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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Learning Management System User Requirements for the National Nuclear Security Administration's International Nuclear Safeguards Engagement Program

The National Nuclear Security Administration's (NNSA) International Nuclear Safeguards Engagement Program (INSEP) is considering investing in new tools that would allow the program to support its partner states from a distance. At the same time, the program is considering approaches that would allow several organizations, including NNSA, IAEA, national laboratories and contractor staff, to collaborate in the development and maintenance of instructional content. Software systems known as Learning Management Systems (LMSs) might represent a mechanism through which INSEP could accomplish these goals (collaborative development and remote support). To assess the usefulness of an LMS, INSEP has specified its needs for delivering online training and compared those needs to the capability of a range of LMSs. This comparison will allow INSEP to determine whether an LMS would be a useful tool and may set the stage for a "make-buy" decision in the future. The study team concluded that INSEP's content development and delivery needs align well with the capabilities of the leading LMSs on the market today and that that INSEP performance requirements allow for a customized approach using existing training portals that are already available to NNSA. Additional work would be required to specify the desired processes for developing online training and outreach materials, structuring the databases, specifying the data that should be collected, and detailing the desired system reports and documentation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Dynamics of genetic code evolution: The emergence of universality

We study the dynamics of genetic code evolution. The model of Vetsigian et al. [Proc. Natl. Acad. Sci. USA 103, 10696 (2006)] and Vetsigian [Collective evolution of biological and physical systems, Ph.D. thesis, 2005] uses the mechanism of horizontal gene transfer to demonstrate convergence of the genetic code to a near universal solution. We reproduce and analyze the algorithm as a dynamical system. All the parameters used in the model are varied to assess their impact on convergence and optimality score. We show that by allowing specific parameters to vary with time, the solution exhibits attractor dynamics. Finally, we study automorphisms of the genetic code arising due to this model. We use this to examine the scaling of the solutions to re-examine universality and find that there is a direct link to mutation rate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Optimum power analysis of a self-reactive wave energy point absorber with mechanically-driven power take-offs

This paper studies a self-reactive ocean wave energy converter (WEC) where energy is absorbed through the relative motion between a floating buoy on the ocean surface and a submerged body. Two types of direct-drive power take off (PTO) systems are examined. One adopts a ball screw system in inverse driving mode with mechanical motion rectifier (MMR) that converts the oscillating bi-directional input heave motion into unidirectional rotation to drive an electromagnetic generator, and another one uses a ball screw system to directly drive the generator (called non-MMR). Dynamic models for both types of PTOs are established and integrated with the overall WEC system model in both the time and frequency domains. Numerical simulation is used to investigate the dynamic performance of both PTOs. The influences of the PTO inerter, which is from the rotational inertia of the flywheel and generator, and the WEC drag damping coefficient are discussed and explored. The analytical closed-form solution for the optimum condition of the non-MMR system is derived based on the criterion of maximum power absorption, and numerical simulation yields the optimum condition for the MMR system. Overall, the results under regular and irregular waves show that the PTO inerter in the MMR system can improve the power absorption for small wave periods and maintain the same performance for large wave periods. The PTO inerter in the non-MMR system shifts the peak frequency. The WEC drag damping negatively influences the power absorption in both PTO systems, especially at large wave periods.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Time-series elemental imaging reveals CAX-dependent redistribution patterns for anoxia recovery

Flooding-induced oxygen deprivation (anoxia) is a challenge to plant survival, necessitating adaptive mechanisms for recovery. This study investigated elemental redistribution during anoxia recovery using time-series elemental imaging to show changes in nutrient distribution. Focusing on the role of Cation/H + Exchangers (CAXs) in Arabidopsis thaliana, we show how mutants deficient in specific CAX transporters (cax1 and the cax1-4 quadruple mutant) respond to anoxia and metal stress. Mutants showed reduced lipid peroxidation and increased expression of flood-tolerance proteins during recovery. X-ray fluorescence microscopy and laser ablation–inductively coupled plasma mass spectrometry were used to show elemental redistribution over time. In wild-type plants (Col-0), post-anoxia elemental distribution resembled the elemental distribution of CAX mutants under normoxic conditions, suggesting that CAX-mediated elemental distribution before anoxia enables faster recovery post-anoxia, rather than affecting remobilization post-anoxia. Although CAX mutants had altered tolerance to excess manganese and copper, leaf metal distribution during metal stress was not altered. Here, these findings introduce the potential utility of time-series elemental imaging to show stress-response phenotypes and the importance of elemental distribution to recovery after anoxia. The novelty of this work lies in resolving spatial distribution patterns in a non-static system to gain insight into mechanisms of stress resilience in plants.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Superconductinglike response in a driven gapped bosonic system

In this study, we propose a mechanism for the photoinduced superconductinglike response recently reported in cuprates and other strongly correlated materials. This mechanism relies on quantum-fluctuating bosons consisting of electron pairs. With periodic drive, the electron pairs and vacancies of pairs form a coherent nonequilibrium condensate, different from conventional superconductors, yet showing a superconductinglike response in some regime even with dissipation. Unlike the case of driven fermionic bands, which results in the familiar Floquet bands with hybridization gaps, for driven bosons the “gap” opens up in the momentum direction, resulting in a resonant region in momentum space where the eigenvalues are complex. We give a simple physical argument for why this picture leads to a “perfect conductor” which exhibits superconductinglike frequency-dependent conductivity but no Meissner response. While our model is quite general, in the case of cuprates, a quantum-fluctuating pair density wave in the pseudogap region may serve as the origin of the quantum-fluctuating electron pairs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Constitutive and inducible oleoresin defenses share genetic architectures and mechanisms in Pinus taeda

The oleoresin defense system of loblolly pine (Pinus taeda) protects trees from insects and pathogens and is an important source of renewable biofuels and chemicals, but the genetic basis of oleoresin production is poorly understood. We characterized the genetic architecture of oleoresin flow, resin canal number, stem wood terpene content, and monoterpene composition in two clonal populations of P. taeda. We used quantitative genetic analyses, genome-wide association studies (GWASs), multiplex network learning, and gene expression profiling to elucidate shared gene networks underlying defense traits and to identify high-quality candidates for breeding and engineering loblolly pine. Genetic analyses revealed polygenic inheritance and trait-to-trait correlations provide strong evidence for shared genes regulating constitutive and induced oleoresin flow. We identified 236 single nucleotide polymorphisms associated with oleoresin flow, resin canal number, and terpene composition and highlight candidate genes likely involved in terpene biosynthesis, cambial meristem reprogramming, and pathogen perception and immune signaling. Fourteen GWAS candidates were methyl jasmonate-responsive in tissues where resin canals initiate and terpene production occurs. Integrating quantitative genetics, GWAS, gene expression, and multiplex network analyses enabled the prioritization of high-quality candidate genes. This work advances the development of more resilient loblolly pine optimized for ecological performance, renewable chemical, and biofuel production.

genome-wide association study↗
DOE OSTI2021

Rotational mechanical gas separator

The invention provides a system for removing target moieties from gas streams, the system comprising a supersonic expander coaxially positioned within an array of oblique shock inducers. Also provided is a method for removing target moieties from gas streams, the method comprising simultaneously subjecting the streams to supersonic expansion and oblique shock compression.

VanOsdol, John G.↗
DOE OSTI2020

Investigation of the electrical conduction mechanisms in P-type amorphous germanium electrical contacts for germanium detectors in searching for rare-event physics

For the first time, electrical conduction mechanisms in the disordered material system is experimentally studied for p-type amorphous germanium (a-Ge) used for high-purity Ge detector contacts. The localization length and the hopping parameters in a-Ge are determined using the surface leakage current measured from three high-purity planar Ge detectors. The temperature dependent hopping distance and hopping energy are obtained for a-Ge fabricated as the electrical contact materials for high-purity Ge planar detectors. As a result, we find that the hopping energy in a-Ge increases as temperature increases while the hopping distance in a-Ge decreases as temperature increases. The localization length of a-Ge is on the order of 2.13$^{-0.05}_{+0.07}$A° to 5.07$^{0.83}_{+2.58}$A°,depending on the density of states near the Fermi energy level within bandgap. Using these parameters, we predict that the surface leakage current from a Ge detector with a-Ge contacts can be much smaller than one yocto amp (yA) at helium temperature, suitable for rare-event physics searches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Simulation of Mechanical Fractionation of Chopped Whole-Plant Corn (WPC) Using Discrete Element Method (DEM)

Fractionating whole-plant corn (WPC) in a single-pass harvesting system requires studies on the WPC-to-equipment interaction for improved property control, as well as mechanical and air-driven separation processes compared to the traditional multi-pass grain and stover harvesting system. The discrete element method (DEM) technique has the potential to simulate WPC mechanical fractionation and support simulation-based design of WPC separation processes. In this study, methods to develop DEM particle models of WPC (kernel, cob, stalk, and husk) and their material properties for simulating mass fractionation using the ASABE standard mechanical shaker were proposed. Measurement was done on the axial dimensions (major, intermediate, and minor) and mass of each WPC type (mean sample size is 56), sampled from single-pass harvesting. Applying gaussian multivariate regression and bootstrapping re-sampling techniques, a DEM particle approximate to each WPC was developed. Sensitivity analysis of the DEM Young‘s modulus, Poisson‘s ratio, and interaction parameters of coefficient of restitution, coefficient of rolling friction, and coefficient of static friction on mass fraction was performed after 156 ASABE sieve-shaking DEM simulation runs, generated using Latin Hypercube Design (LHD) design of experiment (DOE) from 19 DEM material parameters. DEM simulation using Hertz-Mindlin with flexible bond contact laws and DOE optimized material properties successfully reproduced the mass fractions retained in ASABE sieves at 9.8% mean relative error and a coefficient of determination of R2 = 0.87. Here, the DEM methodology developed for mechanical WPC mass fractionation could be deployed to perform virtual design of feedstock handling equipment and performance analysis of mechanical fraction systems.

09 BIOMASS FUELS↗
DOE OSTI2024

Multiscale modeling of metal-hydride interphases—quantification of decoupled chemo-mechanical energies

Abstract The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessing individual movement, habitat use, and behavior of non-breeding marine birds in relation to prey availability in the US Atlantic

Resource availability is a key factor driving marine bird movements and distributions, but direct information on prey availability is difficult to obtain at relevant scales. We present novel methods for describing multi-scale trophic associations, combining movement analyses of marine birds with estimates of forage fish surface aggregations from digital aerial survey data and species occupancy from bottom trawl survey data. We analyzed satellite telemetry data from northern gannets Morus bassanus , red-throated loons Gavia stellata , and long-tailed ducks Clangula hyemalis in the US Atlantic during the non-breeding period. Using discrete-time hidden Markov models to distinguish area-restricted (i.e. putative foraging) from transit movements, we examined how environmental factors influence movement, and how forage fish species distributions and surface aggregations influence habitat use by gannets and loons that have greater dietary reliance. Our results suggest that chlorophyll a concentration significantly affected movement behavior across species, highlighting the importance of higher-productivity areas around estuaries during colder months when regional productivity is low. Though variable across species and seasons, spatial cross-correlation analysis revealed that herring species (Family Clupeidae), including Atlantic menhaden Brevoortia tyrannus , may be important resources; it also showed positive spatial correlations with forage fish aggregations. This suggests that prey patch dynamics and factors driving aggregation formation may be as important as species composition. However, spatial patterns were generally low (<0.3), suggesting a mismatch in spatiotemporal resolution, exemplifying the challenges in quantifying trophic relationships in marine systems. Disentangling predator-prey relationships is critical to understanding the mechanisms driving marine bird behavior in rapidly changing marine systems.

Environmental Sciences & Ecology↗
DOE OSTI2026

Post-Event Fault Identification with Machine Learning for Protection System Validation

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by accidental improper relay settings or deliberate malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that their performance falls within expectations. Relays that fail to isolate a fault or trip when there is no system disturbance can be flagged for settings review in situations where this behavior may not have been noticed due to manual restoration or backup protection operations. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by identifying fault events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Protection System Validation Using Post-Event Anomaly Classification with Machine Learning

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Protection System Validation with Machine Learning Anomaly Classification

A poster for the Early Career Poster Session. Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Long-term strength retrogression of silica-enriched oil well cement: A comprehensive multi-approach analysis

The strength retrogression of a Class G cement enriched by adding 60–80% silica cured under the condition of 200 °C and 50 MPa were investigated by multiple different analysis methods. Short-term strength analysis suggests sonic strength testing is a poor indicator of real mechanical strength at such curing condition. Long-term testing up to 142 d shows all designed systems experiences dramatic deterioration in physical and mechanical properties, such as compressive strength, Young's modulus, water permeability and gas permeability. Further testing was conducted using X-ray diffraction, thermal gravimetric analysis, mercury intrusion and scanning electron microscope. The study reveals that the set cement experiences significant microstructure coarsening with increasing curing time, especially after 30 d curing. The strength retrogression and microstructure coarsening seem to be accompanied with the continued consumption of silica, and are likely caused by the gradual conversion of semi-crystalline C-S-H into crystalline tobermorite and xonolite over the long-term curing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Neural correspondence to spectrum of environmental uncertainty in multiple-cue probability judgment system with time delay

Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human agent, resulting in non-optimal decision-making in MAS. Thus, it is necessary to know how human behavior is changed to capture system reliability under uncertainty. Another issue affecting MAS is time delay, which can delay agent information transfer, resulting in low performance and instability. However, it is difficult to find studies on the influence of time delay on human agents. This study is about understanding the human decision-making process under a specific system reliability environment by uncertainty with time delay. We used concepts of expected and unexpected uncertainty to implement reliability of the system usage environment with three types of time delay conditions: no time delay, regular time delay, and irregular time delay conditions. We used electroencephalogram (EEG) for human cognitive neural mechanisms in multiple-cue judgment systems to understand human decision-making. In the reliability of system usage environment, the unreliable system environment significantly creates less memory load by less utilization of system rules for decision-making. In terms of time delay, delayed information delivery does not significantly affect memory load for decision-making.

cognitive process↗
DOE OSTI2024

7 Innovations in high-rate composite manufacturing: integrating additive manufacturing with compression molding process

Advanced composites play a pivotal role in modern engineering, offering exceptional strength-to-weight ratios and tailored properties, essential for various industries. High-rate composite manufacturing techniques have rapid production capabilities, which are essential for meeting the demands of industries requiring cost-saving, efficiency, and quick turnaround times. This chapter explores the Additive Manufacturing- Compression Molding (AM-CM) system developed by Oak Ridge National Laboratory (ORNL) for advanced composites manufacturing. The AM-CM system integrates additive manufacturing with compression molding, facilitating the production of polymer composite parts with superior mechanical properties and meticulously controlled microstructures. This innovative system not only ensures precise material deposition but also operates as a fast composite manufacturing process, enhancing productivity and performance, which are needed attributes across industrial applications. Through comprehensive mechanical testing and microstructural analysis, AM-CM promotes remarkable fiber alignment and reduced porosity in composite parts compared to alternative thermoplastic high-rate composite manufacturing methods. Furthermore, AM-CM enables overmolding reinforcement using continuous carbon fiber and supports selective reinforcement through customizable toolpaths. It also facilitates the production of hybrid materials to achieve tailored mechanical properties. Future advancements in AM-CM technology aim to enhance process efficiency, broaden material versatility, and improve part performance. This involves exploring novel materials, advancing process monitoring, implementing automation technologies, and integrating artificial intelligence (AI) and machine learning (ML) for predictive modeling and real-time optimization in composite manufacturing. These developments will establish the AM-CM system as a transformative technology in composite manufacturing, driving innovation across industries.

Hassen, Ahmed [ORNL] (ORCID:0000000328521222)↗