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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

SQL and NoSQL Databases for Cyber Physical Production Systems in Internet of Things for Manufacturing (IoTfM)

Abstract In this paper, the design and performance differences between Relational Database Management Systems (RDBMS) and NoSQL Database Systems are examined, with attention to their applicability for real-world Internet of Things for manufacturing (IoTfM) data. While previous work has extensively compared SQL and NoSQL for both generalized and IoT uses, this work specifically examines the tradeoffs and performance differences for manufacturing applications by using a high-fidelity data set collected from a large US manufacturing firm. Growing an IoT system beyond the pilot stage requires scalable data storage; this work seeks to determine the impact of selected database systems on data write performance at scale. Payload size and message frequency were used as the primary characteristics to maintain model fidelity in simulated clients. As the number of simulated asset clients grow, the data write latency was calculated to determine how both database systems’ performance were affected. To isolate the RDBMS and NoSQL differences, a cloud environment was created using Amazon Web Services (AWS) with two identical data ingestion pipelines: writing data to an RDMBS (1) using AWS Aurora MySQL, and (2) using AWS DynamoDB NoSQL. The findings may provide guidance for further experimentation in large-scale manufacturing IoT implementations.

Gamero, David↗
DOE OSTI2014

Data-Model Needs for Belowground Ecology. A Summary Report from the Terrestrial Ecosystem Science (TES) Mini-Workshop, May 8, 2014

Terrestrial life on Earth depends on belowground cycling and storage of a significant portion of the planet’s biologically-active carbon. The interplay among organic inputs by primary producers, soil organic matter stabilization, and assimilation and mineralization by soil organisms drives belowground ecology. Understanding belowground carbon dynamics, including the stability and vulnerability of soil organic carbon stocks, is necessary to project the responses and feedbacks of terrestrial ecosystems to a changing climate. Although conceptually understood, belowground hydrobiogeochemical systems are not well characterized with quantitative and mechanistic representations of critical functions. As a result, these systems are inadequately represented in modeling efforts, limiting our ability to understand and project terrestrial carbon source/sink relationships at various temporal and spatial scales. The belowground science community lacks robust databases and data libraries against which model sensitivities and performance can be tested and enhanced. Improved understanding of belowground ecosystems, including biotic and abiotic interactions, is needed to ensure that model structures include and provide adequate representations of critical belowground processes, functions, and dynamics. A one-day workshop brought together experimental and modeling representatives from the four Department of Energy (DOE) National Laboratories where the Office of Biological and Environmental Research (BER) Terrestrial Ecosystem Science (TES) program has investments in belowground ecology. The group focused on identifying and characterizing (1) critical experimental and observational datasets for testing and developing existing and future belowground-process models and land models; (2) the limitations and needs for modeling belowground processes within ecosystem models; and (3) key areas for leveraging current research activities across the National Laboratories and BER-funded projects. Discussion results are organized in this report as: (I) over-arching data requirements for advancing understanding of belowground ecosystems; (II) efforts that could rapidly improve understanding and model representations of belowground components of terrestrial ecosystems; and (III) longer-term research needs that would benefit from greater and more sustained support.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Novel Framework for Performance Evaluation and Design Optimization of PCM Embedded Heat Exchangers for the Built Environment

This research sheds light on the performance evaluation and design optimization of PCM-HXs for the built environment, addressing several barriers to practical issues to PCM-HX commercialization such as modeling aspects (i.e., modeling expertise and computational / time investment, etc.), manufacturing aspects (i.e., at-scale manufacturing, cost assessments, etc.) and experimental performance assessment (i.e., reliable experimental data, assessment of multiple PCM-working fluid combinations, etc.). We present a novel, comprehensive, and experimentally-validated design optimization framework for PCM-HXs capable of simulating any PCM-HX geometry with reasonable accuracy and significant computational time savings when compared to traditional CFD-based design practices. The framework was validated for a wide range of PCM-HX configurations, including a design optimization for a domestic hot water heater application where TES partially replaces electrical heating input. The resulting PCM-HXs were found to deliver 34-68% of the total daily hot water supply with only 5-10% package volume increase from the water heater, thus within U.S. DOE targets for TES systems. To identify the most promising HXs for PCM applications, first-order geometry and cost analyses were conducted based on off-the-shelf HX products. As part of this work, 9 PCM-HX prototypes were manufactured using additive and conventional manufacturing methods. Detailed economy-of-scale assessments were conducted for the most promising PCM-HXs and were found to have a good outlook for the next 5-10 years. The PCM-HX design optimization framework was validated through comprehensive in-house experimental testing using newly-developed PCM-to-fluid test facilities. In total,10 total in-house component-level experiments were conducted using these prototypes, including 9 with water and 1 with refrigerant (R410A) as the working fluid. It was found that the framework can successfully predict experimental thermal-hydraulic performance within ±10-20% the first time without manual design changes, eliminating the need for time-consuming and expensive prototyping efforts as part of the design process. As part of this work, a publicly-available PCM web tool was released which includes a PCM property database (531 PCMs) and PCM-HX modeling tool to assist the design community on common PCM-HX use-cases, e.g., single/multiple flow path(s) fluid-to-PCM and air-to-fluid-to-PCM configurations (https://ceeeweb.umd.edu/pcmapp/). This work will accelerate the design and time to market for next generation PCM-HXs.

25 ENERGY STORAGE↗
NASA NTRS1998

Fiber Supported Droplet Combustion-2 (FSDC-2)

Experimental results for the burning characteristics of fiber supported, liquid droplets in ambient Shuttle cabin air (21% oxygen, 1 bar pressure) were obtained from the Glove Box Facility aboard the STS-94/MSL-1 mission using the Fiber Supported Droplet Combustion - 2 (FSDC-2) apparatus. The combustion of individual droplets of methanol/water mixtures, ethanol, ethanol/water azeotrope, n-heptane, n-decane, and n-heptane/n-hexadecane mixtures were studied in quiescent air. The effects of low velocity, laminar gas phase forced convection on the combustion of individual droplets of n-heptane and n-decane were investigated and interactions of two droplet-arrays of n-heptane and n-decane droplets were also studied with and without gas phase convective flow. Initial diameters ranging from about 2mm to over 6mm were burned on 80-100 micron silicon fibers. In addition to phenomenological observations, quantitative data were obtained in the form of backlit images of the burning droplets, overall flame images, and radiometric combustion emission measurements as a function of the burning time in each experiment. In all, 124 of the 129 attempted experiments (or about twice the number of experiments originally planned for the STS-94/MSL-1 mission) were conducted successfully. The experimental results contribute new observations on the combustion properties of pure alkanes, binary alkane mixtures, and simple alcohols for droplet sizes not studied previously, including measurements on individual droplets and two-droplet arrays, inclusive of the effects of forced gas phase convection. New phenomena characterized experimentally for the first time include radiative extinction of droplet burning for alkanes and the "twin effect" which occurs as a result of interactions during the combustion of two-droplet arrays. Numerical modeling of isolated droplet combustion phenomenon has been conducted for methanol/water mixtures, n-heptane, and n-heptane/n-hexadecane mixtures, and results compare quantitatively with those found experimentally for methanol/water mixtures. Initial computational results qualitatively predict experimental results obtained for isolated n-heptane and n-heptane/n-hexadecane droplet combustion, although the effects of sooting are not yet included in the modeling work. Numerical modeling of ethanol and ethanol/water droplet burning is under development. Considerable data remain to be fully analyzed and will provide a large database for comparisons with further numerical and analytical modeling and development of future free droplet experiments aboard space platforms.

Colantonio, Renato↗
NASA NTRS2023

Critical Heat Flux of Liquid Hydrogen, Liquid Methane, and Liquid Oxygen: A Review of Available Data and Predictive Tools

Available experimental data dealing with critical heat flux (CHF) of liquid hydrogen (LH2), liquid methane (LCH4), and liquid oxygen (LO2) in pool and flow boiling are compiled. The compiled data are compared with widely used correlations. Experimental pool boiling CHF data for the aforementioned cryogens are scarce. Based on only 25 data points found in five independent sources, the correlation of Sun and Lienhard (1970) is recommended for predicting the pool CHF of LH2. Only two experiments with useful CHF data for the pool boiling of LCH4 could be found. Four different correlations including the correlation of Lurie and Noyes (1964) can predict the pool boiling CHF of LCH4 within a factor of two for more than 70% of the data. Furthermore, based on the 19 data points taken from only two available sources, the correlation of Sun and Lienhard (1970) is recommended for the prediction of pool CHF of LO2. Flow boiling CHF data for LH2 could be found in seven experimental studies, five of them from the same source. Based on the 91 data points, it is suggested that the correlation of Katto and Ohno (1984) be used to predict the flow CHF of LH2. No useful data could be found for flow boiling CHF of LCH4 or LO2. The available databases for flow boiling of LCH4 and LO2 are generally deficient in all boiling regimes. This deficiency is particularly serious with respect to flow boiling.

Multi-Phase Flow↗
DOE OSTI2014

Metaproteomics: extracting and mining proteome information to characterize metabolic activities in microbial communities

Contemporary microbial ecology studies usually employ one or more omics approaches to investigate the structure and function of microbial communities. Among these, metaproteomics aims to characterize the metabolic activities of the microbial membership, providing a direct link between the genetic potential and functional metabolism. The successful deployment of metaproteomics research depends on the integration of high-quality experimental and bioinformatic techniques for uncovering the metabolic activities of a microbial community in a way that is complementary to other meta-omic approaches. The essential, quality-defining informatics steps in metaproteomics investigations are: (1) construction of the metagenome, (2) functional annotation of predicted protein-coding genes, (3) protein database searching, (4) protein inference, and (5) extraction of metabolic information. In this article, we provide an overview of current bioinformatic approaches and software implementations in metaproteome studies in order to highlight the key considerations needed for successful implementation of this powerful community-biology tool.

mass spectrometry↗
DOE OSTI2025

Machine learning-assisted design of metal–organic frameworks for hydrogen storage: A high-throughput screening and experimental approach

Various theoretical approaches, including big data and high-throughput screening techniques, have been explored in developing new materials due to their significant potential time-saving advantages. However, it remains a significant challenge to experimentally realize new materials that are predicted. In this study, we propose a novel materials design strategy that utilizes machine-learning (ML) techniques to predict new porous materials that show promise for hydrogen storage and are likely to be feasible to synthesize. By leveraging ML techniques and metal–organic framework (MOF) databases, we are able to predict the synthesizability of MOF structures. This is evidenced by the successful synthesis of a new vanadium-based MOF that exhibits excellent performance for cryogenic H 2 storage. Notably, the total gravimetric and volumetric H 2 uptakes are as high as 9.0 wt% and 50.0 g/L at 77 K and 150 bar. This ML-assisted materials design offers an efficient and promising approach for developing hydrogen storage materials.

08 HYDROGEN↗
DOE OSTI2026

Data mining the missing ordered phases of Li/Na metal oxides

Data-driven discovery of Li-ion and Na-ion battery materials has been pioneered by generic materials data platforms such as the Materials Project. After decades of progress, it is timely to ask whether there remain underexplored compositional spaces. Here, in this work, we present a systematic data-mining effort to uncover missing ordered binary, ternary and quaternary Li/Na-containing metal oxides using high-throughput density functional theory (DFT). Building on 19,120 stable and metastable oxides entries from the Materials Project, we performed 13,245 additional calculations through isovalent substitutions of known ground states, experimentally reported compounds, and specific prototype structures. Our study identifies 36 new ground states within the GGA/GGA + U convex hull and 45 within the r 2 SCAN convex hull. Additionally, we identified 840 metastable compounds from GGA/GGA + U and 979 from r 2 SCAN that are absent in the present Materials Project databases. Moreover, we have tripled the metastable materials in compositional spaces with a molar ratio of cation/anion >1, highlighting the overlooked opportunities in this compositional space.

25 ENERGY STORAGE↗
DOE OSTI2021

Predictive Synthesis

Current solid state synthesis intrinsically involves a multidimensional space which is challenging to parametrize and predict. The diversity of extended structures comes from the diversity of basic properties of elements of the Periodic system which may exhibit a variety of bonding modes. The fundamental challenges of prediction of the preparative outcome are further complicated by practical synthetic issues. Current development in computational and experimental methods calls for collaborative efforts to make solid state synthesis more predictable. Overall, this Perspective discusses several steps in this direction, including integration of predictions of synthetic conditions with new structure predictions, widespread in situ studies to obtain a panoramic view of the reaction mechanism, and the creation of a synthetic database to properly document all synthetic efforts, including the unsuccessful ones.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed. While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Here, starting with 70 150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.

08 HYDROGEN↗
DOE OSTI2025

Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model

The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties, such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but not limited to optical contrast, Raman spectroscopy, nonlinear optical spectroscopy, near-field optical imaging, and hyperspectral imaging, facilitate the measurement of 2D material thickness, comprehensive data for many materials remain elusive. Over the past decade, the exponential proliferation of 2D materials and their heterostructures has outstripped the capabilities of conventional experimental and computational approaches. In this evolving landscape, machine learning (ML) has emerged as an indispensable tool, offering a scalable approach to augment these traditional methodologies. Addressing the critical gap, we introduce THICK2D—Thickness Hierarchy Inference and Calculation Kit for 2D Materials. This Python-based computational framework harnesses an autogenerated thickness database, developed using large language models, and advanced ML algorithms to facilitate the rapid and scalable estimation of material thickness, relying solely on crystallographic data. To demonstrate the utility and robustness of THICK2D, we successfully used the toolkit to predict the thickness of more than 8000 2D-based materials, sourced from two extensive 2D materials databases. THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
DOE OSTI2021

Computational thermodynamic study of SiC chemical vapor deposition from MTS-H 2

This study focuses on the computational thermodynamic analysis of the chemical vapor deposition (CVD) of SiC from the methyltrichlorosilane-hydrogen (MTS-H 2 ) using up-to-date thermodynamic databases. High-resolution computation has been performed with the fine intervals of temperature and pressure at the various H 2 /MTS ratios of interest to systematically investigate the deposition condition range (800 to 1600°C, 0 to 26 664 Pa, and H 2 /MTS ratios of 0.1 to 100) to guide experimental exploration. The influence of deposition parameters on the compositions and phase stabilities of the deposit and gas phase pertinent to vapor processing is elucidated. Low pressure and medium temperatures (1000 to 1400°C) are beneficial to reaching a higher SiC deposition efficiency and provide an optimal window for preparing a high-purity (>99 wt.% SiC) deposit. This optimal processing window expands significantly with an increasing H 2 /MTS ratio (<20). These results are supported by a number of previous theoretical and experimental observations. The mass fraction of SiC in deposit is proposed as an additional perspective to understand the discrepancy between thermodynamic calculation and experimental observation of pure CVD SiC at low H 2 /MTS ratios.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Recent progress in NASA Langley textile reinforced composites program

The NASA LaRC is conducting and sponsoring research to explore the benefits of textile reinforced composites for civil transport aircraft primary structures. The objective of this program is to develop and demonstrate the potential of affordable textile reinforced composite materials to meet design properties and damage tolerance requirements of advanced aircraft structural concepts. In addition to in-house research, the program was recently expanded to include major participation by the aircraft industry and aerospace textile companies. The major program elements include development of textile preforms, processing science, mechanics of materials, experimental characterization of materials, and development and evaluation of textile reinforced composite structural elements and subcomponents. The NASA Langley in-house focus is as follows: development of a science-based understanding of resin transfer molding (RTM), development of powder-coated towpreg processes, analysis methodology, and development of a performance database on textile reinforced composites. The focus of the textile industry participation is on development of multidirectional, damage-tolerant preforms, and the aircraft industry participation is in the areas of design, fabrication and testing of textile reinforced composite structural elements and subcomponents. Textile processes such as 3D weaving, 2D and 3D braiding, and knitting/stitching are being compared with conventional laminated tape processes for improved damage tolerance. Through-the-thickness reinforcements offer significant damage tolerance improvements. However, these gains must be weighed against potential loss in in-plane properties such as strength and stiffness. Analytical trade studies are underway to establish design guidelines for the application of textile material forms to meet specific loading requirements. Fabrication and testing of large structural components are required to establish the full potential of textile reinforced composite materials.

Dexter, H. Benson↗
NASA NTRS2003

Evaluation of Flush-Mounted, S-Duct Inlets with Large Amounts of Boundary Layer Ingestion

A new high Reynolds number test capability for boundary layer ingesting inlets has been developed for the NASA Langley Research Center 0.3-Meter Transonic Cryogenic Tunnel. Using this new capability, an experimental investigation of four S-duct inlet configurations with large amounts of boundary layer ingestion (nominal boundary layer thickness of about 40% of inlet height) was conducted at realistic operating conditions (high subsonic Mach numbers and full-scale Reynolds numbers). The objectives of this investigation were to 1) provide a database for CFD tool validation on boundary layer ingesting inlets operating at realistic conditions and 2) provide a baseline inlet for future inlet flow-control studies. Tests were conducted at Mach numbers from 0.25 to 0.83, Reynolds numbers (based on duct exit diameter) from 5.1 million to a full-scale value of 13.9 million, and inlet mass-flow ratios from 0.39 to 1.58 depending on Mach number. Results of this investigation indicate that inlet pressure recovery generally decreased and inlet distortion generally increased with increasing Mach number. Except at low Mach numbers, increasing inlet mass-flow increased pressure recovery and increased distortion. Increasing the amount of boundary layer ingestion (by decreasing inlet throat height) or ingesting a boundary layer with a distorted (adverse) profile decreased pressure recovery and increased distortion. Finally, increasing Reynolds number had almost no effect on inlet distortion but increased inlet recovery by about one-half percent at a Mach number near cruise.

Berrier, Bobby L.↗
DOE OSTI2018

Information Files of 238 U(n,f)/ 235 U(n,f) NIFFTE fissionTPC Cross-sections for the Neutron Data Standards Project

This report documents input files, output files and underlying assumptions for an uncertainty estimate for nuclear data evaluations purposes of the normalized 238 U/ 235 U neutron-induced fission cross-section ratios, 238 U(n,f)/ 235 U(n,f), measured by the NIFFTE fission Time Projection Chamber (fissionTPC). This uncertainty estimate and the resulting files were based on information provided by fissionTPC experimentalists, R.J.Casperson, N.S. Bowden, L. Snyder and K.T. Schmitt, and an EXFOR file compiled by N. Otuka. These files were generated for the Neutron Data Standards project coordinated by the IAEA in order to simplify the incorporation of 238 U(n,f)/ 235 U(n,f) fissionTPC data into the standards database.

238 U(n,f)/ 235 U(n,f)↗
DOE OSTI2024

Multi‐Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications

Abstract Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi‐objective search is developed, combined with first‐principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state‐of‐the‐art post‐Cu material, and also semi‐global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Chemistry↗
DOE OSTI2022

Ab initio quantum scattering calculations for the CO–O 2 system and a new CO–O 2 potential energy surface: O 2 and air broadening of the R(0) line in CO

We present ab initio calculations of the collisional broadening of the R(0) pure rotational line in CO (at 115 GHz) perturbed by O 2 . Our calculations are done in a fully quantum way by solving close-coupling quantum-scattering equations without any approximations. We also report a new, highly accurate CO–O 2 potential energy surface on which we did the quantum-scattering calculations. The calculated collisional broadening agrees with the available experimental data in a wide temperature range. The calculated collisional shift is negligible compared to the broadening, which is also consistent with the experimental data. We combine this result with our previous calculations for the same line in CO perturbed by N 2 [Józwiak et al., J. Chem. Phys. 154, 054314 (2021)]; the obtained air-perturbed broadening of the R(0) pure rotational line in CO and its temperature dependence perfectly agree with the HITRAN database. This result constitutes an important step toward developing a methodology for providing accurate ab initio reference data on spectroscopic collisional line-shape parameters for molecular systems relevant to the Earth’s atmosphere and for populating spectroscopic line-by-line databases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗