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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 721 records · Page 40

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NASA NTRS1990

A model for the production of cosmogenic nuclides in chondrites

A model is presented for calculating the production rates of cosmic-ray-produced He, Ne, and Ar as well as Be-10, Al-26, and Mn-53 in chondrites of variable size and shape. The predictions of this model are compared with published data for the meteorites ALH78084, St. Severin, and Keyes. The agreement is found to be about 5 percent for the concentrations of Be-10, Ne-21, Ne-22, Ar-38, and Mn-53, and to be better than 1 percent for the Ne-22/Ne-21 ratios. The correlation between P(Be-10)/P(Ne-21) and Ne-22/Ne-21 ratios is experimentally verified over a wide range of irradiation conditions.

Graf, TH.↗

NASA NTRS1990

Flux-difference split scheme for turbulent transport equations

This paper describes a high accuracy upwind method to solve the mean compressible flow equations closed with two-equation turbulence models. A flux-difference splitting scheme is developed to compute the flowfield without introducing oscillations into the solution. A high-order accuracy scheme is used to predict both the mean flow variables and the turbulence variables. Results comparing two different two-equation turbulence models are presented for supersonic flow over a flat plate and a compressible, free shear-layer. The non-oscillatory behavior of the upwind scheme is demonstrated on the free shear-layer.

Morrison, J. H.↗

NASA NTRS1995

Rapid deceleration mode evaluation

Aircraft with flight capability above 1.4 normally have an RPM lockup or similar feature to prevent inlet buzz that would occur at low engine airflows. This RPM lockup has the effect of holding the engine thrust level at the intermediate power (maximum non-afterburning). For aircraft such as military fighters or supersonic transports, the need exists to be able to rapidly slow from supersonic to subsonic speeds. For example, a supersonic transport that experiences a cabin decompression needs to be able to slow/descend rapidly, and this requirement may size the cabin environmental control system. For a fighter, there may be a desire to slow/descend rapidly, and while doing so to minimize fuel usage and engine exhaust temperature. Both of these needs can be aided by achieving the minimum possible overall net propulsive force. As the intermediate power thrust levels of engines increase, it becomes even more difficult to slow rapidly from supersonic speeds. Therefore, a mode of the performance seeking control (PSC) system to minimize overall propulsion system thrust has been developed and tested. The rapid deceleration mode reduces the engine airflow consistent with avoiding inlet buzz. The engine controls are trimmed to minimize the thrust produced by this reduced airflow, and moves the inlet geometry to degrade the inlet performance. As in the case of the other PSC modes, the best overall performance (in this case the least net propulsive force) requires an integrated optimization of inlet, engine, and nozzle variables. This paper presents the predicted and measured results for the supersonic minimum thrust mode, including the overall effects on aircraft deceleration.

Conners, Timothy R.↗

NASA NTRS1996

Model Stellar Atmospheres and Real Stellar Atmospheres and Status of the ATLAS12 Opacity Sampling Program and of New Programs for Rosseland and for Distribution Function Opacity

I discuss errors in theory and in interpreting observations that are produced by the failure to consider resolution in space, time, and energy. I discuss convection in stellar model atmospheres and in stars. Large errors in abundances are possible such as the factor of ten error in the Li abundance for extreme Population II stars. Finally I discuss the variation of microturbulent velocity with depth, effective temperature, gravity, and abundance. These variations must be dealt with in computing models and grids and in any type of photometric calibration. I have also developed a new opacity-sampling version of my model atmosphere program called ATLAS12. It recognizes more than 1000 atomic and molecular species, each in up to 10 isotopic forms. It can treat all ions of the elements up through Zn and the first 5 ions of heavier elements up through Es. The elemental and isotopic abundances are treated as variables with depth. The fluxes predicted by ATLAS12 are not accurate in intermediate or narrow bandpass intervals because the sample size is too small. A special stripped version of the spectrum synthesis program SYNTHE is used to generate the surface flux for the converged model using the line data on CD-ROMs 1 and 15. ATLAS12 can be used to produce improved models for Am and Ap stars. It should be very useful for investigating diffusion effects in atmospheres. It can be used to model exciting stars for H II regions with abundances consistent with those of the H II region. These programs and line files will be distributed on CD-ROMs.

Kurucz, Robert L.↗

NASA NTRS1998

Real Time Land-Surface Hydrologic Modeling Over Continental US

The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the land surface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

Houser, Paul R.↗

NASA NTRS2001

Tropical Rainfall Measuring Mission: Monitoring the Global Tropics for 3 Years and Beyond

The Tropical Rainfall Measuring Mission (TRMM) was launched in November 1997 as a joint U.S.-Japanese mission to advance understanding of the global energy and water cycle by providing distributions of rainfall and latent heating over the global tropics. As a part of NASA's Earth System Enterprise, TRMM seeks to understand the mechanisms through which changes in tropical rainfall influence global circulation. Additionally, a goal is to improve the ability to model these processes in order to predict global circulations and rainfall variability at monthly and longer time scales. Such understanding has implications for assessing climate processes related to El Nino/La Nina and Global Warming. TRMM has also provided unexpected and exciting new knowledge and applications in areas related to hurricane monitoring, lightning, pollution, hydrology, and other areas. This CD-ROM includes a self-contained PowerPoint presentation that provides an overview of TRMM and significant science results; a set of data movies or animation; and listings of current TRMM-related publications in the literature.

Shepherd, Marshall↗

NASA NTRS2002

Structural Response of Compression-Loaded, Tow-Placed, Variable Stiffness Panels

Results of an analytical and experimental study to characterize the structural response of two compression-loaded variable stiffness composite panels are presented and discussed. These variable stiffness panels are advanced composite structures, in which tows are laid down along precise curvilinear paths within each ply and the fiber orientation angle varies continuously throughout each ply. The panels are manufactured from AS4/977-3 graphite-epoxy pre-preg material using an advanced tow placement system. Both variable stiffness panels have the same layup, but one panel has overlapping tow bands and the other panel has a constant-thickness laminate. A baseline cross-ply panel is also analyzed and tested for comparative purposes. Tests performed on the variable stiffness panels show a linear prebuckling load-deflection response, followed by a nonlinear response to failure at loads between 4 and 53 percent greater than the baseline panel failure load. The structural response of the variable stiffness panels is also evaluated using finite element analyses. Nonlinear analyses of the variable stiffness panels are performed which include mechanical and thermal prestresses. Results from analyses that include thermal prestress conditions correlate well with measured variable stiffness panel results. The predicted response of the baseline panel also correlates well with measured results.

Wu, K. Chauncey↗

NASA NTRS2014

LDAS Land Data Assimilation Systems

The land-surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. The characterization of the spatial and temporal variability of water and energy cycles is critical to improve our understanding of the land-surface-atmosphere interaction and the impact of land-surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land-surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes.

data↗

NASA NTRS2016

How Much Global Burned Area Can Be Forecast on Seasonal Time Scales Using Sea Surface Temperatures?

Large-scale sea surface temperature (SST) patterns influence the interannual variability of burned area in many regions by means of climate controls on fuel continuity, amount, and moisture content. Some of the variability in burned area is predictable on seasonal timescales because fuel characteristics respond to the cumulative effects of climate prior to the onset of the fire season. Here we systematically evaluated the degree to which annual burned area from the Global Fire Emissions Database version 4 with small fires (GFED4s) can be predicted using SSTs from 14 different ocean regions. We found that about 48 of global burned area can be forecast with a correlation coefficient that is significant at a p < 0.01 level using a single ocean climate index (OCI) 3 or more months prior to the month of peak burning. Continental regions where burned area had a higher degree of predictability included equatorial Asia, where 92% of the burned area exceeded the correlation threshold, and Central America, where 86% of the burned area exceeded this threshold. Pacific Ocean indices describing the El Nino-Southern Oscillation were more important than indices from other ocean basins, accounting for about 1/3 of the total predictable global burned area. A model that combined two indices from different oceans considerably improved model performance, suggesting that fires in many regions respond to forcing from more than one ocean basin. Using OCI-burned area relationships and a clustering algorithm, we identified 12 hotspot regions in which fires had a consistent response to SST patterns. Annual burned area in these regions can be predicted with moderate confidence levels, suggesting operational forecasts may be possible with the aim of improving ecosystem management.

Chen, Yang↗

NASA NTRSDate not supplied

Polarization Difference - The Scientific Story Behind One Variable and Its Practical Applications

Cloud ice and snow microphysical characteristics play an important role in determining cloud radiative effect. They are also closely connected to the details of surface precipitation characteristics. Ice orientation is one microphysical property that is traditionally believed to have trivial impact on the weather systems/climate, and it is hard to measure from space. In this presentation, I will show how many scientific stories can be told from one variable – the brightness temperature difference between vertically-polarized and horizontally-polarized 166 GHz measurements from the Global Precipitation Measurement Microwave Imager (GPM-GMI). Through scrutinizing collocated CloudSat, GMI and GPM radar observations together with the aid of radiative transfer model simulations, we can not only tell apart the particle size, shape and orientation, but also understand the precipitation regime as well as the life stage of a a precipitation system. In the second half of my talk, I will showcase how this variable can be used for predicting precipitation flags and separating mixed-phase cloud from liquid and ice with the powerful machine learning/artificial intelligence (ML/AI) technique.

cloud ice and snow microphysical characteristics↗

NASA NTRS2023

ENSO and MJO Modulation of U.S. Cloud-to-Ground Lightning Activity

Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.

Lightning↗

DOE OSTI2022

First Measurement Of Charged-current 1 Muon 2 Proton 0 Pion Single Differential Cross-sections On Argon-40 At 0.8 Gev Average Neutrino Energy With The MicroBooNE Detector

Constraining systematic uncertainties that arise from neutrino-nucleon cross-section modeling is critical for upcoming neutrino oscillation experiments. Neutrino interactions that lead to the ejection of 2 particles from the target nucleus, leaving the nucleus with 2 holes, are one cross-section of interest. These instances, known as 2-particle 2-hole (2p2h) states, can be produced either by neutrino interaction modes or from the influence of nuclear physics on the neutrino interaction. While there are many theoretical models that attempt to describe the nature of 2p2h states, very few experimental measurements of this process exist, making it difficult to benchmark theoretical calculations. Furthermore, no high statistic cross-section measurements of 2p2h states exist on $^{40}$Ar, the target nucleus of many current and next generation neutrino oscillation experiments. This analysis aims to address this issue by providing the first high-statistic single differential cross-section me asurements of 2p2h states. Using data collected from the first 3 years of running of the MicroBooNE experiment, we select $\nu_\mu$ charged-current interaction events that produce a single muon, 2 protons, and no pions in the final state (CC1$\mu$2p0$\pi$). The single differential cross-section is then extracted as a function of a variety of physics variables and compared to theoretical predictions from several event generator model sets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017

Despite the rapid growth of online shopping and research interest in the relationship between online and in-store shopping, national-level modeling and investigation of the demand for online shopping with a prediction focus remain limited in the literature. Here, this paper differs from prior work and leverages two recent releases of the U.S. National Household Travel Survey (NHTS) data for 2009 and 2017 to develop machine learning (ML) models, specifically gradient boosting machine (GBM), for predicting household-level online shopping purchases. The NHTS data allow for not only conducting nationwide investigation but also at the level of households, which is more appropriate than at the individual level given the connected consumption and shopping needs of members in a household. We follow a systematic procedure for model development including employing Recursive Feature Elimination algorithm to select input variables (features) in order to reduce the risk of model overfitting and increase model explainability. Among several ML models, GBM is found to yield the best prediction accuracy. Extensive post-modeling investigation is conducted in a comparative manner between 2009 and 2017, including quantifying the importance of each input variable in predicting online shopping demand, and characterizing value-dependent relationships between demand and the input variables. In doing so, two latest advances in machine learning techniques, namely Shapley value-based feature importance and Accumulated Local Effects plots, are adopted to overcome inherent drawbacks of the popular techniques in current ML modeling. The modeling and investigation are performed at the national level, with a number of findings obtained. The models developed and insights gained can be used for online shopping-related freight demand generation and may also be considered for evaluating the potential impact of relevant policies on online shopping demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees

There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive abilityr> 0.9) but almost as well using climate or geography (r> 0.8). In contrast, genomic prediction within populations was poor (r< 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.

Science & Technology - Other Topics↗

DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗

DOE OSTI2020

COVID-19 biomarkers based on respiratory microbiome content

COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1990

PTA en route noise measurements

A long-range advanced turboprop en route noise database was obtained with weather, tracking, and onboard measurements. In-flight noise directivity measurements were made. Data repeatability within a test day was excellent. Day-to-day variability existed and is not completely understood and therefore not predicted. Comparison of a two-dimensional ray-tracing propagation model with the ensemble average ground-measured data was good; however, as stated above, the day-to-day data variability was not completely predicted. Future research will include looking at alternative propagation models. Three-dimensional ray tracing, fast field program, and the parabolic equation are possibilities. The effect of turbulence needs to be accessed.

Willshire, William L., Jr.↗

NASA NTRS1974

Delay in fatigue crack growth

The importance of delay, or retardation in the rate of fatigue crack growth, produced by load interactions in variable amplitude loading on the accurate prediction of fatigue lives of engineering structures has been well recognized for some time. Heretofore, only a few simple loading combinations or spectra have been examined systematically. In this investigation the effects of a broad range of loading variables on delay in fatigue crack growth at room temperature are examined for a mill annealed Ti-6Al-4V alloy. The results are used to estimate crack growth behavior under programmed loads.

Wei, R. P.↗