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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Terra and Aqua MODIS Intercomparison Using LEO-GEO Double Difference Method

The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites havesuccessfully operated since their launch in 1999 and in 2002, providing more than 18 and 16 years ofcontinuous global observations, respectively. The inter-comparison between the two MODIS instruments can bevery supportive for the instrument calibration and uncertainty assessment. Aqua and Terra MODIS have almostidentical relative spectral response, spatial resolution, and dynamic range for each band. Therefore, a sitedependent correction for a sensor spectral band pair is not necessary for their comparison. However, Terra is inthe morning orbit with an equator crossing time of 10:30 am, and Aqua is in the afternoon orbit with equatorcrossing time of 1:30 pm. Consequently, there is a dearth of simultaneous nadir overpasses (SNOs)between the two satellites. Major challenges in cross-sensor comparison of instruments on different satellitesinclude differences in observation time, solar angle, and view angle over selected pseudo-invariant sites.In this work, the inter-comparisons of thermal emissive bands are performed over a pseudo-invariant target,using the observations from a sensor onboard a geostationary satellite as a bridge. Himawari8 was launched onOctober 7, 2014. The Advanced Himawari Imager (AHI) onboard Himawari8 can be used as a reference tobridge the comparison between Terra and Aqua MODIS. AHI has 16 channels; with spatial resolutions from 0.5km to 2 km at nadir and produces a full disk observations every 10 minutes. The band spectral coveragematchup, comparable spatial resolution and near-simultaneous observation between MODIS and AHI providefeasibility to implement a double difference method. This comparison method minimizes the impact of thedifference in observation time and solar angle. The comparison results will be used as an assessment for MODISinstrument calibration and will be helpful for future enhancement of the L1B product.

Himawari8↗
NASA NTRSDate not supplied

A Comparison of Aircraft Flyover Auralizations by the Aircraft Noise Simulation Working Group

The Aircraft Noise Simulation Working Group (ANSWr), comprised of NASA, DLR, and ONERA, recently completed an analysis campaign to compare aircraft noise simulation tools, establish guidelines for noise prediction, and launch activities to assess uncertainties associated with the simulation. The campaign included the analyses of two DLR conceptual aircraft, a reference tube-and-wing aircraft, and a low noise aircraft with engines mounted above the fuselage-wing-junction. While the total predicted noise for each of the concepts compared favorably among analyses at the peak level, significant differences were noted at the component level. This paper aims to further that effort by auralizing aircraft flyover noise associated with those predictions. Comparisons are made among the NASA, DLR/Empa, and ONERA generated sounds to determine how differences between the system noise prediction and auralization methods result in changes to the auralized sound.

Stephen A Rizzi↗
NASA NTRS2020

Version 2 Ozone Monitoring Instrument SO2 Product (OMSO2 V2): New Anthropogenic SO2 Vertical Column Density Dataset

The Ozone Monitoring Instrument (OMI) has been providing global observations of SO2 pollution since 2004. Here we introduce the new anthropogenic SO2 vertical column density (VCD) dataset in the version 2 OMI SO2 product (OMSO2 V2). As with the previous version (OMSO2 V1.3), the new dataset is generated with an algorithm based on principal component analysis of OMI radiances, but features several updates. The most important among those is the use of expanded lookup tables and model a priori profiles to estimate SO2 Jacobians for individual OMI pixels, in order to better characterize pixel-to-pixel variations in SO2 sensitivity, including over snow and ice. Additionally, new data screening and spectral fitting schemes have been implemented to improve the quality of the spectral fit. As compared with the planetary boundary layer SO2 dataset in OMSO2 V1.3, the new dataset has substantially better data quality, especially over areas that are relatively clean or affected by the south Atlantic anomaly. The updated retrievals over snow/ice yield more realistic seasonal changes in SO2 at high latitudes and offer enhanced sensitivity to sources during wintertime. An error analysis has been conducted to assess uncertainties in SO2 VCDs from both the spectral fit and Jacobian calculations. The uncertainties from spectral fitting are reflected in SO2 slant column densities (SCDs) and largely depend on the signal-to-noise ratio of the measured radiances, as implied by the generally smaller SCD uncertainties over clouds or for smaller solar zenith angles. The SCD uncertainties for individual pixels are estimated to be~0.15-0.3 DU (Dobson Units) between ~40°S and ~40°N and to be~0.2-0.5 DU at higher latitudes. The uncertainties from the Jacobians are approximately ~50-100% over polluted areas, and primarily attributed to errors in SO2 a priori profiles and cloud pressures, as well as the lack of explicit treatment for aerosols. Finally, the daily mean and median SCDs over the presumably SO2-free equatorial East Pacific have increased by only~0.0035 DU and ~0.003 DU respectively over the entire 15-year OMI record; while the standard deviation of SCDs has grown by only~0.02 DU or ~10%. Such remarkable long-term stability makes the new dataset particularly suitable for detecting regional changes in SO2 pollution.

OMI, SO2, Remote Sensing↗
NASA NTRSDate not supplied

Data Simulation Using VLIDORT and PCRTM and its Use for Radiometric Calibration of Hyperspectral UV-NIR-SWIR Sensors

Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), a multiple-scattering multi-layer discrete ordinate scattering codes, coupled with a fast principal component (PC) radiative transfer model (PCRTM), we can simulate the radiation fields for hyperspectral UV-NIR-SWIR sensors from 250nm to 2500nm with a very high speed. Since the accurate gas absorptions, molecules and aerosol/cloud particle scatterings, and the surface reflectance have been considered in these simulations, these model data can be used for satellite or airborne sensor design, characterization, or end-to-end system level uncertainty assessment. Comparison of the modelled data with measured results has been a general practice for operational satellite sensor calibrations, such as the Ozone Mapping and Profiler Suite (OMPS) onboard on SNPP and NOAA-20. This presentation will give more details of this simulation system and models. Some examples of the generation of simulations data for CLARREO PathFinder (CPF) and the Surface Biology and Geology (SBG), as well as some examples to use these data for CPF and OMPS radiometric calibration will be presented.

Xiaozhen Xiong↗
DOE OSTI2022

Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

Here, we use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the 'mean estimation', whereas its standard deviation can be used to construct 'confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban flow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global field reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-field state estimation from sectional data with two-dimensional CNN. We find that SWAG can obtain physically-interpretable confidence-interval estimates from the perspective of epistemic uncertainty. This capability supports its use for a wide range of problems in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Assessment of Measurement Uncertainties in the Jupiter High-240 Experiment

The Jupiter High-240 experiment performed in May of 2019 was previously discussed as a variant of the original Jupiter experiment incorporating plutonium metal alloy fuel plates with higher 240Pu content and lead plates, using both a reference configuration and a second configuration where eight lead plates were replaced with aluminum to simulate voiding. Measurements were recorded for experiment period, the “pressure” of the Comet ram upon closure for each near-critical measurement, and temperature. The experiment reactor period is the time it would take to increase the neutron population by a factor of e. For this experiment, the copper reflectors and upper third of the fuel sits upon a support structure with the lower fuel arrays raised up into the center of the reflectors using a ram (see Fig. 1). The recorded logbook temperature for each measurement corresponds to a resistance temperature detector (RTD) located at the top center of the upper fuel array. This paper summarizes the evaluated uncertainties for the Jupiter High 240 experiment as contributed via the recorded measurements and nuclear data and their assessed impact upon the computation of system reactivity and eigenvalue.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2016

Reducing Multisensor Satellite Monthly Mean Aerosol Optical Depth Uncertainty: 1. Objective Assessment of Current AERONET Locations

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

aerosol optical depth↗
DOE OSTI2023

Towards Integrating Data Quality Assessments and Radiometer Uncertainty for Determining the Expanded Uncertainty of Three-Component Solar Radiation Measurements

Accurate solar irradiance data are fundamental for determining the design and performance characteristics of photovoltaic systems. The uncertainty of solar irradiance measurements depends on many factors including radiometer design, calibration, installation, maintenance, and operational environment. The key contributors to this uncertainty can be classified as the measurement uncertainty of a particular radiometer and the operational uncertainty determined for the time of measurement. Radiometer measurement uncertainty estimates (U R ) can be based on well-established methods used as part of the radiometer calibration process. Estimates of operational uncertainties (U o ) require consideration of additional site-specific factors that affect data quality. A method is needed for establishing the accuracy of solar irradiance data by integrating an existing data quality process and measurement uncertainty estimates for specific radiometers. An algorithm has been developed to integrate data quality analyses and measurement uncertainty estimates for three-component solar irradiance data: global horizontal (total hemispheric) irradiance, direct normal (beam) irradiance, and diffuse horizontal (sky) irradiance collected at one- to 60-minute intervals. The algorithm has been tested using one-minute irradiance measurements. The goal of the project is to distribute a user-friendly software package based on the new algorithm.

data integrity↗
DOE OSTI2023

Uncertainty quantification for equations of state: copper as an example

Equations of state are essential for providing a fundamental description of materials properties in thermodynamic equilibrium and are used to provide closure relations for hydrodynamics simulations. Generally, equations of state rely on simple physics-based parameterized materials models to inform on the free energy of a material through out a given thermodynamic state space. Historically the parameters of these models have been tuned by hand to fit various experimental data. However, modern optimization and uncertainty quantification techniques allow us to quickly test thousands of parameter combinations and obtain meaningful uncertainty estimates on the parameters, opening opportunities for assessing systematic uncertainties in experiments, assessing model adequacy, and more. In this report, we use Bayesian inference to fit the solid (fcc) equation of state of copper. We focus on fitting five different experimental datasets, including the isobaric density, isobaric heat capacity, room temperature isotherm, principal isentrope, and principal Hugoniot. We fit all five data types simultaneously, and then explore the extent to which combinations of 2 subsets of the 5 datasets can constrain the EOS parameters, as compared to the fit to all 5. This information is useful for investigating the extent to which different datasets can con strain EOS models and thereby help guide experimental investigations in order to best constrain the EOS. We also discuss ways that the methodologies can be used to investigate systematic discrepancies between experiments, as well as how the methods can be used to assess model uncertainty. The framework we develop is general, in that it can be used with a variety of optimization or uncertainty quantification techniques and with a variety of data sources, including both experimental and ab-inito data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Application of Probability Methods to Assess Crash Modeling Uncertainty

Full-scale aircraft crash simulations performed with nonlinear, transient dynamic, finite element codes can incorporate structural complexities such as: geometrically accurate models; human occupant models; and advanced material models to include nonlinear stress-strain behaviors, and material failure. Validation of these crash simulations is difficult due to a lack of sufficient information to adequately determine the uncertainty in the experimental data and the appropriateness of modeling assumptions. This paper evaluates probabilistic approaches to quantify the effects of finite element modeling assumptions on the predicted responses. The vertical drop test of a Fokker F28 fuselage section will be the focus of this paper. The results of a probabilistic analysis using finite element simulations will be compared with experimental data.

Lyle, Karen H.↗
NASA NTRS2007

Application of Probability Methods to Assess Crash Modeling Uncertainty

Full-scale aircraft crash simulations performed with nonlinear, transient dynamic, finite element codes can incorporate structural complexities such as: geometrically accurate models; human occupant models; and advanced material models to include nonlinear stress-strain behaviors, and material failure. Validation of these crash simulations is difficult due to a lack of sufficient information to adequately determine the uncertainty in the experimental data and the appropriateness of modeling assumptions. This paper evaluates probabilistic approaches to quantify the effects of finite element modeling assumptions on the predicted responses. The vertical drop test of a Fokker F28 fuselage section will be the focus of this paper. The results of a probabilistic analysis using finite element simulations will be compared with experimental data.

Lyle, Karen H.↗
NASA NTRS1994

SeaWiFS technical report series. Volume 16: The second SeaWiFS Intercalibration Round-Robin Experiment, SIRREX-2, June 1993

The results of the second Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Intercalibration Round-Robin Experiment (SIRREX-2), which was held at the Center for Hydro-Optics and Remote Sensing (CHORS) at San Diego State University on 14-25 Jun. 1993 are presented. SeaWiFS is an ocean color radiometer that is scheduled for launch in 1994. The SIRREXs are part of the SeaWiFS Calibration and Validation Program that includes the GSFC, CHORS, NIST, and several other laboratories. GSFC maintains the radiometric scales (spectral radiance and irradiance) for the SeaWiFS program using spectral irradiance standards lamps, which are calibrated by NIST. The purpose of each SIRREX is to assure that the radiometric scales which are realized by the laboratories who participate in the SeaWiFS Calibration and Validation Program are correct; that is, the uncertainties of the radiometric scales are such that measurements of normalized water-leaving radiance using oceanographic radiometers have uncertainties of 5%. SIRREX-1 demonstrated, from the internal consistency of the results, that the program goals would not be met without improvements to the instrumentation. The results of SIRREX-2 demonstrate that spectral irradiance scales realized using the GSFC standard irradiance lamp (F269) are consistent with the program goals, as the uncertainty of these measurements is assessed to be about 1%. However, this is not true for the spectral radiance scales, where again the internal consistency of the results is used to assess the uncertainty. This is attributed to inadequate performance and characterization of the instrumentation. For example, spatial nonuniformities, spectral features, and sensitivity to illumination configuration were observed in some of the integrating sphere sources. The results of SIRREX-2 clearly indicate the direction for future work, with the main emphasis on instrument characterization and the assessment of the measurement uncertainties so that the results may be stated in a more definitive manner.

Hooker, Stanford B.↗
NASA NTRS2011

Assessment of Laminar, Convective Aeroheating Prediction Uncertainties for Mars Entry Vehicles

An assessment of computational uncertainties is presented for numerical methods used by NASA to predict laminar, convective aeroheating environments for Mars entry vehicles. A survey was conducted of existing experimental heat-transfer and shock-shape data for high enthalpy, reacting-gas CO2 flows and five relevant test series were selected for comparison to predictions. Solutions were generated at the experimental test conditions using NASA state-of-the-art computational tools and compared to these data. The comparisons were evaluated to establish predictive uncertainties as a function of total enthalpy and to provide guidance for future experimental testing requirements to help lower these uncertainties.

Hollis, Brian R.↗
DOE OSTI2020

BioSTEAM-LCA: An Integrated Modeling Framework for Agile Life Cycle Assessment of Biorefineries under Uncertainty

Biorefineries will play a critical role in sustainable bioeconomies, but projections of their environmental impacts vary widely. A core challenge with life cycle assessments (LCAs) of biorefineries is that they are often disconnected from biorefinery design, simulation, and techno-economic analysis (TEA). This lack of integration is a barrier to early stage technology and process evaluations, reducing consistency and transparency across sustainability indicators while limiting our understanding of the relative importance of individual factors (e.g., design decisions, greenhouse gas emission accounting procedures), how these factors interact, and trade-offs or synergies with process economics. In this study, we propose a new agile LCA framework, BioSTEAMLCA, which layers onto BioSTEAM (Biorefinery Simulation and Techno-Economic Analysis Modules, which automates biorefinery design, simulation, and TEA) to characterize the environmental impacts of biorefineries across a landscape of designs, technology performance assumptions, and contexts. Inventory databases and impact assessment methods are integrated to enable flexible user defined LCA system models, and the implications of uncertainties throughout the production system are characterized via Monte Carlo simulation. To demonstrate the capabilities of BioSTEAMLCA, we present a case study for sugarcane ethanol production. Overall, BioSTEAM-LCA enables computationally efficient, agile gate-to-gate LCA to evaluate biorefinery processes, the production of candidate biofuels and bioproducts, and trade-offs among productivity, economics, and environmental impacts under uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2011

Assessment of MODIS Reflected Solar Calibration Uncertainty

Determination of the calibration accuracy and traceability of a remote sensing instrument is a driving issue in the use of satellite data for calibration inter-comparisons and studying climate change. The Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) instruments have successfully operated for more than 11 and 9 years, respectively. Twenty of the thirty six MODIS spectral bands are in the reflected solar region with center wavelengths ranging from 0.41 to 2.2 microns. MODIS reflective solar band (RSB) on-orbit calibration is reflectance based through the use of an on-board solar diffuser (SO). The calibration uncertainty requirements are +/-2.0% for the RSB reflectance factors at sensor specified typical scene reflectances or radiances. The SO bi-directional reflectance factor (BRF) was characterized pre-launch and its on-orbit changes are tracked by an on-board solar diffuser stability monitor (SDSM). This paper provides an assessment of MODIS RSB on-orbit calibration traceability and uncertainty for its Level 1B (L1B) reflectance factors. It examines in details each of the uncertainty contributors, including those from pre-launch measurements as well as on-orbit observations. Common challenging issues and differences due to individual sensors' specific characteristics and on-orbit performance are also discussed in this paper. Guidance and recommendations are presented, based on lessons from MODIS RSB calibration uncertainty assessment, for the development of future instrument calibration and validation plans.

Xiong, Xiaoxiong↗