air conditioning (HVAC), security, scheduling) that were previously not internet-facing, and equipping them with sensors that collect information about their environment and the people that inhabit it. These data-enabled systems can potentially deliver improved occupant and resident experiences and help meet the U.S. Department of Energy (DOE) national energy and carbon reduction goals. Deploying connected devices new to being networked, however, is not without its challenges. This paper explores tools available to system designers and integrators that facilitate a cybersecurity landscape assessment – or more specifically the identification of threats, vulnerabilities, and adversarial behaviors that could be used against these networked systems. These assessments can help stakeholders shift security prioritization proactively toward the beginning of the development process.

cyber-risk assesment, adversarial behavior, MITRE ↗
DOE OSTI2020

Thermal shock resistance of multilayer silicon carbide receiver tubes for 800°C molten salt concentrating solar power application

CSP power tower receiver systems during rapid transient weather periods can be vulnerable to thermal shock conditions from rain that which can facilitate the onset of leaks and failures that can have catastrophic consequences. Silicon carbide (SiC) materials have attractive receiver application characteristics for being light weight, having high-strength and excellent thermal shock resistance performance which make them a particularly good fit for receiver absorber materials in CSP. In this investigation, the performance characteristics of Ceramic Tubular Products (CTP) SiC ceramic matrix composite (CMC), multilayered tubes were explored with respect to thermal shock performance for solar receiver applications in next generation CSP plants. Here, thermal shock testing was performed at the Sandia National Laboratories (SNL) Solar Furnace facility using a dynamic stage and thermal shock tube test setup. The tubes tested under incident solar heat flux of 100 W/cm2 were heated with inner tube temperatures reaching approximately 800 °C, with outer temperatures exceeding or just reaching 1000 El for the multilayer and monolithic SiC tubes respectively. The tubes were then quenched with simulated rain. The tubes were then cooled and subjected to hoop stress analysis using an Instron device to assess their subsequent mechanical strength. Furthermore, the on-sun study experimental results indicate an average of 24.2% and 97% higher hoop strength for the CMC tubes than those composed of monolithic SiC and aluminum oxide (A1203) respectively.

14 SOLAR ENERGY↗
DOE OSTI2021

Analyzing the Effects of Cyberattacks on Distribution System State Estimation

Key components of power systems—such as energy management systems, automatic generation control, and state estimation—are under serious vulnerability from cyberattacks. Cyber threats in electric grids have increased significantly because of the increased interconnectivity of supervisory control and data acquisition systems and public network infrastructure. As the penetration level of distributed energy resources increases, it is imperative to employ system-monitoring techniques such as state estimation for the reliable operation of distribution systems. Recently, multiple methods have been developed that exploit the low rank property of distribution system state matrix and are robust to bad data, such as matrix completion. This paper analyzes the impact of various realistic cyberattack scenarios on matrix completion. Realistic cyberattack scenarios are converted into data corruption models that are used in an extensive simulation of a custom IEEE 123-bus system.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Analyzing the Effects of Cyberattacks on Distribution System State Estimation: Preprint

Key components of power systems—such as energy management systems, automatic generation control, and state estimation—are under serious vulnerability from cyber attacks. Cyber threats in electric grids have increased significantly because of the increased interconnectivity of supervisory control and data acquisition systems and public network infrastructure. As the penetration level of distributed energy resources increases, it is imperative to employ system-monitoring techniques such as state estimation for the reliable operation of distribution systems. Recently, multiple methods have been developed that exploit the low rank property of distribution system state matrix and are robust to bad data, such as matrix completion. This paper analyzes the impact of various realistic cyber attack scenarios on matrix completion. Realistic cyber attack scenarios are converted into data corruption models that are used in an extensive simulation of a custom IEEE 123-bus system.

41 EE - Solar Energy Technologies Office (EE-4S)↗