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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Effect of a simulated ice accretion on the aerodynamics of a swept wing

The effect of a simulated glaze ice accretion on the aerodynamic performance of a three-dimensional swept wing is studied experimentally. A semispan wing of effective aspect ratio four was mounted from the sidewall of the UIUC subsonic wind tunnel. The model uses a NACA 0012 airfoil section on a rectangular planform with interchangeable tip and root sections to allow for 0- and 30-degree sweep. A sidewall suction system is used to minmize the tunnel boundary-layer interaction with the model. Surface pressure data from five spanwise stations are compared to earlier data from a similar tunnel. A three-component sidewall balance has been designed, built and used to measure lift, drag and pitching moment on the clean and iced model. The data compare well to the integrated pressure data and to theory on the clean model. In addition, helium-bubble flow visualization has been performed on the iced model and reveals extensive spanwise flow in the separation bubble aft of the upper surface horn. This compares well to the computational results of other researchers. Sidewall suction was found to have no effect on the aerodynamics of the swept wing.

Bragg, M. B.↗
NASA NTRS1991

The structure of the interstellar medium

We have compared theoretical H I models to a large area H I channel map centered at 1 = 205 deg, b = 0 deg. One set of models were calculated in which uncorrelated clouds of H I populate space. Of the cloud models considered, which include 'Spitzer'-type clouds and the McKee-Ostriker formulation, none were able to reproduce the data successfully. In another class of models, holes and holes with shells permeate an otherwise continuous H I medium. Although not entirely successful, these models contain characteristics that are similar to the data. In order to reproduce the amplitude of the H I variations on angular scales of over 1 deg, the volume occupied by ionized gas must be less than 30 percent, in sharp conflict with the models that suggest that the volume of the ISM is dominated by hot gas.

Bregman, Joel N.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-35172-DND

The wide field of view instrument on Earth Radiation Budget Experiment (ERBE) Earth Radiation Budget Satellite (ERBS) took measurements from November 1984 through September 1999. It provides top-of-atmosphere (TOA) reflected shortwave and emitted longwave irradiance at a 10 degree by 10 degree spatial resolution. We have recently re-processed ERBS wide field of view data with algorithms similar to those used in the CERES process (Shrestha et al. 2019). We also calibrated ERBS shortwave and longwave irradiance with CERES-derived irradiances. Combining ERBS and CERES data, we analyze the time series of global mean net TOA irradiance from 1985. The change of TOA net radiation over 30 years derived from ERBS and CERES data is larger than the change derived by Allan et al. (2014). The difference in the net irradiance change from two data sets is, however, within the uncertainty of ocean heating rates derived from ocean temperature measurements in 80?s. We also investigate the spatial distribution of changes in reflected shortwave irradiances derived from ERBS and CERES periods. The spatial distribution of reflected shortwave irradiance changes over the tropics and sub-tropical regions derived from our data set is qualitatively consistent with the result of Norris et al. (2016).

Antonio Viudez-Mora↗
DOE OSTI2023

Environmental sustainability in basic research: a perspective from HECAP+

The climate crisis and the degradation of the world's ecosystems require humanity to take immediate action. The international scientific community has a responsibility to limit the negative environmental impacts of basic research. The HECAP+ communities (High Energy Physics, Cosmology, Astroparticle Physics, and Hadron and Nuclear Physics) make use of common and similar experimental infrastructure, such as accelerators and observatories, and rely similarly on the processing of big data. Our communities therefore face similar challenges to improving the sustainability of our research. This document aims to reflect on the environmental impacts of our work practices and research infrastructure, to highlight best practice, to make recommendations for positive changes, and to identify the opportunities and challenges that such changes present for wider aspects of social responsibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1985

Charge and energy dependence of the residence time of cosmic ray nuclei below 15 GeV/nucleon

The relative abundance of nuclear species measured in cosmic rays at Earth has often been interpreted with the simple leaky box model. For this model to be consistent an essential requirement is that the escape length does not depend on the nuclear species. The discrepancy between escape length values derived from iron secondaries and from the B/C ratio was identified by Garcia-Munoz and his co-workers using a large amount of experimental data. Ormes and Protheroe found a similar trend in the HEAO data although they questioned its significance against uncertainties. They also showed that the change in the B/C ratio values implies a decrease of the residence time of cosmic rays at low energies in conflict with the diffusive convective picture. These conclusions crucially depend on the partial cross section values and their uncertainties. Recently new accurate cross sections of key importance for propagation calculations have been measured. Their statistical uncertainties are often better than 4% and their values significantly different from those previously accepted. Here, these new cross sections are used to compare the observed B/C+O and (Sc to Cr)/Fe ratio to those predicted with the simple leaky box model.

Soutoul, A.↗
NASA NTRS1985

Towards a Global MAP Data Base

Scientific and ecological needs call for the generation of a global data base of measured data from the Earth's atmosphere, similar but complementary to the data of the meteorological service. Right now there is a large gap between these needs and the available financial means. If this prevails, it might be prohibitive for the generation of such a data base. There must be a compromise between the needs and the means. Priorities must be set. Thus, discussions should be started on which data can be or should be stored. Newly generated data for storage in such a data base is recommended to be handled by the World Data Centers (WDCs). This brief report should help to stimulate discussions and ubsequent actions.

Hartmann, G. K.↗
NASA NTRS2020

Bayesian Framework For Bioburden Density Calculations To Perform Planetary Protection Probabilistic Risk Assessment

The planetary protection discipline aims to minimize the microbial contamination on spacecraft to prevent the inadvertent contamination of other planetary bodies, known as forward planetary protection (PP). Planetary protection probabilistic risk assessment (PRA) relies on two core methodologies-the contamination probability event tree analysis and statistical parameter estimation. Planetary protection engineers combine several techniques to estimate the bioburden present on spacecraft components. A direct assay to enumerate CFU (colony forming units) is the preferred methodology, but given a similar processing environment the bioburden present on certain components is inferred using: (1) a NASA defined bioburden estimate based upon the biological cleanliness of the manufacturing/assembly environment or (2) sampled data from a similar spacecraft component. The paper presents an empirical Bayesian framework to systematically treat bioburden estimation and its uncertainties on different levels starting with measurement procedures to combining different components to subsystems and whole spacecraft. It is shown that the Bayesian approach can effectively handle estimations and their uncertainties at different levels and produce a reliable estimate for bioburden to be used to evaluate the probability of contamination.

Seuylemezian, Arman↗
NASA NTRS1994

Analysis of subgrid models using direct and large-eddy simulations of isotropic turbulence

Direct and large eddy simulations of forced and decaying isotropic turbulence have been performed using a pseudospectral and a finite-difference code. Subgrid models that include a one-equation subgrid kinetic energy model with and without a stochastic backscatter forcing term and a new scale similarity model have been analyzed in both Fourier space and physical space. The Fourier space analysis showed that the energy transfer across the cutoff wavenumber k(sub c) is dominated by local interaction. The correlation between the exact and the modeled (by a spectral eddy viscosity) nonlinear terms and the subgrid energy transfer in physical space was found to be quite low. In physical space, a similar correlation analysis was carried out using top hat filtering. Results show that the subgrid stress and the energy flux predicted by the subgrid models correlates very well with the exact data. The scale similarity model showed very high correlation for reasonable grid resolution. However, with decrease in grid resolution, the scale similarity model became more uncorrelated, when compared to the kinetic energy subgrid model. The subgrid models were then used for large-eddy simulations for a range of Reynolds number. It was determined that the dissipation was modeled poorly and that the correlation with the exact results was quite low for all the models. In general, for coarse grid resolution, the scale similarity model consistently showed very low correlation while the kinetic energy model showed a relatively higher correlation. These results suggest that to use the scale similarity model relatively fine grid resolution may be required, whereas, the kinetic energy model could be used even in coarse grid.

Menon, S.↗
NASA NTRS2019

Comparing Level-2 and Level-3 Satellite Ocean Color Retrieval Validation Methodologies

Many ocean color data applications leverage global spatially composited Level-3(L3) satellite data, because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on Level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities.L2 and L3 satellite data retrievals of the photosynthetic pigment chlorophyll-a are compared with a common in situ data set, revealing similar L2 and L3 satellite-to-in situ performance for both MODIS-Aqua and VIIRS-SNPP. This agreement implies that L2 validation results are generally applicable to L3 data. However, uncertainties are introduced during the generation of L3 data from L2 data. L3 data comparisons introduce a wider temporal window between the time of in situ measurement and the time of the satellite observation, which can unintentionally reflect on the quality of the satellite retrieval or algorithm performance. The choice of L3 map projection may introduce additional uncertainty by spatially distorting the true location of the satellite retrievals. Each manipulation of satellite data beyond theinstrument's native spatio temporal reference (L2) reduces the applicability of L2 validation results to higher data processing levels.

Ocean Color↗
NASA NTRS1985

Further comparison of wind tunnel and airplane acoustic data for advanced design high speed propeller models

Comparisons were made between the SR-2 and SR-3 model propeller noise data taken in the NASA 8-by-6 wind tunnel, in the United Technologies Research Center (UTRC) anechoic tunnel, and with boom and fuselage microphones on the NASA Jetstar airplane. Plots of peak blade passage tone noise versus helical tip Mach number generally showed good agreement. The levels of the airplane fuselage data were somewhat lower than the boom data by an approximately uniform value. The curve shapes were similar except for the UTRC data which was flatter than the other sets. This was attributed to the UTRC data being taken at constant power while the other data were taken at constant advance ratio. General curves of the peak blade passage tone versus helical tip Mach number fit through all the data are also presented. Directivity shape comparisons at the cruise condition were similar for the airplane and 8-by-6 tunnel data. The UTRC data peaked farther forward but, when an angle correction was made for the different axial Mach number used in the UTRC tests, the shape was similar to the others. The general agreement of the data from the four configurations enables the formation of a good consensus of the noise from these propellers.

Dittmar, J. H.↗
DOE OSTI2021

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks

Abstract Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PayamDiba/CoNSEPT

Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference.

Dibaeinia, Payam↗
NASA NTRS1993

The effects of AVIRIS atmospheric calibration methodology on identification and quantitative mapping of surface mineralogy, Drum Mountains, Utah

The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) measures reflected light in 224 contiguous spectra bands in the 0.4 to 2.45 micron region of the electromagnetic spectrum. Numerous studies have used these data for mineralogic identification and mapping based on the presence of diagnostic spectral features. Quantitative mapping requires conversion of the AVIRIS data to physical units (usually reflectance) so that analysis results can be compared and validated with field and laboratory measurements. This study evaluated two different AVIRIS calibration techniques to ground reflectance: an empirically-based method and an atmospheric model based method to determine their effects on quantitative scientific analyses. Expert system analysis and linear spectral unmixing were applied to both calibrated data sets to determine the effect of the calibration on the mineral identification and quantitative mapping results. Comparison of the image-map results and image reflectance spectra indicate that the model-based calibrated data can be used with automated mapping techniques to produce accurate maps showing the spatial distribution and abundance of surface mineralogy. This has positive implications for future operational mapping using AVIRIS or similar imaging spectrometer data sets without requiring a priori knowledge.

Kruse, Fred A.↗
DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗
NASA NTRS1993

An evaluation of the National Meteorological Center weekly hindcast of upper-ocean temperature along the eastern Pacific equator in January 1992

The upper-ocean temperature distribution along the Pacific equator from 139 to 103 deg W was observed in January 1992 with temperature profiles recorded from a ship and inferred from an ocean general circulation model calculation involving data assimilation (i.e., hindcast). An El Nino episode was in progress. The l00-m-thick mixed layer depth, the mixed-layer temperature, and the depth-averaged temperature below the thermocline were similar in both data products. Considerable differences occurred in the representation of the 15-25 C thermocline, such as the depth-averaged temperatures above and below the 20 C isotherm, the east-west slope of the 20 C isotherm, and a 1000-km-wide depression. The longitudinal-averaged root-mean-square difference between the hindcast and observed depths of the center of the thermocline was 17 m. Most of the disparities could be attributed to a high wavenumber transient event that the model-based assimilation system was not intended to resolve.

Halpern, David↗
NASA NTRS2008

Using Airborne High Spectral Resolution Lidar Data to Evaluate Combined Active Plus Passive Retrievals of Aerosol Extinction Profiles

Aerosol extinction profiles are derived from backscatter data by constraining the retrieval with column aerosol optical thickness (AOT), for example from coincident MODIS observations and without reliance on a priori assumptions about aerosol type or optical properties. The backscatter data were acquired with the NASA Langley High Spectral Resolution Lidar (HSRL). The HSRL also simultaneously measures extinction independently, thereby providing an ideal data set for evaluating the constrained retrieval of extinction from backscatter. We will show constrained extinction retrievals using various sources of column AOT, and examine comparisons with the HSRL extinction measurements and with a similar retrieval using data from the CALIOP lidar on the CALIPSO satellite.

Burton, S. P.↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2021

Nitromethane Decomposition via Automated Reaction Discovery and an Ab Initio Corrected Kinetic Model

In the explore the systematic construction of kinetic models from in silico reaction data for the decomposition of nitromethane. Our models are constructed in a computationally affordable manner by using reactions discovered through accelerated molecular dynamics simulations using the ReaxFF reactive force field. The reaction paths are then optimized to determine reaction rate parameters. We introduce a reaction barrier correction scheme that combines accurate thermochemical data from density functional theory with ReaxFF minimal energy paths. We validate our models across different thermodynamic regimes, showing predictions of gas phase CO and NO concentrations and high-pressure induction times that are similar to experimental data. The kinetic models are analyzed to find fundamental decomposition reactions in different thermodynamic regimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗