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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Micromechanical Modeling Efforts for Advanced Composites

Over the past two decades, NASA Lewis Research Center's in-house efforts in analytical modeling for advanced composites have yielded several computational predictive tools. These are, in general, based on simplified micromechanics equations. During the last 3 years, our efforts have been directed primarily toward developing prediction tools for high temperature ceramic matrix composite (CMC's) materials. These materials are being considered for High Speed Research program applications, specifically for combustor liners. In comparison to conventional materials, CMC's offer several advantages: high specific stiffness and strength, and higher toughness and nonbrittle failure in comparison to monolithic ceramics, as well as environmental stability and wear resistance for both roomtemperature and elevated-temperature applications. Under the sponsorship of the High Temperature Engine Materials Program (HITEMP), CMC analytical modeling has resulted in the computational tool Ceramic Matrix Composites Analyzer (CEMCAN).

Source record↗
NASA NTRS2006

Computational Analysis of a Chevron Nozzle Uniquely Tailored for Propulsion Airframe Aeroacoustics

A computational flow field and predicted jet noise source analysis is presented for asymmetrical fan chevrons on a modern separate flow nozzle at take off conditions. The propulsion airframe aeroacoustic asymmetric fan nozzle is designed with an azimuthally varying chevron pattern with longer chevrons close to the pylon. A baseline round nozzle without chevrons and a reference nozzle with azimuthally uniform chevrons are also studied. The intent of the asymmetric fan chevron nozzle was to improve the noise reduction potential by creating a favorable propulsion airframe aeroacoustic interaction effect between the pylon and chevron nozzle. This favorable interaction and improved noise reduction was observed in model scale tests and flight test data and has been reported in other studies. The goal of this study was to identify the fundamental flow and noise source mechanisms. The flow simulation uses the asymptotically steady, compressible Reynolds averaged Navier-Stokes equations on a structured grid. Flow computations are performed using the parallel, multi-block, structured grid code PAB3D. Local noise sources were mapped and integrated computationally using the Jet3D code based upon the Lighthill Acoustic Analogy with anisotropic Reynolds stress modeling. In this study, trends of noise reduction were correctly predicted. Jet3D was also utilized to produce noise source maps that were then correlated to local flow features. The flow studies show that asymmetry of the longer fan chevrons near the pylon work to reduce the strength of the secondary flow induced by the pylon itself, such that the asymmetric merging of the fan and core shear layers is significantly delayed. The effect is to reduce the peak turbulence kinetic energy and shift it downstream, reducing overall noise production. This combined flow and noise prediction approach has yielded considerable understanding of the physics of a fan chevron nozzle designed to include propulsion airframe aeroacoustic interaction effects.

Massey, Steven J.↗
NASA NTRS1994

(abstract) Line Mixing Behavior of Hydrogen-Broadened Ammonia Under Jovian Atmospheric Conditions

Laboratory spectral data reported last year have been used to investigate the line mixing behavior of hydrogen-broadened ammonia inversion lines. The data show that broadening parameters appearing in the modified Ben-Reuven opacity formalism of Berge and Gulkis (1976) cannot maintain constant values over pressure ranges that include low to moderate pressures and high pressures. Also, they cannot change drastically in value, as in the Spilker (1990) revision of the Berge and Gulkis formalism. It has long been recognized that at low pressures, less than about 1 bar of a Jovian atmospheric mixture, a VVW formalism yields more accurate predictions of ammonia opacity than Ben-Reuven formalisms. At higher pressures the Ben-Reuven formalisms are more accurate. Since the Ben-Reuven lineshape collapses to a VVW lineshape in the low pressure limit, this low pressure inaccuracy of the Ben-Reuven formalisms is surprising. By incorporating various behavior, a new formalism is produced that is more accurate than previous formalisms, particularly in the critical 'transition region' from 0.5 to 2 bars, and that can be used without discontinuity from pressures of zero to hundreds of bars. The new formalism will be useful in such applications as interpretation of radio astronomical and radio occultation data on giant planet atmospheres, and radiative transfer modeling of those atmospheres.

inversion lines formalisms radiative transfer mode↗
NASA NTRS1988

Cross-contact chain

A system is provided for use with wafers that include multiple integrated circuits that include two conductive layers in contact at multiple interfaces. Contact chains are formed beside the integrated circuits, each contact chain formed of the same two layers as the circuits, in the form of conductive segments alternating between the upper and lower layers and with the ends of the segments connected in series through interfaces. A current source passes a current through the series-connected segments, by way of a pair of current tabs connected to opposite ends of the series of segments. While the current flows, voltage measurements are taken between each of a plurality of pairs of voltage tabs, the two tabs of each pair connected to opposite ends of an interface that lies along the series-connected segments. A plot of interface conductances on a normal probability chart, enables prediction of the yield of good integrated circuits from the wafer.

Lieneweg, Udo↗
NASA NTRS2015

Simplified Methodology to Estimate the Maximum Liquid Helium (LHe) Cryostat Pressure from a Vacuum Jacket Failure

The aircraft-based Stratospheric Observatory for Infrared Astronomy (SOFIA) is a platform for multiple infrared astronomical observation experiments. These experiments carry sensors cooled to liquid helium temperatures. The liquid helium supply is contained in large (i.e., 10 liters or more) vacuum-insulated dewars. Should the dewar vacuum insulation fail, the inrushing air will condense and freeze on the dewar wall, resulting in a large heat flux on the dewar's contents. The heat flux results in a rise in pressure and the actuation of the dewar pressure relief system. A previous NASA Engineering and Safety Center (NESC) assessment provided recommendations for the wall heat flux that would be expected from a loss of vacuum and detailed an appropriate method to use in calculating the maximum pressure that would occur in a loss of vacuum event. This method involved building a detailed supercritical helium compressible flow thermal/fluid model of the vent stack and exercising the model over the appropriate range of parameters. The experimenters designing science instruments for SOFIA are not experts in compressible supercritical flows and do not generally have access to the thermal/fluid modeling packages that are required to build detailed models of the vent stacks. Therefore, the SOFIA Program engaged the NESC to develop a simplified methodology to estimate the maximum pressure in a liquid helium dewar after the loss of vacuum insulation. The method would allow the university-based science instrument development teams to conservatively determine the cryostat's vent neck sizing during preliminary design of new SOFIA Science Instruments. This report details the development of the simplified method, the method itself, and the limits of its applicability. The simplified methodology provides an estimate of the dewar pressure after a loss of vacuum insulation that can be used for the initial design of the liquid helium dewar vent stacks. However, since it is not an exact tool, final verification of the dewar pressure vessel design requires a complete, detailed real fluid compressible flow model of the vent stack. The wall heat flux resulting from a loss of vacuum insulation increases the dewar pressure, which actuates the pressure relief mechanism and results in high-speed flow through the dewar vent stack. At high pressures, the flow can be choked at the vent stack inlet, at the exit, or at an intermediate transition or restriction. During previous SOFIA analyses, it was observed that there was generally a readily identifiable section of the vent stack that would limit the flow – e.g., a small diameter entrance or an orifice. It was also found that when the supercritical helium was approximated as an ideal gas at the dewar condition, the calculated mass flow rate based on choking at the limiting entrance or transition was less than the mass flow rate calculated using the detailed real fluid model2. Using this lower mass flow rate would yield a conservative prediction of the dewar’s wall heat flux capability. The simplified method of the current work was developed by building on this observation.

Ungar, Eugene K.↗
NASA NTRS2015

Rising Temperatures Reduce Global Wheat Production

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32◦ degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time.

agriculture↗
NASA NTRS2015

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Crespo, Luis G.↗
NASA NTRS2021

Evaluation of 18 Satellite- and Model-Based Soil Moisture Products Using in Situ Measurements From 826 Sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E SWI , SMOS SWI , AMSR2 SWI , and ASCAT SWI , with the L-band-based SMAPL3E SWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI SWI ), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3E SWI . The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Hylke E. Beck↗
NASA NTRS2021

Sputtering of Surfaces by Ion Irradiation: A Comparison of Molecular Dynamics and Binary Collision Approximation Models to Laboratory Measurements

We compare various sputtering simulation methods to experimental results in both the low energy (<1 keV) and high energy (≥1 keV) impact regimes for argon ions impacting a pure copper substrate at normal incidence. Our results indicate that for high energy impacts, both binary collision approximation (BCA) and molecular dynamics methods can be used to generate reasonable predictions for the yield and energy distribution of the sputtered atoms. We also find reasonable agreement between the theoretical and experimental results down to impact energies of 600 eV. However, at 200 eV impact energies, significant discrepancies appear between the experimental and theoretical ejecta energy distributions in the peak position, the width of the energy distribution, and the magnitude of the high energy tail. These discrepancies appear to arise from the experimental results being only for atoms sputtered normal to the substrate surface, whereas the theoretical results are integrated over all 2π solid angles above the surface. Using the BCA code SDTrimSP and limiting the results to only atoms emitted within ±15° of the surface normal brings theory and experiment into reasonable agreement. These results suggest that for low energy impacts, the energy distribution of sputtered atoms is highly dependent on the emission angle of the ejecta.

Solar wind↗
NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗
NASA NTRS2024

HPIC: The Habitable Worlds Observatory Preliminary Input Catalog

The Habitable Worlds Observatory Preliminary Input Catalog (HPIC) is a list of ∼13,000 nearby bright stars that will be potential targets for the Habitable Worlds Observatory (HWO) in its search for Earth-sized planets around Sun-like stars. We construct this target list using the TESS and Gaia DR3 catalogs and develop an automated pipeline to compile stellar measurements and derived astrophysical properties for all stars. We benchmark the stellar properties in the HPIC relative to those of the manually curated ExEP HWO Precursor Science Stars list and find that, for the 164 best targets for exo-Earth direct imaging, our stellar properties are consistent. We demonstrate the utility of the HPIC by using it as an input for yield calculations to predict the science output of various mission designs, including those with larger telescope diameters and those focused on other planet types besides Earth analogs, such as Jupiter-mass planets. The breadth and completeness of the HPIC is essential for accurate HWO mission trade studies, and it will be useful for other exoplanet studies and general astrophysics studying the population of bright nearby stars.

Noah W. Tuchow↗
DOE OSTI2021

BETO 2021 Peer Review - Advanced Catalyst Synthesis and Characterization (ACSC) Project WBS 2.5.4.304; 303; 305

The Advanced Catalyst Synthesis and Characterization (ACSC) project, in close collaboration with the Chemical Catalysis for Bioenergy (ChemCatBio) Consortium enabling projects, CatCost, and the Engineering of Catalyst Scale-Up project, (1) provides fundamental insight into working catalysts leading to actionable recommendations for all of the ChemCatBio catalysis projects, (2) addresses overarching catalysis challenges central to the ChemCatBio Consortium, and (3) adapts and applies new synthesis methodologies and in situ/operando characterization capabilities to meet the evolving needs of the catalysis projects. The outcome is a transition from empirical catalyst development to rational design through the prediction of materials with targeted properties based on advanced characterization combined with computational modeling, and the synthesis of next generation catalysts with predicted structures that yield demonstrated improvements in catalytic performance. In FY18, the ACSC helped to demonstrate the utility of the complete catalyst and process development cycle for dimethyl ether to high-octane gasoline over metal-modified zeolite catalysts for the Upgrading of C1 Building Blocks project, and in FY21 will leverage capabilities, expertise, and computational models established for this effort to target next-generation catalysts for ethanol to distillates for the Upgrading of C2 Intermediates project with enhanced performance in half the time.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

2021 Monthly Rice Production in Chinese Coastal Provinces

This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R 2 value of 0.9991501 with an adjusted R 2 value of 0.9991502 and a ridge regression model with an R 2 value of 0.9865443 with an adjusted R 2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations.

Tubbs, Heidi↗
DOE OSTI2021

Theoretical kinetics predictions for NH 2 + HO 2

Recent modeling studies of NH 3 oxidation, which are motivated by the prospective role of ammonia as a zero-carbon fuel, have indicated significant discrepancies between existing literature mechanisms. In this study high level theoretical kinetics predictions have been obtained for the reaction of NH 2 with HO 2 , which has previously been highlighted as an important reaction with high sensitivity and high uncertainty. The potential energy surface is explored with coupled cluster calculations including large basis sets and high-level corrections to yield high accuracy (~0.2 kcal/mol) estimates of the stationary point energies. Variational transition state theory is used to predict the microcanonical rate constants, which are then incorporated in master equation treatments of the temperature and pressure dependent kinetics. For the radical-radical channels, the microcanonical rates are obtained from variable reaction coordinate transition state theory implementing directly evaluated multireference electronic energies. The analysis yields predictions for the total rate constant as well as the branching to the NH 3 + O 2 , H 2 NO + OH, and HNO + H 2 O channels. Rate constants are also reported for the H 2 NO + OH reaction as they arise naturally from the analysis. The rate constant and branching fraction determined in this work for the NH 2 + HO 2 reaction deviate significantly from values used in most previous modeling studies. The fact that the main product channel is chain terminating, rather than propagating, has strong implications for modeling NH 3 ignition and oxidation, in particular at intermediate temperatures and elevated pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Data-Driven Framework for Predicting the Sorting and Screening Performance of an Integrated Biomass Feedstock Preprocessing System

The characteristics of mechanically sorted and screened lignocellulosic biomass, such as the mass contents of corn stover anatomical fractions (leaves, husks, stalks, cobs, etc.), can be used to calculate the intermediate feedstock quality attributes “yield” and “purity” that indicate the conversion efficiency of biocrude. No prior study has investigated the correlations from the characteristics of raw biomass and preprocessing unit operation parameters to those intermediate feedstock quality attributes. This work presents a data-driven framework for assessing and predicting the intermediate feedstock quality attributes in an integrated biomass feedstock preprocessing system. Our study used corn stover as a typical type of herbaceous biomass because of its abundance in the U.S. It began with data acquisition of moisture content, particle size distribution, and anatomical fractions of the materials after each unit operation in the system. The objective of this preprocessing system is to minimize husks and leaves and maximizing cobs and stalks by mechanically separating the materials into three streams via disc screen and air separator. Prototype neural network models were then developed to evaluate the feasibility of predicting process outcomes based on measurable parameters. It is found that incorporating physical constraints into these prediction models significantly enhances the accuracy of the predicted yield and purity against the ground truth data. The experimental data and model predictions indicate that decreasing throughput increases purity, while higher throughput results in lower purity. Finally, an optimization problem was introduced to search optimal combinations of feed material properties and preprocessing unit operation parameters, as the intermediate feedstock quality attributes – yield and purity, appeared to be competing factors. The study also suggests the continual need to improve the data-driven framework’s predictability by incorporating more accurate physical models to describe the dynamics in the preprocessing units such as the air separator.

09 - BIOMASS FUELS↗
NASA NTRS2014

Impacts of El Nino Southern Oscillation on the Global Yields of Major Crops

The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Nino/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Nino likely improves the global-mean soybean yield by 2.15.4 but appears to change the yields of maize, rice and wheat by -4.3 to +0.8. The global-mean yields of all four crops during La Nina years tend to be below normal (-4.5 to 0.0).Our findings highlight the importance of ENSO to global crop production.

Crops↗
DOE OSTI2024

Analysis of multipacting threshold sensitivity to the random distributions of the secondary electron yield parameters

The way multipacting develops, depends strongly on the secondary emission property of the surface material. The knowledge of secondary electron yield is crucial for accurate prediction of the multipacting threshold. Variations in secondary electron yield parameters from experimental measurements create uncertainty, stemming from handling and surface preparation, and these uncertainties significantly affect multipacting threshold predictions. Despite their significance, the previous studies on the multipacting phenomenon did not adequately address the effect of an assumed random distribution of the secondary emission parameters on the multipacting threshold. Therefore, this paper aims to provide a comprehensive statistical study on how the different random distributions of the secondary emission parameters and, as a result, the uncertainty in the secondary electron yield affect multipacting thresholds. We focus on three commonly used distributions, namely uniform, normal, and truncated normal distributions, to define the uncertainty of random inputs. We use the chaos polynomial expansion method to determine how much each of the random parameters contributes to the multipacting threshold uncertainty. Additionally, we calculate Sobol sensitivity indices to evaluate the impact of the individual parameters or groups of parameters on the model outputs and study how different random distributions of these parameters affected the Sobol index results.

physics↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗