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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Synthetic magnetoelectric coupling in a nanocomposite multiferroic

Given the paucity of single phase multiferroic materials (with large ferromagnetic moment), composite systems seem an attractive solution to realize magnetoelectric coupling between ferromagnetic and ferroelectric order parameters. Despite having antiferromagnetic order, BiFeO₃ (BFO) has nevertheless been a key material due to excellent ferroelectric properties at room temperature. We studied a superlattice composed of 8 repetitions of 6 unit cells of La₀.₇Sr₀.₃MnO₃ (LSMO) grown on 5 unit cells of BFO. Significant net uncompensated magnetization in BFO, an insulating superlattice, is demonstrated using polarized neutron reflectometry. Remarkably, the magnetization enables magnetic field to change the dielectric properties of the superlattice, which we cite as an example of synthetic magnetoelectric coupling. Importantly, controlled creation of magnetic moment in BFO is a much needed path toward design and implementation of integrated oxide devices for next generation magnetoelectric data storage platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Compositional dependence of linear and nonlinear optical response in crystalline hafnium zirconium oxide thin films

Composition dependence of second harmonic generation, refractive index, extinction coefficient, and optical bandgap in 20 nm thick crystalline Hf 1– x Zr x O 2 (0 ≤ x ≤ 1) thin films is reported here. The refractive index exhibits a general increase with increasing ZrO 2 content with all values within the range of 1.98–2.14 from 880 nm to 400 nm wavelengths. A composition dependence of the indirect optical bandgap is observed, decreasing from 5.81 eV for HfO 2 to 5.17 eV for Hf 0.4 Zr 0.6 O 2 . The bandgap increases for compositions with x > 0.6, reaching 5.31 eV for Hf 0.1 Zr 0.9 O 2 . Second harmonic signals are measured for 880 nm incident light. The magnitude of the second harmonic signal scales with the magnitude of the remanant polarization in the composition series. Film compositions that display near zero remanent polarizations exhibit minimal second harmonic generation while those with maximum remanent polarization also display the largest second harmonic signal. The results are discussed in the context of ferroelectric phase assemblage in the hafnium zirconium oxide films and demonstrate a path toward a silicon-compatible integrated nonlinear optical material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides

In multi-cellular organisms, cells and tissues coordinate biochemical signal propagation across length scales spanning micrometres to metres. Designing synthetic materials with similar capacities for coordinated signal propagation could allow these systems to adaptively regulate themselves across space and over time. Here, we combine ideas from cell signalling and electronic circuitry to propose a biochemical waveguide that transmits information in the form of a concentration of a DNA species on a directed path. The waveguide could be seamlessly integrated into a soft material because there is virtually no difference between the chemical or physical properties of the waveguide and the material it is embedded within. We propose the design of DNA strand displacement reactions to construct the system and, using reaction–diffusion models, identify kinetic and diffusive parameters that enable super-diffusive transport of DNA species via autocatalysis. Finally, to support experimental waveguide implementation, we propose a sink reaction and spatially inhomogeneous DNA concentrations that could mitigate the spurious amplification of an autocatalyst within the waveguide, allowing for controlled waveguide triggering. Chemical waveguides could facilitate the design of synthetic biomaterials with distributed sensing machinery integrated throughout their structure and enable coordinated self-regulating programmes triggered by changing environmental conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Energy Flow in Thin Shell Implosions and Explosions

Energy flow and balance in convergent systems beyond petapascal energy densities controls the fate of late-stage stars and the potential for controlling thermonuclear inertial fusion ignition. Time-resolved x-ray self-emission imaging combined with a Bayesian inference analysis is used to describe the energy flow and the potential information stored in the rebounding spherical shock at 0.22 PPa (2.2 Gbar or billions of atmospheres pressure). This analysis, together with a simple mechanical model, describes the trajectory of the shell and the time history of the pressure at the fuel-shell interface, ablation pressure, and energy partitioning including kinetic energy of the shell and internal energy of the fuel. Here, the techniques used here provide a fully self-consistent uncertainty analysis of integrated implosion data, a thermodynamic-path independent measurement of pressure in the petapascal range, and can be used to deduce the energy ow in a wide variety of implosion systems to petapascal energy densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

High-resolution magnetic microscopy applications using nitrogen-vacancy centers in diamond

Magnetic microscopy with high spatial resolution helps to solve a variety of technical problems in condensed-matter physics, electrical engineering, biomagnetism, and geomagnetism. In this work we used quantum diamond magnetic microscope (QDMM) setups, which use a dense uniform layer of magnetically-sensitive nitrogen-vacancy (NV) centers in diamond to image an external magnetic field using a fluorescence microscope. We used this technique for imaging few-micron ferromagnetic needles used as a physically unclonable function (PUF) and to passively interrogate electric current paths in a commercial 555 timer integrated circuit (IC). As part of the QDMM development, we also found a way to calculate ion implantation recipes to create diamond samples with dense uniform NV layers at the surface. This work opens the possibility for follow-up experiments with 2D magnetic materials, ion implantation, and electronics characterization and troubleshooting.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Allometric Trophic Networks From Individuals to Socio-Ecosystems: Consumer–Resource Theory of the Ecological Elephant in the Room

A well-known parable is that of the blind men studying an elephant each of which assert the elephant is the part they first hold in their hands, e.g., “rope!” says the tail holder while the leg holder asserts “tree!” The various subdisciplines of ecology appear similar in that we each engage in our enthusiastic but at least somewhat myopic study with remarkably limited agreement or even discussion about the overall system which we all study. Allometric trophic network (ATN) theory offers a path out of this dilemma by integrating across scales, taxa, habitats and organizational levels from physiology to ecosystems based on consumer-resource interactions among co-existing organisms. The network architecture and the metabolic and behavioral processes that determine the structure and dynamics of these interactions form the first principles of ATN theory, which in turn provides a synthetic overview and powerfully predictive framework for ecology from organisms to ecosystems. Beyond ecology, ATN theory also synthesizes eco-evolutionary and socio-ecological research still largely based on consumer-resource mechanisms but respectively integrated with different processes including natural selection and market mechanisms. This paper briefly describes foundations, advances, and future directions of ATN theory including predicting an ecosystem’s phenotype from its community’s genotype in order to accelerate more predictive and unified understanding of the complex systems studied by ecologists and other environmental scientists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic

Observations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercooled liquid and ice amounts (expressed as liquid-water and ice-water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While the correlation between snowfall rate and LWP is rather weak, correlation coefficients between radar-derived snowfall rate and IWP are high (~0.8), which is explained, in part, by the generally low LWP/IWP ratios during significant precipitation. Correlation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (r < 0.3). The results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15 min averaging versus daily averages). Observationally based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Energy Flow in Thin Shell Implosions and Explosions

Energy flow and balance in convergent systems beyond petapascal energy densities controls the fate of late-stage stars and the potential for controlling thermonuclear inertial fusion ignition. Timeresolved x-ray self-emission imaging combined with a Bayesian inference analysis is used to describe the energy flow and the potential information stored in the rebounding spherical shock at 0.22 petaPascal (2.2 Gbar or billions of atmospheres pressure). This analysis, together with a simple mechanical model, describes the trajectory of the shell and the time history of the pressure at the fuel-shell interface, ablation pressure, and energy partitioning including kinetic energy of the shell and internal energy of the fuel. The techniques used here provide a fully self-consistent uncertainty analysis of integrated implosion data, a thermodynamic-path independent measurement of pressure in the petaPascal range, and can be used to deduce the energy flow in a wide variety of implosion systems to petapascal energy densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

End-to-End Performance Management for Large Distributed Storage

Storage systems for large distributed clusters of computer servers are themselves large and distributed. Their complexity and scale make it hard to ensure that applications using them get good, predictable performance. At the same time, shared access to the system from multiple applications, users, and internal system activities leads to a need for predictable performance. This research investigates mechanisms for improving storage system performance in large distributed storage systems through mechanisms that integrate the performance aspects of the path that I/O operations take through the system, from the application interface on the compute server, through the network, to the storage servers. The research focuses on five parts of the I/O path in a distributed storage system: I/O scheduling at the storage server, storage server cache management, client-to-server network flow control, client-to-server connection management, and client cache management.

42 ENGINEERING↗
DOE OSTI2016

Assembly for directing combustion gas

An arrangement is provided for delivering gases from a plurality of combustors of a can-annular gas turbine combustion engine to a first row of turbine blades including a first row of turbine blades. The arrangement includes a gas path cylinder, a cone and an integrated exit piece (IEP) for each combustor. Each IEP comprises an inlet chamber for receiving a gas flow from a respective combustor, and includes a connection segment. The IEPs are connected together to define an annular chamber extending circumferentially and concentric to an engine longitudinal axis, for delivering the gas flow to the first row of blades. A radiused joint extends radially inward from a radially outer side of the inlet chamber to an outer boundary of the annular chamber, and a flared fillet extends radially inward from a radially inner side of the inlet chamber to an inner boundary of the annular chamber.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Retrieval Augmented Generation for Robust Cyber Defense

In cybersecurity, the ability to efficiently analyze and respond to vulnerabilities, weaknesses, attack patterns, and threat tactics is critical for effective defense strategies. With the increasing complexity and volume of cybersecurity data, traditional methods of querying and retrieving information are often inadequate. To address this challenge, we implemented Retrieval-Augmented Generation (RAG) systems—CyRAG and GraphCyRAG—that integrate large language models (LLMs) with both structured data from relational databases and knowledge graphs such as Neo4j. CyRAG is designed to handle structured data, focusing on CVE (Common Vulnerabilities and Exposures) and CWE (Common Weakness Enumeration) entities to generate accurate and context-rich responses. In contrast, GraphCyRAG leverages Neo4j knowledge graphs to retrieve interconnected information from CVE, CWE, CAPEC (Common Attack Pattern Enumeration and Classification), and ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) datasets. By utilizing Neo4j’s graph-based framework, GraphCyRAG enables deeper traversal of relationships between vulnerabilities and attack patterns, providing cybersecurity analysts with more comprehensive insights into potential attack vectors and mitigation strategies. Our preliminary results demonstrate that integrating knowledge graphs with RAG significantly enhances both the accuracy and depth of threat analysis, allowing for the retrieval of dynamic, real-time data and the generation of contextually aware responses. This approach helps analysts uncover hidden relationships between cyber entities, predict exploit paths, and prioritize mitigation efforts effectively. The integration of RAG with cybersecurity knowledge graphs represents a significant advancement in cybersecurity threat intelligence, enabling more informed decision-making and stronger defense strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Path Sampling for Rare Events Boosted by Machine Learning

The study by Jung et al. introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. Here, this commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method’s potential impact and limitations.

Minh, Porhouy [Univ. of Minnesota, Minneapolis, MN↗
DOE OSTI2025

DEVELOPMENT OF AN INTEGRATED SECURITY AND SAFETY MONITORING SYSTEM FOR SPENT NUCLEAR FUEL AND HIGH-LEVEL WASTE TRANSPORTATION

This paper provides an overview of the progress to date, and discussion of the path forward, related to designing, fabricating, and testing an integrated security and safety monitoring system (ISSMS) for railcars used to transport spent nuclear fuel (SNF) and high-level radioactive waste (HLW) in the United States. The system will comply with the US Department of Energy’s (DOE) Order 460.2B “Departmental Materials Transportation Management” and the Association of American Railroads’ (AAR) standard S-2043 “Performance Specification for Trains Used to Carry High-level Radioactive Material” [1] developed specifically for railcars used to transport high-level radioactive material (HLRM). DOE is in the process of developing and testing an ISSMS that will satisfy both DOE requirements and AAR standards. DOE has already developed railcar designs for transportation of HLRM. In 2024, DOE’s Atlas railcar project completed the design, fabrication and testing of three railcar types: transportation cask-carrying, buffer, and security escort, resulting in AAR conditional approval to operate on freight rail networks in North America. DOE decided to combine the required security and safety systems into one system, and this combined system is the subject of the current effort. DOE is developing and proposes to implement the ISSMS for these railcars as part of the build out of the railcar fleet. DOE began planning for development of the ISSMS in February 2020. The project is divided into seven phases starting with conceptual design and continuing through production design, as shown in Figure 1. An earlier version of the system was tested as part of the Atlas railcar consist demonstration test run in 2023. This paper describes the design features and system testing using the Atlas project railcars, and laboratory testing completed to date. The paper will also describe the activities planned to support the DOE project to ship the High Burn-Up Research Cask (HBRC) in 2027.

Schultze, Michael [ORNL] (ORCID:0000000283205671)↗
DOE OSTI2014

Communication: Phase diagram of C{sub 36} by atomistic molecular dynamics and thermodynamic integration through coexistence regions

We report an atomistic molecular dynamics determination of the phase diagram of a rigid-cage model of C{sub 36}. We first show that free energies obtained via thermodynamic integrations along isotherms displaying “van der Waals loops,” are fully reproduced by those obtained via isothermal-isochoric integration encompassing only stable states. We find that a similar result also holds for isochoric paths crossing van der Waals regions of the isotherms, and for integrations extending to rather high densities where liquid-solid coexistence can be expected to occur. On such a basis we are able to map the whole phase diagram of C{sub 36}, with resulting triple point and critical temperatures about 1770 K and 2370 K, respectively. We thus predict a 600 K window of existence of a stable liquid phase. Also, at the triple point density, we find that the structural functions and the diffusion coefficient maintain a liquid-like character down to 1400–1300 K, this indicating a wide region of possible supercooling. We discuss why all these features might render possible the observation of the melting of C{sub 36} fullerite and of its liquid state, at variance with what previously experienced for C{sub 60}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Scaling growth rates for perovskite oxide virtual substrates on silicon

Abstract The availability of native substrates is a cornerstone in the development of microelectronic technologies relying on epitaxial films. If native substrates are not available, virtual substrates - crystalline buffer layers epitaxially grown on a structurally dissimilar substrate - offer a solution. Realizing commercially viable virtual substrates requires the growth of high-quality films at high growth rates for large-scale production. We report the stoichiometric growth of SrTiO 3 exceeding 600 nm hr −1 . This tenfold increase in growth rate compared to SrTiO 3 grown on silicon by conventional methods is enabled by a self-regulated growth window accessible in hybrid molecular beam epitaxy. Overcoming the materials integration challenge for complex oxides on silicon using virtual substrates opens a path to develop new electronic devices in the More than Moore era and silicon integrated quantum computation hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A generalized anisotropic deformation formulation for geomaterials

In this paper, the Combined Finite-Discrete Element Method (FDEM) has been applied to analyze the deformation of anisotropic geomaterials. In the most general case geomaterials are both nonhomogeneous and non-isotropic. With the aim of addressing anisotropic material problems, improved 2D FDEM formulations have been developed. These formulations feature the unified hypo-hyper elastic approach combined with a multiplicative decomposition-based selective integration for volumetric and shear deformation modes. This approach is significantly different from the co-rotational formulations typically encountered in finite element codes. Unlike the corotational formulation, the multiplicative decomposition-based formulation naturally decomposes deformation into translation, rotation, plastic stretches, elastic stretches, volumetric stretches, shear stretches, etc. This approach can be implemented for a whole family of finite elements from solids to shells and membranes. This novel 2D FDEM based material formulation was designed in such a way that the anisotropic properties of the solid can be specified in a cell by cell basis, therefore enabling the user to seed these anisotropic properties following any type of spatial variation, for example, following a curvilinear path. In addition, due to the selective integration, there are no problems with volumetric or shear locking with any type of finite element employed.

58 GEOSCIENCES↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Integrated soft snake robot for gamma spectroscopy of radiologically contaminated ducting

Anticipated growth in the number and complexity of nuclear facilities entering the decommissioning phase of their lifespan requires innovative methods of mitigating radiological and safety hazards for workers. Advancements in robotics provide opportunities for remote characterization of radiologically contaminated ducts and pipes, reducing worker exposure. However, the nuclear decommissioning industry has shown a hesitancy in adopting new technology due to concerns regarding capital costs and a lack of demonstration data in representative environments. This work aims to leverage novel soft robotics to develop and demonstrate a sensor-integrated characterization tool for contaminated ducting systems. Two gamma spectrometers – one scintillation-based and one solid-state – were selected and integrated into a pneumatically-actuated snake-like soft robot using stretchable electronic cabling. The robot was then demonstrated at the Idaho National Laboratory’s Critical Infrastructure Test Range Complex. The robot maneuvered through a pseudo-constrained corridor containing several sealed sources and the integrated spectrometers collected spectra at various points along the path of travel. Both spectrometers successfully collected spectra with identifiable, isotope-specific peaks. To evaluate the effect of contamination buildup on the collected spectra, the robot with the integrated scintillation detector was tested using a liquid radioactive source. The resulting spectra showed little detection interference from the liquid contamination; however, a short-lived isotope was used which may not be representative of longer-lived contamination.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗