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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

TOI-2076 and TOI-1807: Two Young, Comoving Planetary Systems within 50 pc Identified by TESS that are Ideal Candidates for Further Follow Up

We report the discovery of two planetary systems around comoving stars: TOI-2076 (TIC 27491137) and TOI-1807 (TIC 180695581). TOI-2076 is a nearby (41.9 pc) multiplanetary system orbiting a young (204 ± 50 Myr), bright (K = 7.115 in TIC v8.1) start. TOI-1807 hosts a single transiting planet and is similarly nearby (42.58 pc), similarly young (180 ± 40 Myr ), and bright. Both targets exhibit significant, periodic variability due to starspots, characteristic of their young ages. Using photometric data collected by TESS we identify three transiting planets around TOI-2076 with radii of R {sub b} = 3.3 ± 0.04 R {sub ⊕}, R {sub c} = 4.4 ± 0.05 R {sub ⊕}, and R {sub d} = 4.1 ± 0.07 R {sub ⊕}. Planet TOI-2076b has a period of P {sub b} = 10.356 days. For both TOI-2076c and d, TESS observed only two transits, separated by a 2 yr interval in which no data were collected, preventing a unique period determination. A range of long periods (<17 days) are consistent with the data. We identify a short-period planet around TOI-1807 with a radius of R {sub b} = 1.8 ± 0.04 R {sub ⊕} and a period of P {sub b} = 0.549 days. Their close proximity, and bright, cool host stars, and young ages make these planets excellent candidates for follow up. TOI-1807b is one of the best-known small (R < 2 R{sub ⊕}) planets for characterization via eclipse spectroscopy and phase curves with JWST. TOI-1807b is the youngest ultra-short-period planet discovered to date, providing valuable constraints on formation timescales of short-period planets. Given the rarity of young planets, particularly in multiple-planet systems, these planets present an unprecedented opportunity to study and compare exoplanet formation, and young planet atmospheres, at a crucial transition age for formation theory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A Large Sample of Extremely Metal-poor Galaxies at z < 1 Identified from the DESI Early Data

Extremely metal-poor galaxies (XMPGs) at relatively low redshift are excellent laboratories for studying galaxy formation and evolution in the early universe. Much effort has been spent on identifying them from large-scale spectroscopic surveys or spectroscopic follow-up observations. Previous work has identified a few hundred XMPGs. In this work, we obtain a large sample of 223 XMPGs at z < 1 from the early data of the Dark Energy Spectroscopic Instrument (DESI). The oxygen abundance is determined using the direct T e method based on the detection of the [O iii]λ4363 line. The sample includes 95 confirmed XMPGs based on the oxygen abundance uncertainty; the remaining 128 galaxies are regarded as XMPG candidates. These XMPGs are only 0.01% of the total DESI observed galaxies. Their coordinates and other properties are provided in the paper. The most XMPGs have an oxygen abundance of ~1/34 Z ⊙ , a stellar mass of about 1.5 × 10 7 M ⊙ , and a star formation rate of 0.22 M ⊙ yr –1 . The two most XMPGs present distinct morphologies suggesting different formation mechanisms. The local environmental investigation shows that XMPGs preferentially reside in relatively low-density regions. Many of them fall below the stellar mass–metallicity relations (MZRs) of normal star-forming galaxies. From a comparison of the MZR with theoretical simulations, it appears that XMPGs are good analogs to high-redshift star-forming galaxies. The nature of these XMPG populations will be further investigated in detail with larger and more complete samples from the ongoing DESI survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Cybersecurity Resiliency of Marine Renewable Energy Systems-Part 1: Identifying Cybersecurity Vulnerabilities and Determining Risk

Technology innovation, market demand, and the potential impacts of a changing climate are driving the marine renewable energy (MRE) industry to develop market-ready systems to provide low-carbon electricity for emerging, off-grid markets. The advanced operational and information technology devices used in MRE systems create a pathway for a cyber threat actor to gain unauthorized access to data or disrupt operation. To improve the resiliency of MRE systems as a predictable, affordable, and reliable source of energy from oceans and rivers, guidance was developed for an end users' organization that describes a framework for identifying and managing cybersecurity risk. The development of the cybersecurity guidance is based on standards described in the Risk Management Framework and Cybersecurity Framework developed by the National Institute of Standards and Technology (NIST). This paper is the first of a two-part series that describes an approach to determine the cybersecurity risk for MRE systems based on assessing potential cyber threats, identifying vulnerabilities (people, processes, and technology, including physical and operational environment), and evaluating the consequences a cyberattack would have on operation of the MRE system and impact on end users' mission and business objectives. MRE developers and stakeholders can use this approach to assess their current cybersecurity risk posture to incorporate appropriate cybersecurity controls to reduce the consequences and impacts from a cyberattack on MRE systems. This approach can be refined further as MRE systems are deployed and operational configurations are available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Decision Tree Regression to Identify Representative Road Sections for Evaluating Performance of Connected and Automated Class 8 Tractors

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified. High-level metrics and detailed information of the derived road sections are also illustrated and discussed, which demonstrate their representativeness and the effectiveness of the approach. Meanwhile, the capability of this approach can be easily extended by integrating specific constraints into the DTR algorithm. As an example, a specific representative road section with an aggressive road grade profile was also provided via this approach.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2020

System and method to characterize and identify operating modes of electric loads

A system characterizes and identifies one of a plurality of different operating modes of a number of electric loads. The system includes a processor; a voltage sensor providing a voltage signal for one of the electric loads to the processor; a current sensor providing a current signal for the one electric load to the processor; and a routine executed by the processor and structured to characterize the different operating modes using steady state and voltage-current trajectory features determined from the voltage and current signals, and to identify a particular one of the different operating modes based on a plurality of operating mode membership functions of the steady state and voltage-current trajectory features.

Yang, Yi↗
DOE OSTI2020

Machine learning technique to identify grains in polycrystalline materials samples

A method of identifying grains in polycrystalline materials, the method including (a) identifying local crystal structure of the polycrystalline material based on neighbor coordination or pattern recognition machine learning, the local crystal structure including grains and grain boundaries, (b) pre-processing the grains and the grain boundaries using image processing techniques, (c) conducting grain identification using unsupervised machine learning; and (d) refining a resolution of the grain boundaries.

Sankaranarayanan, Subramanian↗
DOE OSTI2021

ML Clustering to Identify Natural Gas Pipeline Infrastructure Vulnerabilities

The network of more than 2.5 million miles of natural gas pipelines the U.S. are exposed to a range of vulnerabilities, ranging from extreme weather, to human behavior, and increasingly, to cyber threats. Advanced, data-driven analytics, including the use of machine learning and artificial intelligence, afford an opportunity to identify potential vulnerabilities, better understand risks, and help mitigate vulnerabilities in the network. At NETL, machine learning is being used to explore pipeline vulnerabilities and identify significant clusters of failure events, including failures due to weather, human behavior, and materials. These findings are being used to support the development of new technologies, including sensors, but can also be used to evaluate and inform mitigation strategies to reduce risks and vulnerabilities throughout the network.

Bauer, Jennifer↗
DOE OSTI2021

System, algorithm, and method using short pulse interrogation with neutrons to detect and identify matter

A system, method, and algorithm for detecting and identifying special nuclear material (SNM) (including fissionable material), explosives, or drugs is disclosed. This material detection system relies on active interrogation of material using a short, intense neutron pulse, and characterization of the resulting prompt gamma response in two short time windows, the first concurrent with and the second immediately following the end of the neutron pulse, optionally subject to a total transit delay time of the neutrons from the neutron source to the target and from the target to the gamma detector. A high data rate analysis system implements data stream analysis and statistical correlations of the two short time window gamma responses to rapidly detect or identify SNM, explosives, or drugs. The duration of the neutron pulse is so short that minimal dose results. Various shields help to minimize the background signal falling on the gamma detector to improve sensitivity.

Derzon, Mark S.↗
DOE OSTI2021

Spatial identification of assets using n-dimensional asset identifiers

Described herein are methods, systems, and storage media having computer-processable instructions for assigning an n-dimensional asset identifier to an asset (n ≥ 3). The identifier can be generated by determining a position of the asset in n dimensions, wherein the position is based at least on the center of mass of the asset's geometry. Extents are determined for each of the n dimensions, wherein each extent is measured from the center of mass to a surface of an n-dimensional bounding polytope containing the asset. The position and the extents of the asset can then be encoded or embedded into a single, non-opaque, code string. The position and the extent information are intrinsic to the string.

Borkum, Mark I.↗
DOE OSTI2021

A Novel Fiber-Bragg-Grating-Based Unique Identifier for Use in Tags and Seals

Tags and seals are important tools used in safeguards regimes for the control of nuclear materials. Vital to a successful tag or seal is the ability to securely, uniquely, and positively distinguish it from others of the same kind. This allows one to validate that items entered into an inspection regime to which the tag or seal is affixed are as claimed. We are developing a new type of unique identifier (UID) for such applications that is based on fiber-Bragg grating (FBG) technology. The index of refraction of the core glass of a fiber optic can be locally and permanently altered if sensitized fibers are exposed to intense ultraviolet light. This can be used to create index modulations with a regular periodicity comparable to the wavelengths of light with which the fiber is used. If broadband illumination is sent down the fiber, any light that is in resonance with the modulations will be reflected back to the input end of the fiber where it can be detected. The shape of the returned spectrum can be set by the modulation pattern written into the fiber’s core. We take advantage of these properties to create a compact UID that contains several FBGs potted into a monolithic body. By connecting the UID to an optical interrogator, the complex spectral return can be uniquely identified with a short measurement, providing a robust identification. As a device based on narrowband spectral returns, FBGs are inherently radiation tolerant, making them ideal for use with nuclear materials. Further, they can be read out remotely via a long fiber optic or locally via brief connection to a portable optical interrogator. Because the spectral signature of FBGs is sensitive to external mechanical perturbations, we are exploring the ability to reversibly alter the signature obtained from the UID by applying controlled mechanical forces with a simple device. Such a “key” will allow different stakeholders to obtain their own unique signature from the device, providing a new level of surety to safeguards inspections.

Ziock, Klaus-Peter↗
DOE OSTI2023

System and method for identifying objects of interest in images based on likelihood map decluttering

An automatic threat recognition system and method is disclosed for scanning the x-ray CT image of an article to identify the objects of interest (OOIs) contained within the article, which are otherwise not always quickly apparent or discernable to an individual. The system uses a computer to receive information from two-dimensional (2D) image slices from a reconstructed computed tomography (CT) scan image and to produce a plurality of voxels for each slice of the 2D image. The computer analyzes the voxels to create a likelihood map (LM) representing likelihoods that voxels making up the CT image are associated with a material of interest (MOI). The computer further analyzes the LM to construct neighborhoods of voxels within the LM, and classifies each voxel neighborhood based on its features, thereby decluttering the LM to facilitate the process of connecting voxels of a like MOI together to form segments. The computer classifies each candidate segment based on its features, thereby identifying those segments that correspond to objects of interest.

Paglieroni, David W.↗
DOE OSTI2023

Investigation of the Use of Dynamic Probabilistic Risk Assessment Methodologies for Identifying Digital I&C System Common Cause Failures

Digital Instrumentation and Control (I&C) systems have a key role in nuclear power plants in the upgrade of aging analog systems. Digital systems improve plant safety and reliability through features such as increased hardware reliability and stability and improved failure detection capability. There is no consensus on which of the current probabilistic risk assessment methods are most suitable for use in the reliability analysis of digital I&C systems. While the traditional event-tree/fault-tree (ET/FT) approach is still used for their reliability modeling, there are concerns regarding this approach in properly accounting for dynamic interactions among system components since potentially significant dependencies among failure events may not be identified and/or their likelihood may not be properly quantified. Dynamic methodologies are expected to provide a much more accurate representation of probabilistic evolution of the I&C systems in time due to their capability to more properly account for complex interactions than the static approach. The applicability of dynamic PRA methodologies for digital I&C system is investigated using the criteria presented in the NUREG/CR-6901, and the comparisons made in NUREG/CR-6901 are updated in light of the latest studies. The Dynamic Event Tree (DET) approach has been identified as one of the top dynamic methods when evaluated against the requirements for the reliability modeling of digital I&C systems. The DET method is a strong candidate for integration into existing PRA studies, as it bears many similarities to the traditional ET approach. In this study, the DET approach has been applied to the Plant Protection System of the APR1400 design, and the results are compared to results from its available traditional ET/FT analysis. Possible approaches to evaluate and quantify the effects of common cause failures on system safety using dynamic methods are also examined.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Trustworthiness and Trust: Identifying Factors that Drive Successful Human-AI Interaction in Nuclear Power Plant Applications

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are rapidly evolving and considered a promising tool for efficient and continued safe operations of the U.S. nuclear power plants (NPPs). Emerging AI techniques like large language models (LLMs) are one such technology that may support personnel at existing NPPs perform work more efficiently. For example, operators may query the current operational status of a power plant via a chat interface leveraging LLMs to access plant-related information in an interactive manner rather than manually collecting various sensor data for tasks such as surveillances or completing work orders. This is a fundamental shift in the way operators currently perform their tasks today. The literature of human-automation interaction indicates that trust is a crucial factor that drives successful interaction between a human operator and an automated system, like an AI-infused NPP application. This work presents the results of a literature review on key factors that relate to trust in AI/LLM technologies for NPP applications. The relevant literature of human factors and cognitive engineering has identified various factors related to trust including trustworthiness, performance characteristics, operator skill and perceived risk. This preliminary literature review will guide development and evaluation of models involving the identified factors influencing trust in AI and develop a framework for human-centered design for interface between humans and AI. By addressing trust, this work supports developing a technical basis for designing key characteristics of AI/LLM to support calibrated trust, which will ultimately support wide-scale adoption of AI/LLM technologies, as well as ensure safe, effective, and reliable use.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Alternative Analysis and Prioritization of Department of Energy “TBD” Materials with No Identified Disposition Pathway

The Department of Energy (DOE) complex manages a significant inventory of excess nuclear materials for which disposition pathways have not been identified, commonly referred to as "To Be Determined" (TBD) items. The Disposition Pathways Program, initiated in FY2018, provides a standardized framework for identifying viable disposition pathways for these materials. In 2023, the program undertook a comprehensive review and systematic analysis of the remaining TBD material groups, updating the assessment from the 2020 TBD Study using the 2022 fiscal year-end Nuclear Material Inventory Assessment (NMIA) as the primary data source. This effort aimed to utilize quantitative analysis to down-select from a wide range of potential disposition options and prioritize the remaining pathways to facilitate informed, risk-based decision-making for future programmatic funding and execution. This paper details the alternative analysis methodology used for screening and prioritization, highlighting the key criteria, ranking process, and resultant recommendations. The study successfully narrowed fifty-two potential disposition options down to nineteen, providing a focused path forward for addressing a longstanding challenge within the DOE complex.

Ramsey, Catherine [Savannah River National Laborat↗
DOE OSTI2024

Identifying recharge sources and their impacts on a North Central New Mexico shallow aquifer using unsupervised machine learning

In this article, shallow aquifers are important but highly variable resources in arid to semi-arid regions. Limited shallow aquifer volume results in high sensitivity to recharge fluctuations, which can impact the local fauna and flora, and transport of contaminants in the aquifer or vadose zone. Aquifer response to external forcing (e.g., precipitation) is usually solved by estimating aquifer parameters and running physics-based models to match known fluctuations of hydraulic head. However, this technique is time and computationally expensive. Furthermore, high aquifer complexity decreases precision in physics-based models. Alternatively supervised machine learning is used to predict aquifer dynamics. However, these techniques rely on input data and struggle to interpret aquifer response for missing sources (i.e., snowpack data). To counter these problems, we propose an unsupervised machine learning technique (NMFk) to estimate the impact of different sources on aquifer recharge. NMFk is used to understand the influence of external forcing on shallow aquifer recharge in the Pajarito Plateau (Los Alamos, NM, USA). The results show how NMFk can be used to reduce the data dimension in a complex field dataset to three recharge signals that cause fluctuations within the field data. Here, the source signals are interpreted as rainfall, snowmelt, and a delayed aquifer response to the previous two signals. These results evidence how heterogeneous aquifers delimited by canyons incised into the Pajarito Plateau respond in similar ways to the source signals identified by NMFk. Furthermore, results show the importance of the local geology where faults act as sinks, and anthropogenic disturbances can facilitate infiltration amplifying the interpreted signal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Identifying Photoinduced Dipolar Polarization and Orbit–Orbit Interaction between Excitons in Organic–Inorganic Hybrid Perovskites

Abstract Photoinduced polarization and orbit–orbit interaction are important issues in hybrid perovskites toward developing optoelectronic functionalities. This paper identifies that photoinduced polarization occurs in hybrid perovskites with mixed‐cation methylammonium (MA)/formamidinium (FA) (MA x FA (1− x ) PbI 3 ) by measuring bulk polarization at 1 MHz in a magnetic field. Interestingly, when the internal dipole moment is increased upon increasing the MA:FA ratio, the photoinduced dipolar polarization can be substantially enhanced, clarifying the controversial issue of whether photoexcitation can induce a dielectric polarization within dipolar polarization regime in hybrid perovskites. Furthermore, upon increasing photoinduced dipolar polarization, it is found that the intrinsic orbit–orbit interaction between excitons can be increased, revealed by monitoring photocurrent change (Δ J sc ) upon switching the photoexcitation between linear and circular polarizations. This presents that organic cations are directly involved in the orbit–orbit interaction within band structures. Clearly, the studies provide an insightful understanding of the dipole moment effects on photoinduced dipolar polarization and orbit–orbit interaction between excitons in hybrid perovskites toward controlling the optoelectronic properties.

Dou, Yixuan↗
DOE OSTI2020

Establishing a Theoretical Landscape for Identifying Basal Plane Active 2D Metal Borides (MBenes) toward Nitrogen Electroreduction

Abstract To achieve efficient ammonia synthesis via electrochemical nitrogen reduction reaction (NRR), a qualified catalyst should have both high specific activity and large active surface area. However, integrating these two merits into one single material remains a big challenge due to the difficulty in balancing multiple reaction intermediates. Here, it is demonstrated that the boron‐analogues of MXenes, namely “MBenes”, could cope with the challenge and achieve the high activity and large reaction region simultaneously toward NRR. Using extensive density functional theory computations and taking 16 MBenes as representatives, it is identified that seven MBenes (CrB, MoB, WB, Mo 2 B, V 3 B 4 , CrMnB 2 , and CrFeB 2 ) not only have intrinsic basal plane activity for NRR with limiting potentials ranging from −0.22 to −0.82 V, but also possess superior capability of suppressing the competitive hydrogen evolution reaction. Particularly, different from the MXenes whose surface oxidation may block the active sites, once oxidized, these MBenes can catalyze NRR via the self‐activating process, reducing O*/OH* into H 2 O* under reaction conditions, and favoring the N 2 electroreduction. As a result, the exceptional activity and selectivity, high active area (≈10 19 m −2 ), and antioxidation nature render these MBenes as pH‐universal catalysts for NH 3 production without introducing any dopants and defects.

Guo, Xiangyu↗
DOE OSTI2022

Identifying the Signatures of Intermolecular Interactions in Blends of PM6 with Y6 and N4 Using Absorption Spectroscopy

Abstract In organic solar cells, the resulting device efficiency depends strongly on the local morphology and intermolecular interactions of the blend film. Optical spectroscopy was used to identify the spectral signatures of interacting chromophores in blend films of the donor polymer PM6 with two state‐of‐the‐art nonfullerene acceptors, Y6 and N4, which differ merely in the branching point of the side chain. From temperature‐dependent absorption and luminescence spectroscopy in solution, it is inferred that both acceptor materials form two types of aggregates that differ in their interaction energy. Y6 forms an aggregate with a predominant J‐type character in solution, while for N4 molecules the interaction is predominantly in a H‐like manner in solution and freshly spin‐cast film, yet the molecules reorient with respect to each other with time or thermal annealing to adopt a more J‐type interaction. The different aggregation behavior of the acceptor materials is also reflected in the blend films and accounts for the different solar cell efficiencies reported with the two blends.

Kroh, Daniel↗