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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Design feasibility of an advanced technology supersonic cruise aircraft

Research and development programs provide confidence that technology is in-hand to design an economically attractive, environmentally sound supersonic cruise aircraft for commercial operations. The principal results of studies and tests are described including those which define the selection of significant design features. These typically include the results of: (1) wind-tunnel tests, both subsonic and supersonic, (2) propulsion performance and acoustic tests on noise suppressors, including forward-flight effects, (3) studies of engine/airframe integration, which lead to the selection of engine cycles/sizes to meet future market, economic, and social requirements; and (4) structural testing.

Rowe, W. T.↗
DOE OSTI2020

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Eolian Erosion on Mars: Three Scales

Formation and erosion of deposits on Mars with vertical scales of 100 to 1,000 meters and horizontal scales of 100 km may have occurred at rates very different from present ones. Evidence for the rates on small scales comes from observations of dust storms and photometry of wind streaks. These observations cover scales less than 1 cm vertically and time scales less than 10 years. Intermediate scale features such as yardangs and sand dunes are more difficult to evaluate, but for dunes, plausible rates of transport may be calculated. Net eolian action in some areas may be so low that in order to study the large features one needs to select those likely to have been modified more rapidly and that exhibit distinctive contrasts with surrounding areas. Best suited for such study are relatively soft deposits overlying more resistant substrates. Viking Orbiter images of four of the largest such areas were examined to test how the erosional features correlate with patterns attributable to winds, properties of deposits, or non-eolian erosion, and for constraints on erosion and transport rates.

Thomas, P. G.↗
NASA NTRS1992

The composition of Martian aeolian sands: Thermal emissivity from Viking IRTM observations

Aeolian sands provide excellent surfaces for the remote determination of the mineralogic composition of Martian materials, because such deposits consist of relatively well-sorted, uniform particle sizes and might consist of chemically unaltered, primary mineral grains derived from bedrock. Dark features on the floors of Martian craters are controlled by aeolian processes and many consist largely of unconsolidated, windblown sand. Measurement of the thermal emissivity of geologic materials provides a way to identify mid-infrared absorption bands, the strength and positions of which vary with mineral structure and composition. The Viking Infrared Thermal Mapper (IRTM) had four surface-sensing mid-IR bands, three of which, the 7, 9, and 11 micron channels, correspond to absorption features characteristic of carbonates, sialic, and mafic minerals, respectively. In this study, the highest quality IRTM data were constrained so as to avoid the effects of atmospheric dust, clouds, surface frosts, and particle size variations (the latter using data obtained between 7 and 9 H, and they were selected for dark intracrater features such that only data taken directly from the dark feature were used, so as to avoid thermal contributions from adjacent but unrelated materials. Three-point emissivity spectra of Martian dart intracrater features were compared with laboratory emission spectra of minerals and terrestrial aeolian sands convolved using the IRTM response function to the four IRTM spectral channels.

Edgett, Kenneth S.↗
DOE OSTI2022

High Flux Isotope Reactor Low Enriched Uranium U-10Mo Fuel Design Parameters

Activities to convert the HFIR from HEU to LEU are ongoing as part of the US Department of Energy (DOE) National Nuclear Security Administration (NNSA) nuclear nonproliferation mission. Design activities to study the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a uranium-molybdenum (U-10Mo) monolithic alloy fuel. This high-density alloy contains 90 wt % uranium and 10 wt % molybdenum and has a uranium density of 15.318gU/cm 3 . The goal of these studies is to generate several candidate HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. Recent advancements in modeling and simulation tools and design methods enabled a thorough analysis of the available design space with U-10Mo fuel. A surrogate model used this analysis as training data to quickly determine the performance of a design given specific design parameters. An optimization module used this surrogate model to quickly search this multidimensional search space given specific desired performance characteristics. This approach was made possible by the large available design space with U-10Mo fuel. Shift, a Monte Carlo tool optimized for high-performance computing (HPC) architectures, was used for faster calculation and better data management for reactor physics simulations. Once most of these design studies were complete, a new suite called the Python HFIR Analysis and Measurement Engine (PHAME) was developed to connect all fuel design analysis steps, making design studies more efficient and reproducible. The post-processing capabilities of these new tools are leveraged for the information provided herein. Leveraging these tools, several candidate fuel designs were selected with varying levels of feature complexity and reactor performance. This report provides design feature details for four selected HFIR LEU U-10Mo fuel designs and their corresponding performance and safety metrics. Nominal best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities, are provided. Simulations show that the high uranium density of U-10Mo fuel provides a large potential design space that enables various LEU designs to meet HEU core performance metrics and safety requirements with a power increase from 85 MW (HEU) to 95 MW or 100 MW (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Selected Area Deposition of PtC x Nanostructures: Implications for Functional Coatings of 3D Nanoarchitectures

Here, we report that selected area deposition of 3D nanostructures is induced via a pyrolytic laser chemical vapor deposition (CVD) process where selected area heating results from the unique photothermal transport regime that is intrinsic to 3D nanostructures. PtC x composite nanostructures were deposited by focused electron beam-induced deposition (FEBID) and used as 3D templates. Subsequent simultaneous localized delivery of an organometallic PtC x precursor and pulsed 915 nm laser irradiation to the pre-defined nanostructures results in selected area deposition on the FEBID features. Results indicate the ability to initiate the process on sub-diffraction-limit nanoscale features. To elucidate the mechanisms that are operative in the selected area deposition, we analyze the effects of the laser repetition rate, power, and pulse duration. Thermal simulations corroborate that the pseudo-1D thermal transport of the nanostructure geometry coupled with the optical and thermal properties of the nanostructure governs the CVD reaction. The results demonstrated here suggest that controlling the thermal transport in nanomaterial architectures could be a useful means to spatially control localized photothermally stimulated chemical reactions and induce selected area reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Structures of glasses created by multiple kinetic arrests

X-ray scattering has been used to characterize glassy itraconazole (ITZ) prepared by cooling at different rates. Faster cooling produces ITZ glasses with lower (or zero) smectic order with more sinusoidal density modulation, larger molecular spacing, and shorter lateral correlation between the rod-like molecules. We find that each glass is characterized by not one, but two fictive temperatures T f (the temperature at which a chosen order parameter is frozen in the equilibrium liquid). The higher T f is associated with the regularity of smectic layers and lateral packing, while the lower T f with the molecular spacings between and within smectic layers. This indicates that different structural features are frozen on different timescales. The two timescales for ITZ correspond to its two relaxation modes observed by dielectric spectroscopy: the slower δ mode (end-over-end rotation) is associated with the freezing of the regularity of molecular packing and the faster α mode (rotation about the long axis) with the freezing of the spacing between molecules. Finally, our finding suggests a way to selectively control the structural features of glasses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2012

Autonomous Rover Traverse and Precise Arm Placement on Remotely Designated Targets

This software controls a rover platform to traverse rocky terrain autonomously, plan paths, and avoid obstacles using its stereo hazard and navigation cameras. It does so while continuously tracking a target of interest selected from 10 20 m away. The rover drives and tracks the target until it reaches the vicinity of the target. The rover then positions itself to approach the target, deploys its robotic arm, and places the end effector instrument on the designated target to within 2-3-cm accuracy of the originally selected target. This software features continuous navigation in a fairly rocky field in an outdoor environment and the ability to enable the rover to avoid large rocks and traverse over smaller ones. Using point-and-click mouse commands, a scientist designates targets in the initial imagery acquired from the rover s mast cameras. The navigation software uses stereo imaging, traversability analysis, path planning, trajectory generation, and trajectory execution. It also includes visual target tracking of a designated target selected from 10 m away while continuously navigating the rocky terrain. Improvements in this design include steering while driving, which uses continuous curvature paths. There are also several improvements to the traversability analyzer, including improved data fusion of traversability maps that result from pose estimation uncertainties, dealing with boundary effects to enable tighter maneuvers, and handling a wider range of obstacles. This work advances what has been previously developed and integrated on the Mars Exploration Rovers by using algorithms that are capable of traversing more rock-dense terrains, enabling tight, thread-the-needle maneuvers. These algorithms were integrated on the newly refurbished Athena Mars research rover, and were fielded in the JPL Mars Yard. Forty-three runs were conducted with targets at distances ranging from 5 to 15 m, and a success rate of 93% was achieved for placement of the instrument within 2-3 cm of the target.

Nesnas, Issa A.↗
NASA NTRS1973

Otolithic influences on extraocular and intraocular muscles

Selective stimulation of utricular gravireceptors leads to gross activation of the bulbar reticular formation where a strong interaction with evoked spino-bulbo-spinal reflex activity occurs. The utricular neurons encountered by microelectrodes in the lateral vestibular nuclei show four types of elicited activity; two of these display an increased firing rate, and two exhibit pronounced inhibitory effects. Application of a stimulus of long duration and constant intensity to the utricle has shown that rapid adaptation of the peripheral receptors is a prominent feature. The effects of selective utricular stimulation upon eye movements, as recorded by the corneoretinal potential method, have been studied in experiments on cats and monkeys and it can be firmly stated that prolonged stimulation of the utricle can evoke strong primary nystagmus, followed by a secondary nystagmus at the cessation of stimulation. The action of utricular stimulation on ocular reflexes has been examined further, with particular attention to evoked pupillary reactions in both cats and monkeys: constriction during the fast phase of the brisk conjugate eye movement, and dilatation during the flow phase.

Gernandt, B. E.↗
DOE OSTI2022

GCS site selection in saline Miocene formations in South Louisiana

One of the key requirements of a commercial scale geologic carbon sequestration (GCS) site selection process is identifying injection sites to safely inject and retain injected CO 2 over extended time scales. This requires thorough evaluation of site-specific surface and subsurface features. A site screening and selection framework was developed and used to identify potential sites in South Louisiana, incorporating surface data including site proximity to large CO 2 stationary sources, vicinity of large population centres and state lands, land usage, legacy oil and gas wells, and local/regional geologic trends. Regionally the Miocene sediments thicken and dip towards the south, reflecting a prograding deltaic environment during the Miocene which resulted in the deposition of thick packages of fine-grained sandstones suitable for CO 2 storage. The results of three example selected sites that are spatially separated with varying subsurface and surface features are presented in this study. The multiple stacked sand zones at these sites provide ample amount of pore volume for CO 2 storage with varying degree of stratigraphic/structural traps for long terms storage containment. Here, the screening framework presented in this study and the detailed evaluation of three specific sites in South Louisiana provides a process to screen and characterize sites for GCS projects and the suitability of these Miocene sediments for commercial scale CO 2 storage projects. It is envisioned that the analysis described in this paper will be beneficial for a large number of audiences involved in GCS work and aid in the site selection process.

58 GEOSCIENCES↗
DOE OSTI2025

WELLS Interactive Application

The Wellbore Exploration and Location Logistic System (WELLS) Interactive Application is an interactive tool to enable easy exploration and visualization of the living national wellbore database (WELLS Database (https://edx.netl.doe.gov/dataset/wells_database)). The tool and underlying database were created and are maintained by the National Energy Technology Laboratory (NETL), providing visualization of the more than six million public wellbore records from more than 65 authoritative state, federal, and tribal resources. The WELLS Interactive Application serves up wellbore data from oil, gas, underground injection, research, geothermal, geotechnical, groundwater, and other types of wells in a single, standardized, unified system. In addition to the surface location of these wells, the underlying database combines select key attributes for features such as well age, depth, and operating status. The system also provides users with references back to the original sources used in this unified platform. The underlying data can be accessed through the WELLS Database: https://edx.netl.doe.gov/dataset/wells_database Additional Information: The WELLS Interactive Application (formerly titled CO2-Locate) enables visualization and access to the public wellbore records through an intuitive web-based mapping tool. The WELLS Interactive Application was designed to help users visualize, query, analyze, and download wellbore records. Public wellbore points are included as a layer in the Map page, called Public Wells. Additionally, a multivariate hexagon grid summarizing well density from proprietary well data, called Well Density, is included to identify data gaps between the public and proprietary well data. Filtering functionalities in the tool allow these two layers to be spatially filtered by state, county, or basin as well as by status, type, true vertical depth, and spud year. The WELLS Interactive Application also contains a Near Me tool can be used to search and explore wellbore data within a user-defined distance of a specified location on the map, which can also be downloaded. The Query tool allows users to query the selected or filtered wells in the Public Wells layer and export the data. For additional information on these tool functionalities, see the help documentation on the About page of the tool. Notes for Consideration: The Well Density layer provided in this application is derived from proprietary wellbore data, the records of which do not always contain values for key features (status, type, true vertical depth, or spud year). Therefore, data might not be available when layers are queried for all filter combinations. Additionally, visualizing layers and applying filters may take additional time to load (i.e., draw on the map) due to the large size of the data.

ccs↗
DOE OSTI2021

Computational Framework for Machine-Learning-Enabled 13 C Fluxomics

13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

13C metabolic flux analysis↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
NASA NTRS1983

Space Shuttle Columbia views the world with imaging radar: The SIR-A experiment

Images acquired by the Shuttle Imaging Radar (SIR-A) in November 1981, demonstrate the capability of this microwave remote sensor system to perceive and map a wide range of different surface features around the Earth. A selection of 60 scenes displays this capability with respect to Earth resources - geology, hydrology, agriculture, forest cover, ocean surface features, and prominent man-made structures. The combined area covered by the scenes presented amounts to about 3% of the total acquired. Most of the SIR-A images are accompanied by a LANDSAT multispectral scanner (MSS) or SEASAT synthetic-aperture radar (SAR) image of the same scene for comparison. Differences between the SIR-A image and its companion LANDSAT or SEASAT image at each scene are related to the characteristics of the respective imaging systems, and to seasonal or other changes that occurred in the time interval between acquisition of the images.

Ford, J. P.↗
DOE OSTI2023

Meeting Report on the 3rd Chinese American Society for Mass Spectrometry Conference—Advancing Biological and Pharmaceutical Mass Spectrometry

Following the highly successful Chinese American Society for Mass Spectrometry (CASMS) conferences in the previous 2 years, the 3rd CASMS Conference was held virtually on August 28–31, 2023, using the Gather. Town platform to bring together scientists in the MS field. The conference offered a 4-day agenda with a scientific program consisting of two plenary lectures, and 14 parallel symposia in which a total of 70 speakers presented technological innovations and their applications in proteomics and biological MS and metabo-lipidomics and pharmaceutical MS. In addition, 16 invited speakers/panelists presented at two research-focused and three career development workshops. Moreover, 86 posters, 12 lightning talks, 3 sponsored workshops, and 11 exhibitions were presented, from which 9 poster awards and 2 lightning talk awards were selected. Furthermore, the conference featured four young investigator awardees to highlight early-career achievements in MS from our society. In conclusion, the conference provided a unique scientific platform for young scientists (i.e. graduate students, postdocs, and junior faculty/investigators) to present their research, meet with prominent scientists, learn about career development, and job opportunities (http://casms.org).

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

PARIS: Predicting application resilience using machine learning

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. Furthermore, while analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient prediction of concentrating solar power plant productivity using data clustering

Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.

14 SOLAR ENERGY↗