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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

2007 Lunar Regolith Simulant Workshop Overview

The National Aeronautics and Space Administration (NASA) vision has as a cornerstone, the establishment of an Outpost on the Moon. This Lunar Outpost will eventually provide the necessary planning, technology development, and training for a manned mission to Mars in the future. As part of the overall activity, NASA is conducting Earth-based research and advancing technologies to a Technology Readiness Level (TRL) 6 maturity under the Exploration Technology Development Program that will be incorporated into the Constellation Project as well as other projects. All aspects of the Lunar environment, including the Lunar regolith and its properties, are important in understanding the long-term impacts to hardware, scientific instruments, and humans prior to returning to the Moon and living on the Moon. With the goal of reducing risk to humans and hardware and increasing mission success on the Lunar surface, it is vital that terrestrial investigations including both development and verification testing have access to Lunar-like environments. The Marshall Space Flight Center (MSFC) is supporting this endeavor by developing, characterizing, and producing Lunar simulants in addition to analyzing existing simulants for appropriate applications. A Lunar Regolith Simulant Workshop was conducted by MSFC in Huntsville, Alabama, in October 2007. The purpose of the Workshop was to bring together simulant developers, simulant users, and program and project managers from ETDP and Constellation with the goals of understanding users' simulant needs and their applications. A status of current simulant developments such as the JSC-1A (Mare Type Simulant) and the NASA/U.S. Geological Survey Lunar Highlands-Type Pilot Simulant (NU-LHT-1 M) was provided. The method for evaluating simulants, performed via Figures of Merit (FoMs) algorithms, was presented and a demonstration was provided. The four FoM properties currently being assessed are: size, shape, density, and composition. Some of the Workshop findings include: simulant developers must understand simulant users' needs and applications; higher fidelity simulants are needed and needed in larger quantities now; simulants must be characterized to allow "apples-to-apples" comparison of test results; simulant users should confer with simulant experts to assist them in the selection of simulants; safety precautions should be taken in the handling and use of simulants; shipping, storing, and preparation of simulants have important implications; and most importantly, close communications among the simulant community must be maintained and will be continued via telecoms, meetings, and an annual Lunar Regolith Simulant Workshop.

McLemore, Carole A.↗
DOE OSTI2023

Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry

ABSTRACT Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to 1 m in depth from irrigated and planted field treatments and was analyzed using a suite of omics and chemical analyses. The soil microbial community composition was impacted more by irrigation and plant cover treatments than by soil depth. By contrast, metabolomes, lipidomes, and proteomes differed more with soil depth than treatments. Deep soil (>50 cm) had higher soil pH and calcium concentrations and higher levels of organic acids, bicarbonate, and triacylglycerides. By contrast, surface soil (0–5 cm) had higher concentrations of soil organic matter, organic carbon, oxidizable carbon, and total nitrogen. Surface soils also had higher amounts of sugars, sugar alcohols, phosphocholines, and proteins that reflect osmotic and oxidative stress responses. The lipidome was more responsive to perennial tall wheatgrass treatments compared to the metabolome or proteome, with a striking change in diacylglyceride composition. Permanganate oxidizable carbon was more consistently correlated to metabolites and proteins than soil organic and inorganic carbon and soil organic matter. This study reveals specific compounds that reflect differences in organic, inorganic, and oxidizable soil carbon fractions that are impacted by interactions between irrigation-supplied moisture and plant cover in calcareous soil profiles. IMPORTANCE Carbon is cycled through the air, plants, and belowground environment. Understanding soil carbon cycling in deep soil profiles will be important to mitigate climate change. Soil carbon cycling is impacted by water, plants, and soil microorganisms, in addition to soil mineralogy. Measuring biotic and abiotic soil properties provides a perspective of how soil microorganisms interact with the surrounding chemical environment. This study emphasizes the importance of considering biotic interactions with inorganic and oxidizable soil carbon in addition to total organic carbon in carbonate-containing soils for better informing soil carbon management decisions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

or-topas: Operations Research Toolkit for Pyomo Alternative Solutions

SAND2026-16702O OR-TOPAS: Operations Research Toolkit for Pyomo Alternative Solutions is a tool that enhances optimization applications defined by the Pyomo modeling library. It offers functions to generate optimal or near-optimal solutions, operating independently of Pyomo’s solver interface. Users can configure these functions with specific solver names and options, resulting in a custom solution object that returns a list of solutions. The OR-TOPAS library includes methods tailored to the properties of the model, such as binary integer programs versus linear programs, and specific solver interfaces like Gurobi. It does not provide models for specific applications, but it is applicable to a wide range of Pyomo optimization models. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Siirola, John [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Fracture properties of high-entropy alloys

Abstract Since the concept of high-entropy alloys (HEAs) as materials with at least four or five principal elements in (near)-equiatomic composition was introduced in 2004, this new class of materials has penetrated essentially all materials science-related fields. The main reason for this is that some face-centered-cubic alloy compositions have been shown to exhibit truly outstanding mechanical properties with extraordinary combinations of strength, ductility, and fracture toughness, particularly at cryogenic temperatures, whereas certain body-centered-cubic refractory compositions display remarkable high-temperature strength. While significant efforts have been put into rapid screening and narrowing the compositional space of HEAs to a manageable scope, there are still only a few metallic alloys that push the limits of mechanical performance. Here, we review work on some of the most damage-tolerant HEAs discovered to date and discuss the fundamental reasons why their resistance to fracture and subsequent stable crack growth is so exceptional. Graphical abstract

Gludovatz, Bernd (ORCID:0000000224203879)↗
NASA NTRS1988

A relational data-knowledge base system and its potential in developing a distributed data-knowledge system

A new approach used in constructing a rational data knowledge base system is described. The relational database is well suited for distribution due to its property of allowing data fragmentation and fragmentation transparency. An example is formulated of a simple relational data knowledge base which may be generalized for use in developing a relational distributed data knowledge base system. The efficiency and ease of application of such a data knowledge base management system is briefly discussed. Also discussed are the potentials of the developed model for sharing the data knowledge base as well as the possible areas of difficulty in implementing the relational data knowledge base management system.

Rahimian, Eric N.↗
NASA NTRS1991

Combined fluorescence, reflectance, and ground measurements of a stressed Norway spruce forest for forest damage assessment

The detection and monitoring of stress and damage in forested areas is of utmost importance to forest managers for planning purposes. Remote sensing are the most suitable means to obtain this information. This requires that remote sensing data employed in a forest survey be properly chosen and utilized for their ability to measure canopy spectral features directly related to key tree and canopy properties that are indicators of forest health and vitality. Plant reflectance in the visible to short wave IR regions (400 to 2500 nm) provides information on its biochemical, biophysical, and morphological make up, whereas plant fluorescence in the 400 to 750 nm region is more indicative of the capacity and functioning of its photosynthetic apparatus. A measure of both these spectral properties can be used to provide an accurate assessment of stress and damage within the forest canopy. Foliar chlorophyll and nitrogen are essential biochemical constituents required for the proper functioning and maintenance of a plant's biological processes. Chlorophyll-a is the prime reactive center for photosynthesis, by which a plant converts CO2 and H2O into necessary plant products. Nitrogen forms an important component of the amino-acids, enzymes, proteins, alkaloids, and cyanogenic compounds that make up a plant, including its pigments. Both chlorophyll and nitrogen have characteristic absorption features in the visible to short wave IR region. By measuring the wavelength position and depth of these features and the fluorescence response of the foliage, the health and vitality of a canopy can be ascertained. Examples for a stressed Norway spruce forest in south-eastern Austria are presented.

Banninger, C.↗
NASA NTRS1999

Numerical Modeling of Three-Dimensional Fluid Flow with Phase Change

We present a numerical method to compute phase change dynamics of three-dimensional deformable bubbles. The full Navier-Stokes and energy equations are solved for both phases by a front tracking/finite difference technique. The fluid boundary is explicitly tracked by discrete points that are connected by triangular elements to form a front that is used to keep the stratification of material properties sharp and to calculate the interfacial source terms. Two simulations are presented to show robustness of the method in handling complex phase boundaries. In the first case, growth of a vapor bubble in zero gravity is studied where large volume increase of the bubble is managed by adaptively increasing the front resolution. In the second case, growth of a bubble under high gravity is studied where indentation at the rear of the bubble results in a region of large curvature which challenges the front tracking in three dimensions.

Esmaeeli, Asghar↗
NASA NTRSDate not supplied

Geometric Assessment of PlanetScope Imagery

Commercial companies such as Maxar, Planet Labs, and BlackSky have built large archives of high resolution (1-3 m) Earth observing data. The high temporal resolution and global coverage of these satellites makes commercial satellite images ideal for scientists studying rapidly changing processes such as flooding and fires. The high spatial resolution allows scientists to push studies to a fine scale such as monitoring agricultural growth experiments or detecting vehicles for traffic management. Scientists using these data need to know geolocation accuracy, band-to-band registration (BBR), and pixel footprint size to best determine appropriate applications as well as what extra steps they will need to take to use the data. Here, we find those key geometric properties for images from Planet Labs’ SuperDove constellation. Geolocation accuracy is assessed relative to WorldView images at globally distributed cities, band-to-band registration (BBR) is examined for every image we acquired (200+ images), and pixel footprint sizes are calculated from edge responses of the images over Cal/Val sites in China, India, or USA both near launch time and 1+ years after launch. We find that SuperDove geolocation is highly self-consistent with average global offsets among SuperDove images of 3.80 m (1.27 pixels). Despite this self-consistency, relative geolocation accuracy (with WorldView images as reference) varies by location from 3.20 m – 28.09 m mean offset. SuperDove BBR is sub-pixel and varies by band, with a mean radial offset relative to red band varying from 0.39 - 1.13 m. SuperDove image footprint size improves after 1+ years in orbit, with an average footprint size of 3.41 pixels (10.24 m) soon after launch and 3.23 pixels (9.7 m) 1+ years after launch. The BBR and self-consistency offsets are relatively small, given the large footprint sizes. A similarly extensive investigation is in progress for Planet Labs’ Dove-R series.

Alana G. Semple↗
DOE OSTI2021

Mapdfn.py

SAND2021-4824 O mapdfn.py takes the output of dfnWorks describing a discrete fracture network (DFN) and converts the DFN to an equivalent porous medium representation. This is done by looping through cells in a 3-D model domain, tagging those intersected by fractures, and calculating effective properties. Coordinate transformations are imported from an open source script (transformations.py) developed at the University of California. An associated driver script, mapdfn2pflotran.py, calls the functions in mapdfn.py and writes the mapdfn.py output to hdf5 files for use with PFLOTRAN. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-4824 O

Stein, Emily↗
DOE OSTI2024

A nanoscale photonic thermal transistor for sub-second heat flow switching

Abstract Control of heat flow is critical for thermal logic devices and thermal management and has been explored theoretically. However, experimental progress on active control of heat flow has been limited. Here, we describe a nanoscale radiative thermal transistor that comprises of a hot source and a cold drain (both are ~250 nm-thick silicon nitride membranes), which are analogous to the source and drain electrodes of a transistor. The source and drain are in close proximity to a vanadium oxide (VO x )-based planar gate electrode, whose dielectric properties can be adjusted by changing its temperature. We demonstrate that when the gate is located close ( < ~1 µm) to the source-drain device and undergoes a metal-insulator transition, the radiative heat transfer between the source and drain can be changed by a factor of three. More importantly, our nanomembrane-based thermal transistor features fast switching times ( ~ 500 ms as opposed to minutes for past three-terminal thermal transistors) due to its small thermal mass. Our experiments are supported by detailed calculations that highlight the mechanism of thermal modulation. We anticipate that the advances reported here will open new opportunities for designing thermal circuits or thermal logic devices for advanced thermal management.

42 ENGINEERING↗
DOE OSTI2026

Lawrence Livermore National Laboratory – Innovation and Partnerships Office (IPO): FY24 Technology Transfer Report (2025 Report V3)

Lawrence Livermore National Laboratory’s (LLNL) Innovation and Partnerships Office (IPO) is the focal point for Laboratory engagement with industry. Organizations partner with LLNL to unlock the commercial viability of breakthrough technologies invented at the Laboratory and collaborate to mature them in order to solve complex industry challenges. LLNL technology transfer connects cutting-edge science with real-world applications, empowering businesses to thrive while advancing the nation’s technological edge. We do this by identifying new economic opportunities, protecting LLNL intellectual property (IP) and transferring it to the private sector through licensing and partnerships

42 ENGINEERING↗
NASA NTRS2023

Thermal Stability of M x O y Doped Zirconia Aerogels (M = Y, Yb, Gd, Ce, Ca) Studied Through 1200°C

The high porosities and low densities of ceramic aerogels offer outstanding insulative performance in applications where weight is a critical factor. The high surface area-to-volume ratios and specific surface areas provide extremely low thermal conductivity, but also contribute to rapid densification of the pore structure at elevated temperatures. This densification diminishes their favorable properties and inhibits use of aerogels in high temperature applications. This work contributes to a design framework for thermally stable aerogels via the study of dopant chemistry (Y, Yb, Gd, Ca, Ce) in zirconia aerogels. The structural evolution was studied to 1200°C using nitrogen physisorption, scanning electron microscopy, and x-ray diffraction. The role of dopant identity and concentration in thermal stability was elucidated. In context of the design framework, dopant chemistry is an aggregate for many closely related material properties, each of which may contribute to aerogel structural evolution. To develop a truly predictive design framework for ceramic-based aerogels, systematic and comprehensive evaluation of thermodynamic and kinetic properties must be performed in conjunction with studies on structural evolution.

aerogel↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
DOE OSTI2021

Constructing high-performance radiation-resistant ternary YSZ-MgO-CNT nanocomposites via tailored nanostructures

Developing long-lifetime bulk-form ceramic-based materials with high irradiation resistance is crucial for advanced nuclear systems. Here, we incorporated carbon nanotubes (CNTs) into yttria-stabilized zirconia (YSZ) and magnesia (MgO) nanocrystals to fabricate bulk YSZ-MgO-CNT nanocomposites with abundant ternary nanostructures by spark plasma sintering. To understand the role of tailored ternary nanostructure on irradiation, we investigated the microstructure and mechanical properties evolutions of the YSZ-MgO-CNT nanocomposites irradiated by multi-energy He + ions at high temperature to different fluences. Compared with the single-phase YSZ and ultrafine-grained YSZ-MgO composites, the YSZ-MgO-CNT nanocomposites possessed higher ability to manage irradiation-induced He bubbles/defects via the defect-interface interactions of proposed “loading-unloading” and “loading-transporting-unloading” mechanisms for controlling the dynamical behaviors of He atoms/defects in the CNT-doped ternary nanostructures, thereby presenting more stable microstructure and better performance in resisting irradiation hardening. In conclusion, this work provides insight into the design of advanced inert matrix nuclear fuel and new nuclear waste management materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Historical Documentation of Buildings 0460 and 0463 at Technical Area 16 (Volume 1)

This report provides documentation as a standard mitigation measure to the adverse effects that occurred by the demolition of theses historic properties. To mitigate the adverse effects, LANL has followed Section 106 process contained in 36 CFR 800.6, resolution of adverse effects. In addition to these regulations and within this report, LANL has implemented the standards for documenting and reporting in accordance with the A Plan for the Management of the Cultural Heritage at Los Alamos National Laboratory, New Mexico (CRMP), LA-UR-19-21590, formerly LA-UR-15-27624. These standard reporting measures include archival-quality digital photographs of the building’s interior, exterior, outside landscape; updating LANL historic building survey forms including 11 in. x 17 in. copies (in a reduced scale) of key original and as-built drawings; identification and documentation of historically significant equipment and artifacts; a comprehensive list of LANL architectural drawings; construction-history maps of TA-16 including current Register Eligible and Ineligible Buildings; and a detailed use history of the building and technical division associated with its operation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1982

Electrolyte management in porous battery components. Static measurements

The interaction between the porous hydrogen and nickel electrodes and microporous separator with respect to electrolyte management in nickel/hydrogen cells has been investigated. The distribution of electrolyte among the components has been measured and correlated with the pore size distributions, total void volume, and resistance of a variety of electrodes and separators. Calculations are used to show the effects of systematically varying these properties.

Abbey, K. M.↗
DOE OSTI2025

Impact of droplet oxidation on mechanical properties of an Al-7Si-0.4Mg alloy fabricated with liquid metal jetting

Droplet-on-demand liquid metal jetting (DOD-LMJ) is a new method for additive manufacturing of bulk structural alloys. Here, we report on the microstructure, tensile, and fatigue properties of an Al-7Si-0.4Mg (A356) alloy fabricated with LMJ. Liquid metal droplets were shielded by high-purity Ar gas shroud during deposition. Atom probe tomography revealed that a few nanometers thick (Al-Mg-Si)-O oxide film formed on the droplets despite Ar gas shielding. Tensile tests on peak-aged LMJ A356 alloy showed that yield strength was isotropic (250 MPa), but ductility was lower in the build direction (6.1 ± 1.4 %) compared to the transverse direction (9.4 ± 1.0 %). Lower ductility in the build direction was attributed to delamination of metal-oxide interfaces at layer boundaries. The ductility and yield strength of LMJ A356 were similar to cast A356 and laser powder bed fused (LPBF) A357 alloys, indicating the limited impact of oxide film on tensile properties. The oxide film severely impacted the fatigue properties. Fatigue resistance of LMJ A356 was limited by fatigue crack initiation at lack-of-fusion defects and fatigue crack propagation along layer boundaries by delamination of the metal-oxide interface. The fatigue strength of LMJ A356 at 60 MPa was lower than cast A356 and LPBF A357 alloys in the peak-aged condition. This research underscores the need for managing droplet oxidation during LMJ additive manufacturing of structural alloys.

42 ENGINEERING↗