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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

The Ocean Colour Climate Change Initiative: I. A Methodology for Assessing Atmospheric Correction Processors Based on In-Situ Measurements

The Ocean Colour Climate Change Initiative intends to provide a long-term time series of ocean colour data and investigate the detectable climate impact. A reliable and stable atmospheric correction procedure is the basis for ocean colour products of the necessary high quality. In order to guarantee an objective selection from a set of four atmospheric correction processors, the common validation strategy of comparisons between in-situ and satellite derived water leaving reflectance spectra, is extended by a ranking system. In principle, the statistical parameters such as root mean square error, bias, etc. and measures of goodness of fit, are transformed into relative scores, which evaluate the relationship of quality dependent on the algorithms under study. The sensitivity of these scores to the selected database has been assessed by a bootstrapping exercise, which allows identification of the uncertainty in the scoring results. Although the presented methodology is intended to be used in an algorithm selection process, this paper focusses on the scope of the methodology rather than the properties of the individual processors.

Climate Change↗
NASA NTRS2014

A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

Air pollution↗
DOE OSTI2022

Dark Energy Survey Year 3 results: galaxy clustering and systematics treatment for lens galaxy samples

ABSTRACT In this work, we present the galaxy clustering measurements of the two DES lens galaxy samples: a magnitude-limited sample optimized for the measurement of cosmological parameters, maglim, and a sample of luminous red galaxies selected with the redmagic algorithm. maglim/redmagic sample contains over 10 million/2.5 million galaxies and is divided into six/five photometric redshift bins spanning the range z ∈ [0.20, 1.05]/z ∈ [0.15, 0.90]. Both samples cover 4143 $\deg ^2$ over which we perform our analysis blind, measuring the angular correlation function with an S/N ∼ 63 for both samples. In a companion paper, these measurements of galaxy clustering are combined with the correlation functions of cosmic shear and galaxy–galaxy lensing of each sample to place cosmological constraints with a 3 × 2pt analysis. We conduct a thorough study of the mitigation of systematic effects caused by the spatially varying survey properties and we correct the measurements to remove artificial clustering signals. We employ several decontamination methods with different configurations to ensure the robustness of our corrections and to determine the systematic uncertainty that needs to be considered for the final cosmology analyses. We validate our fiducial methodology using lognormal mocks, showing that our decontamination procedure induces biases no greater than 0.5σ in the (Ωm, b) plane, where b is the galaxy bias.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data

Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning regressor ensemble model, comprised of a random forest and an XGBoost model, and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Comparing Outdoor to Indoor Performance for Bifacial Modules Affected by Polarization-Type Potential-Induced Degradation

Bifacial photovoltaic (PV) modules have the advantage of using light reflected off of the ground to contribute to power production. Predicting the energy gain is challenging and requires complex models to do so accurately. Often, module degradation over time is neglected in models for the sake of simplicity or is underestimated. Comparing outdoor and indoor current–voltage (I–V) performance for bifacial modules is more challenging than for monofacial modules, as there are additional variables to consider such as rear albedo non-uniformity, cell mismatch, and their effects on temperature. This challenge is compounded when heterogeneous degradation modes occur, such as polarization-type potential-induced degradation (PID-p). To examine the effects of PID-p on I–V predictions using an empirical data-driven approach, 16 bifacial PERC modules are installed outdoors on racks with different albedo conditions. A subset is exposed to high-voltage biases of −1500 V or +1500 V. Outdoor data are traced at irradiance ranges of 150–250 W/m 2 , 500–600 W/m 2 , and 900–1000 W/m 2 . These curves are corrected using control module temperature, wire resistivity, and module resistance measured indoors. We examine several methods to transform indoor I–V curves to accurately, and more simply than existing methods, approximate outdoor performance for bifacial modules without and with varying levels of PID-p degradation. This way, bifacial performance modeling can be more accessible and informed by fielded, degraded modules. Distributions of percent errors between indoor and outdoor performance parameters and Mean Absolute Percent Errors (MAPEs) are used to assess method quality. Results including low-irradiance data (150–250 W/m 2 ) are discussed but are filtered for quantifying method quality as these data introduce substantial errors. The method with the most optimal tradeoff between low MAPE and analysis simplicity involves measuring the front side of a module indoors at an irradiance equal to plane-of-array irradiance plus the product of module bifaciality and albedo irradiance. This method gives MAPE values of 1–6.5% for non-degraded and 1.6–5.9% for PID-p degraded module performance.

14 SOLAR ENERGY↗
NASA NTRS1995

Calibration of Tests for Time-Dilation in GRB Pulse Structures

If gamma-ray bursts are at cosmological distances, then not only their constituent pulses but also the intervals between pulses should be time-dilated. Unlike time-dilation measures of pulse emission, intervals would appear to require less "K-correction" for redshift of narrower temporal structure from higher energy into the band of observation. However, stretching of pulse intervals is intrinsically difficult to measure without incurring a timescale-dependent bias since, as time profiles are stretched, more structure can appear at the limit of resolution. This problem is compounded in dimmer bursts since identification of significant structures is problematic. We have attempted to minimize brightness bias problems by equalizing the signal-to-noise level of all bursts. Then, analyzing wavelet-denoised profiles binned to several resolutions, we identify significant (3 - 4 sigma) fluctuations between pulse structures and interjacent valleys. When bursts are ranked by peak flux, an interval time-dilation signature is evident, but its measure is dependent upon choice of temporal resolution. This result appears to tentatively and qualitatively confirm the work of Davis, who found such a signature using a pulse fitting approach.

Norris, J. P.↗
NASA NTRS2012

High Precision, Absolute Total Column Ozone Measurements from the Pandora Spectrometer System: Comparisons with Data from a Brewer Double Monochromator and Aura OMI

We present new, high precision, high temporal resolution measurements of total column ozone (TCO) amounts derived from ground-based direct-sun irradiance measurements using our recently deployed Pandora single-grating spectrometers. Pandora's small size and portability allow deployment at multiple sites within an urban air-shed and development of a ground-based monitoring network for studying small-scale atmospheric dynamics, spatial heterogeneities in trace gas distribution, local pollution conditions, photochemical processes and interdependencies of ozone and its major precursors. Results are shown for four mid- to high-latitude sites where different Pandora instruments were used. Comparisons with a well calibrated double-grating Brewer spectrometer over a period of more than a year in Greenbelt MD showed excellent agreement and a small bias of approximately 2 DU (or, 0.6%). This was constant with slant column ozone amount over the full range of observed solar zenith angles (15-80), indicating adequate Pandora stray light correction. A small (1-2%) seasonal difference was found, consistent with sensitivity studies showing that the Pandora spectral fitting TCO retrieval has a temperature dependence of 1% per 3K, with an underestimation in temperature (e.g., during summer) resulting in an underestimation of TCO. Pandora agreed well with Aura-OMI (Ozone Measuring Instrument) satellite data, with average residuals of <1% at the different sites when the OMI view was within 50 km from the Pandora location and OMI-measured cloud fraction was <0.2. The frequent and continuous measurements by Pandora revealed significant short-term (hourly) temporal changes in TCO, not possible to capture by sun-synchronous satellites, such as OMI, alone.

Ozone↗
DOE OSTI2020

Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data: Preprint

Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning model and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1997

Automation Bias and Countermeasures in Flight Crews

Results of recent research investigating the use of automated systems have indicated the presence of automation bias, a term describing errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers take inappropriate action because they over-attend to automated information or directives, and automation omission errors, i.e., errors made when decision makers do not take appropriate action because they are not informed of an imminent problem or situation by automated aids, can result from this tendency. In a series of studies, participants who perceived themselves as "accountable" for their strategies of interaction with automation were significantly more likely to verify its correct functioning, and committed significantly fewer automation-related errors than those who did not report this perception. In this study, we focus on two manipulations to encourage verification behaviors within cockpit crews. The first, an information/training manipulation, will focus on explicit information on automation bias, and training for verification of automated functioning. The second manipulation will consist of display prompts to verify automated functioning. Participants (glass-cockpit crews) will fly a series of approaches on the Mini-ACFS, a two-person pan-task flight simulator. Approach scenarios include several automation "events," that is, glitches, malfunctions, or inappropriate recommendations, that can be caught if cross-checked with other cockpit indicators. We anticipate that these manipulations will ameliorate automation bias. Final data analysis will be completed prior to the OSU symposium, and is expected to support the hypotheses that: a) reduced errors are associated with verification behaviors; b) crews will be more likely to verify under training/display conditions than when in the control condition; and c) effects will persist when subjects return for a follow-up exercise 6-9 weeks after their first session. Implications for training and automation design will be discussed.

Mosier, Kathleen, L.↗
NASA NTRS1988

Using ridge regression in systematic pointing error corrections

A pointing error model is used in the antenna calibration process. Data from spacecraft or radio star observations are used to determine the parameters in the model. However, the regression variables are not truly independent, displaying a condition known as multicollinearity. Ridge regression, a biased estimation technique, is used to combat the multicollinearity problem. Two data sets pertaining to Voyager 1 spacecraft tracking (days 105 and 106 of 1987) were analyzed using both linear least squares and ridge regression methods. The advantages and limitations of employing the technique are presented. The problem is not yet fully resolved.

Guiar, C. N.↗
NASA NTRS1979

Orbit/attitude estimation with LANDSAT Landmark data

The use of LANDSAT landmark data for orbit/attitude and camera bias estimation was studied. The preliminary results of these investigations are presented. The Goddard Trajectory Determination System (GTDS) error analysis capability was used to perform error analysis studies. A number of questions were addressed including parameter observability and sensitivity, effects on the solve-for parameter errors of data span, density, and distribution an a priori covariance weighting. The use of the GTDS differential correction capability with acutal landmark data was examined. The rms line and element observation residuals were studied as a function of the solve-for parameter set, a priori covariance weighting, force model, attitude model and data characteristics. Sample results are presented. Finally, verfication and preliminary system evaluation of the LANDSAT NAVPAK system for sequential (extended Kalman Filter) estimation of orbit, and camera bias parameters is given.

Hall, D. L.↗
DOE OSTI2023

Comparative Density Functional Theory Study of Magnetic Exchange Couplings in Dinuclear Transition-Metal Complexes

Multicenter transition-metal complexes (MCTMs) with magnetically interacting ions have been proposed as components for information-processing devices and storage units. For any practical application of MCTMs as magnetic units, it is crucial to characterize their magnetic behavior, and in particular, the isotropic magnetic exchange coupling, J, between its magnetic centers. Due to the large size of typical MCTMs, density functional theory is the only practical electronic structure method for evaluating the J coupling. Here, we assess the accuracy of different density functional approximations for predicting the magnetic couplings of eight dinuclear transition-metal complexes, including five dimanganese, two dicopper, and one divanadium with known reliable experimental J couplings spanning from ferromagnetic to strong antiferromagnetic. The density functionals considered include global hybrid functionals which mix semilocal density functional approximations and exact exchange with a fixed admixing parameter, six local hybrid functionals where the admixing parameters are extended to be spatially dependent, the SCAN and r 2 SCAN meta-generalized gradient approximations (GGAs), and two widely used GGAs. We found that global hybrids tested in this work have a tendency to over-correct the error in magnetic coupling parameters from the Perdew–Burke–Ernzerhof (PBE) GGA as seen for manganese complexes. The performance of local hybrid density functionals shows no improvement in terms of bias and is scattered without a clear trend, suggesting that more efforts are needed for the extension from global to local hybrid density functionals for this particular property. The SCAN and r 2 SCAN meta-GGAs are found to perform as well as benchmark global hybrids on most tested complexes. We further analyze the charge density redistribution of meta-GGAs as well as global and local hybrid density functionals with respect to that of PBE, in connection to the self-interaction error or delocalization error.

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

Systematic softening in universal machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have led to universal MLIPs (uMLIPs) that are pre-trained on diverse datasets, providing opportunities for universal force fields and foundational machine learning models. However, their performance in extrapolating to out-of-distribution complex atomic environments remains unclear. In this study, we highlight a consistent potential energy surface (PES) softening effect in three uMLIPs: M3GNet, CHGNet, and MACE-MP-0, which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces, defects, solid-solution energetics, ion migration barriers, phonon vibration modes, and general high-energy states. The PES softening behavior originates primarily from the systematically underpredicted PES curvature, which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets. Our findings suggest that a considerable fraction of uMLIP errors are highly systematic, and can therefore be efficiently corrected. We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

WRF Modelling of Deep Convection and Hail for Wind Power Applications

Deep convection and the related occurrence of hail, intense precipitation and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the United States Southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modelling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates and precipitation totals show good agreement with observations. Here, the occurrence of precipitation during power producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modelled with a mean proportion correct of 0.77 and an odds ratio 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry and there is evidence for positive model bias in cloud reflectivity.

17 WIND ENERGY↗
NASA NTRS2020

SCA Test Report: H4RG-21224 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below.The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2020

SCA Test Report: H4RG-21225 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test,the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2020

SCA Test Report: H4RG-21317 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 μm.This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗
NASA NTRS2020

SCA Test Report: H4RG-21319 WFIRST

This report summarizes the measured performance for the Sensor Chip Assembly (SCA), which is identified in Table 1. The SCA architecture is a substrate-removed HgCdTe detector with an area of 4096x4096 pixels (with a reference pixel area of four pixels deep around all four sides, available to substitute corresponding image pixels) and a pixel pitch of 10 µm. This SCA has been tested at the Goddard Space Flight Center (GSFC/NASA) Detector Characterization Laboratory (DCL). Teledyne (the vendor) classifies its detectors into different grades based on testing performed at its facility. The classification of this SCA and the tested dates are included in Table 1 below. The tests performed on this array are derived from the document WFIRST-PROC-09220_WFIRST-SCA-ATP_-.docx. A summary of the test parameters, requirements, and test results is presented in Table 2. The details of each test are subsequently described in the report. In the Summary (Table 2), SCA results are reported at an operating temperature of 95K, and 1.0 V bias voltage. In the detailed section of each test, the results for 95 K and 0.5V bias voltage is also reported. The data for the result reported in this document was acquired in pixel reset mode. For all cases, the frame time used is 2.830 seconds. Reference pixel correction was applied to every raw frame.

Laddawan Miko↗