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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

3D Air Quality and the Clean Air Interstate Rule: Lagrangian Sampling of CMAQ Model Results to Aid Regional Accountability Metrics

The Clean Air Interstate Rule (CAIR) is expected to reduce transport of air pollutants (e.g. fine sulfate particles) in nonattainment areas in the Eastern United States. CAIR highlights the need for an integrated air quality observational and modeling system to understand sulfate as it moves in multiple dimensions, both spatially and temporally. Here, we demonstrate how results from an air quality model can be combined with a 3d monitoring network to provide decision makers with a tool to help quantify the impact of CAIR reductions in SO2 emissions on regional transport contributions to sulfate concentrations at surface monitors in the Baltimore, MD area, and help improve decision making for strategic implementation plans (SIPs). We sample results from the Community Multiscale Air Quality (CMAQ) model using ensemble back trajectories computed with the NASA Langley Research Center trajectory model to provide Lagrangian time series and vertical profile information, that can be compared with NASA satellite (MODIS), EPA surface, and lidar measurements. Results are used to assess the regional transport contribution to surface SO4 measurements in the Baltimore MSA, and to characterize the dominant source regions for low, medium, and high SO4 episodes.

Fairlie, T. D.↗
NASA NTRS2007

Expedition to Siberia: A Firsthand Account

Nowhere on Earth is warming faster than the Arctic. In northern Siberia, average temperatures have risen 3-5 deg F over the past 30 years, whereas the worldwide average increase in that time is 1 deg F. Betweeen July 28 and August 12, 2007, a small international team of remote sensing and forest ecosystem scientists from NASA and Russia's Academy of Science set off on a three-week scientific expedition through the heart of the remote, wild forests of Siberia. They traveled southward down the Kochechum River observing the gradual transition from tundra to taiga, taking inventory of plant species along the way, and making ground-truth measurements to validate data being collected by several NASA satellites flying 700 kilometers overhead.

Ranson, Jon↗
NASA NTRS2012

Accounting for Proof Test Data in a Reliability Based Design Optimization Framework

This paper investigates the use of proof (or acceptance) test data during the reliability based design optimization of structural components. It is assumed that every component will be proof tested and that the component will only enter into service if it passes the proof test. The goal is to reduce the component weight, while maintaining high reliability, by exploiting the proof test results during the design process. The proposed procedure results in the simultaneous design of the structural component and the proof test itself and provides the designer with direct control over the probability of failing the proof test. The procedure is illustrated using two analytical example problems and the results indicate that significant weight savings are possible when exploiting the proof test results during the design process.

Ventor, Gerharad↗
NASA NTRS2016

Can Aerosol Direct Radiative Effects Account for Analysis Increments of Temperature in the Tropical Atlantic?

In the late 1990's, prior to the launch of the Terra satellite, atmospheric general circulation models (GCMs) did not include aerosol processes because aerosols were not properly monitored on a global scale and their spatial distributions were not known well enough for their incorporation in operational GCMs. At the time of the first GEOS Reanalysis (Schubert et al. 1993), long time series of analysis increments (the corrections to the atmospheric state by all available meteorological observations) became readily available, enabling detailed analysis of the GEOS-1 errors on a global scale. Such analysis revealed that temperature biases were particularly pronounced in the Tropical Atlantic region, with patterns depicting a remarkable similarity to dust plumes emanating from the African continent as evidenced by TOMS aerosol index maps. Yoram Kaufman was instrumental encouraging us to pursue this issue further, resulting in the study reported in Alpert et al. (1998) where we attempted to assess aerosol forcing by studying the errors of a the GEOS-1 GCM without aerosol physics within a data assimilation system. Based on this analysis, Alpert et al. (1998) put forward that dust aerosols are an important source of inaccuracies in numerical weather-prediction models in the Tropical Atlantic region, although a direct verification of this hypothesis was not possible back then. Nearly 20 years later, numerical prediction models have increased in resolution and complexity of physical parameterizations, including the representation of aerosols and their interactions with the circulation. Moreover, with the advent of NASA's EOS program and subsequent satellites, atmospheric aerosols are now monitored globally on a routine basis, and their assimilation in global models are becoming well established. In this talk we will reexamine the Alpert et al. (1998) hypothesis using the most recent version of the GEOS-5 Data Assimilation System with assimilation of aerosols. We will explicitly calculate the impact of aerosols on the temperature analysis increments in the tropical Atlantic and assess the extent to which inclusion of atmospheric aerosols have reduced these increments.

General Circulation Models↗
NASA NTRS2017

Accounting for the Speed-Accuracy Trade-Off in Quantifying Human-In-The-Loop Error Probabilities

Human-in-the-loop (HITL) simulations cannot collect enough data from human operators to validate estimates of error probabilities for task components. Error rates for tasks have been estimated by using laboratory data for error rates depending on variables such as the cognitive complexity of the task. The limited channel capacity of human operators compels error rates to be strongly related to the time available for the task, the speed-accuracy trade-off. HITL simulations can provide valuable data on the time available for the operator's tasks. We propose that the response times be used in conjunction with measured speed-accuracy curves to estimate the operator error rates contributing to mission failure. Such analyses should be especially important in the estimation of error rates in off-nominal situations.

probability↗
NASA NTRS2019

Sensitivity of Maize Yield in Smallholder Systems to Climate Scenarios in Semi-Arid Regions of West Africa: Accounting for Variability in Farm Management Practices

Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 2040–2069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between −9% and −39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.

Freduah, Bright S.↗
NASA NTRS2020

Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system

The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents ∆-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.

Lakhmiri, Dounia↗
NASA NTRSDate not supplied

Accounting for the Influence of Decorrelation in Microphone Phased Array Deconvolution Methods

Microphone phased arrays are a common tool for use in aeroacoustic wind tunnel testing. The analysis of acquired array data is known to suffer from decorrelation effects, where the coherence of an acoustic wave measured by a pair of microphones is degraded as the wave passes through a turbulent free shear layer or boundary layer. This paper describes, in detail, how to mitigate the influence of decorrelation effects when processing array data with deconvolution methods. This is done using the DAMAS algorithm as an example, applied to recent airframe noise test data acquired in the NASA Langley 14- by 22-Foot Subsonic Tunnel. Two ways of handling the turbulent propagation modeling, both assuming plane wave propagation, are described. Results show that while turbulence model fit parameters may differ, both methods output extremely similar deconvolution results. Further improvements likely require more accurate mean shear layer data prior to developing more involved turbulence models.

microphone array↗