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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Comparison of the Impact of the Arctic Oscillation and East Atlantic - West Russia Teleconnection on Interannual Variation in East Asian Winter Temperatures and Monsoon

The large-scale impacts of the Arctic Oscillation (AO) and the East Atlantic/West Russia (EA/WR) teleconnection on the East Asian winter climate anomalies are compared for the past 34 winters focusing on 1) interannual monthly to seasonal temperature variability, 2) East Asian winter monsoon (EAWM), and 3) the Siberian high (SH) and cold surge. Regression analysis reveals warming by AO and EA/WR over mid-latitude East Asia during their positive phase and vice versa. The EA/WR impact is found to be comparable to the AO impact in affecting the East Asian temperature and monsoon. For example, warm (cold) months over mid-latitude East Asia during the positive (negative) AO are clearly seen when the AO and EA/WR are in the same phase. Near zero correlation is found between temperature and the AO phase when both teleconnections are in an opposite phase. The well-known negative relationship between SH and the AO phase is observed significantly more often when the AO is in the same phase with the EA/WR. Also, the indices of EAWM, cold surge, and SH are found to be more highly negative-correlated with the EA/WR rather than with the AO. The advective temperature change and associated circulation demonstrate that the anomalous large-scale field including the SH over the mid-latitude Asian inland is better represented by the EA/WR, influencing the East Asian winter climates. These results suggest that the impact of EA/WR should be considered more important than previously thought for a better understanding of East Asian winter temperature and monsoon variability.

Arctic Oscillation↗
NASA NTRS2016

A New Load Residual Threshold Definition for the Evaluation of Wind Tunnel Strain-Gage Balance Data

A new definition of a threshold for the detection of load residual outliers of wind tunnel strain-gage balance data was developed. The new threshold is defined as the product between the inverse of the absolute value of the primary gage sensitivity and an empirical limit of the electrical outputs of a strain{gage. The empirical limit of the outputs is either 2.5 microV/V for balance calibration or check load residuals. A reduced limit of 0.5 microV/V is recommended for the evaluation of differences between repeat load points because, by design, the calculation of these differences removes errors in the residuals that are associated with the regression analysis of the data itself. The definition of the new threshold and different methods for the determination of the primary gage sensitivity are discussed. In addition, calibration data of a six-component force balance and a five-component semi-span balance are used to illustrate the application of the proposed new threshold definition to different types of strain{gage balances. During the discussion of the force balance example it is also explained how the estimated maximum expected output of a balance gage can be used to better understand results of the application of the new threshold definition.

load residuals↗
NASA NTRS2016

A Quantum Annealing Computer Team Addresses Climate Change Predictability

The near confluence of the successful launch of the Orbiting Carbon Observatory2 on July 2, 2014 and the acceptance on August 20, 2015 by Google, NASA Ames Research Center and USRA of a 1152 qubit D-Wave 2X Quantum Annealing Computer (QAC), offered an exceptional opportunity to explore the potential of this technology to address the scientific prediction of global annual carbon uptake by land surface processes. At UMBC,we have collected and processed 20 months of global Level 2 light CO2 data as well as fluorescence data. In addition we have collected ARM data at 2sites in the US and Ameriflux data at more than 20 stations. J. Dorband has developed and implemented a multi-hidden layer Boltzmann Machine (BM) algorithm on the QAC. Employing the BM, we are calculating CO2 fluxes by training collocated OCO-2 level 2 CO2 data with ARM ground station tower data to infer to infer measured CO2 flux data. We generate CO2 fluxes with a regression analysis using these BM derived weights on the level 2 CO2 data for three Ameriflux sites distinct from the ARM stations. P. Gentine has negotiated for the access of K34 Ameriflux data in the Amazon and is applying a neural net to infer the CO2 fluxes. N. Talik validated the accuracy of the BM performance on the QAC against a restricted BM implementation on the IBM Softlayer Cloud with the Nvidia co-processors utilizing the same data sets. G. Nearing and K. Harrison have extended the GSFC LIS model with the NCAR Noah photosynthetic parameterization and have run a 10 year global prediction of the net ecosystem exchange. C. Pellisier is preparing a BM implementation of the Kalman filter data assimilation of CO2 fluxes. At UMBC, R. Prouty is conducting OSSE experiments with the LISNoah model on the IBM iDataPlex to simulate the impact of CO2 fluxes to improve the prediction of global annual carbon uptake. J. LeMoigne and D. Simpson have developed a neural net image registration system that will be used for MODIS ENVI and will be converted to a BM algorithm implementation on the QAC. The first integer adder has been implemented on the D-Wave 2X by A. Shehab that will perform HAAR wavelets for image compression of MODIS scenes. Finally, based on the next generations of QACs, we are preparing a 5-year performance road map on the scalability of the current QAC algorithms.

Science Data Processing↗
NASA NTRS2017

A Universal Threshold for the Assessment of Load and Output Residuals of Strain-Gage Balance Data

A new universal residual threshold for the detection of load and gage output residual outliers of wind tunnel strain{gage balance data was developed. The threshold works with both the Iterative and Non{Iterative Methods that are used in the aerospace testing community to analyze and process balance data. It also supports all known load and gage output formats that are traditionally used to describe balance data. The threshold's definition is based on an empirical electrical constant. First, the constant is used to construct a threshold for the assessment of gage output residuals. Then, the related threshold for the assessment of load residuals is obtained by multiplying the empirical electrical constant with the sum of the absolute values of all first partial derivatives of a given load component. The empirical constant equals 2.5 microV/V for the assessment of balance calibration or check load data residuals. A value of 0.5 microV/V is recommended for the evaluation of repeat point residuals because, by design, the calculation of these residuals removes errors that are associated with the regression analysis of the data itself. Data from a calibration of a six-component force balance is used to illustrate the application of the new threshold definitions to real{world balance calibration data.

wind tunnel balance↗
NASA NTRS2017

Changes in the Structure and Propagation of the MJO with Increasing CO2

Changes in the Madden-Julian Oscillation (MJO) with increasing CO2 concentrations are examined using the Goddard Institute for Space Studies Global Climate Model (GCM). Four simulations performed with fixed CO2 concentrations of 0.5, 1, 2 and 4 times pre-industrial levels using the GCM coupled with a mixed layer ocean model are analyzed in terms of the basic state, rainfall and moisture variability, and the structure and propagation of the MJO.The GCM simulates basic state changes associated with increasing CO2 that are consistent with results from earlier studies: column water vapor increases at approximately 7.1% K(exp -1), precipitation also increases but at a lower rate (approximately 3% K(exp -1)), and column relative humidity shows little change. Moisture and rainfall variability intensify with warming. Total moisture and rainfall variability increases at a rate that is similar to that of the mean state change. The intensification is faster in the MJO-related anomalies than in the total anomalies, though the ratio of the MJO band variability to its westward counterpart increases at a much slower rate. On the basis of linear regression analysis and space-time spectral analysis, it is found that the MJO exhibits faster eastward propagation, faster westward energy dispersion, a larger zonal scale and deeper vertical structure in warmer climates.

Adames, Angel F.↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRSDate not supplied

Assessing Flooding Vulnerability to Assist High Water Intervention and Urban Planning Programs in the Charles River Watershed with NASA DEVELOP

The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a total population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flooding events increases and a changing climate poses an ongoing threat to local communities, governments and organizations in Massachusetts are in need of accurate flood risk assessments. NASA DEVELOP partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess flood vulnerability and susceptibility in the watershed. The team used Landsat 5 Thematic Mapper, Landsat 8 Operational Land Imager, Sentinel-1 C-Band Synthetic Aperture Radar, and Sentinel-2 MultiSpectral Instrument to assess the feasibility of identifying the extent of past flood events using remote sensing. After identifying images that overlapped with reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding in the time available for this study. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical regression analysis and validation and supervised classification to develop a flood susceptibility map, incorporating several flood conditioning factors. The susceptibility maps were calibrated to various thresholds, including two that highlight hypothetical flooding under more liberal and more conservative planning scenarios. These were overlaid with demographic and socioeconomic data to create flood vulnerability maps. The team’s flood susceptibility maps showed an improvement in capturing known flood events over the FEMA 100-year and 500-year floodplain maps. These results will be improved with the addition of stormwater drainage mapping and precipitation data. Results can be used to fill in the gaps to help the stakeholders understand their communities’ vulnerability and susceptibility to flooding and improve their preparedness plans.

Trista Brophy↗
NASA NTRSDate not supplied

Application of Support Vector Regression to Derive Crater Depth/Diameter From Satellite Images

Through the study of impact crater shapes, one can draw important conclusions about the nature and evolution of planetary surfaces [e.g., 1-4].In particular, studying the depth (d) to diameter (D)ratio (d/D) of a population of impact craters, in combination with crater count statistics, can yield valuable insights regarding rates of erosion and burial[5]. Motivated by the great abundance of available planetary surface image data, the goal of this project is to develop an efficient way to estimate d/D from satellite images of impact craters for which stereo information is not available [6]. We set out to develop and train a machine learning algorithm to extract d/D from a dataset of synthetic impact crater images for which model d/D is known. The applications of machine learning to planetary science are numerous and diverse [7], including automatic planetary surface mapping [8] and the detection of impact craters [9]. Our algorithm makes use of Support Vector Regression (SVR), which is a type of Support Vector Machine (SVM) [10, 11].SVMs are a branch of supervised machine learning valued for their straightforward implementation and versatility in solving both classification and regression problems. In regression analysis, an SVR algorithm produces a hyperplane function to fit the training data points, as well as an ε-tube that surrounds the hyperplane. Tunable hyperparameters include the width of the ε-tube (ε) and the amount an algorithm is penalized for points which fall outside the ε-tube.

L R Chin↗
NASA NTRS2021

Assessment of aerosol burden over Ghana

Although air pollution in Ghana is ranked number one in environmental health threats to public health and sixth to cause of deaths, routine monitoring is rare. This paper presents fourteen years (2005-2018) assessment of aerosol optical depth (AOD) at 3 km resolution from MODIS Aqua and Terra satellites to ascertain the Spatio-temporal and seasonal distribution of aerosols over Ghana and its major cities. The MODIS AOD at 3 km were validated against ground-based Aerosol Robotic Network (AERONET) AODs to ascertain the suitability of the MODIS 3 km data for air quality application in the region. The contribution of distant aerosols to city aerosol loadings was also assessed with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backscatter model. A moderate-high aerosol burden (AODs ~ 0.50) was observed over Ghana with a significant contribution from the pre-monsoon season. City centres of Takoradi and Kumasi showed higher aerosol loads (AODs ~ 0.80) than Accra and Tamale. The HYSPLIT model showed that distant or transported aerosol sources to the city centres were of both marine and land generated origins. Linear regression analysis between MODIS AOD and AERONET AOD showed a reasonably good correlation of ~ 0.60 for Aqua and Terra. From the validation analysis, both Aqua and Terra satellites can be used for air quality monitoring over Ghana; however, more ground research must be conducted to ascertain better aerosol model assumptions for the region.

Aerosols↗
NASA NTRSDate not supplied

Development of the Ames Global Hyperspectral Synthetic Dataset

This study develops the surface BRDF (bidirectional reflectance distribution function) product of the Ames Global Hyperspectral Synthetic Dataset (AGHSD), based on the corresponding MODIS products, to support the NASA Surface Biology and Geology mission development. A main challenge in deriving a hyperspectral dataset from the multi-band satellite products is how to identify a succinct yet robust algorithm that allow us to infer BRDF at unobserved wavelengths based on the few observed bands. Using the theories of radiative transfer in vegetation canopies, we arrive at a simple equation that accurately approximates hyperspectral surface BRDF as the weighted sum of components from the soil and the vegetation. Each of the components is modeled by the product of the spectrally-dependent optical properties of a surface element (the spectra of the soil surface reflectance, the leaf single albedo, or the canopy scattering coefficient) and a spectrally-independent bidirectional scattering function. The optical properties of the soil and the vegetation can be obtained from existing spectral libraries or model simulations. The bidirectional scattering functions are represented by the Ross-Thick-Li-Sparse BRDF model, where the linear coefficients are estimated with regression analysis from the multi-band MODIS data. We validate the algorithm with simulations by Monte Carlo Ray Tracing model experiments, and the results are highly consistent with the theoretic derivation. We apply the algorithm to generate the AGHSD BRDF product at 1km and 8-day resolutions for the year of 2019. The results are biogeochemically and physically coherent and consistent, and thus serve the goal to support the science and application development of the SBG community.

Hyperspectral↗
NASA NTRS2024

Central Park Ecological Conservation: Assessing Tree Health Conditions in New York City’s Central Park with NASA Earth Observation Data

The Central Park Conservancy stewards New York City’s iconic Central Park with a mission to preserve the park for all. This mission is complicated by the spread of Dutch elm disease (DED) which has threatened the culturally and ecologically significant American elm tree (Ulmus americana). Central Park is home to one of the largest and last remaining urban concentrations of American elm and the Conservancy currently protects them through integrated pest management. This paper discusses an interdisciplinary feasibility study that assessed the application of NASA Earth observations from 2014 to 2023 to detect changes in forest phenology possibly related to DED. Landsat 8 and 9 imagery was used to calculate multiyear time series of the Normalized Difference Vegetation Index (NDVI) and quantify changes in land surface phenology for a given year. A pixel-based logistic regression analysis was performed using changes in NDVI, tree site locations, and recorded occurrences of trees infected with DED as inputs. The results of this analysis show that changes in NDVI derived from Landsat data are capable of detecting unhealthy tree canopies with 71% precision and healthy tree canopies with 41% precision. The study had uncertainties and limitations due to the spatial and temporal resolutions of Landsat, the natural variability in land surface phenology and NDVI, and the attempt to detect disease impacts while disease prevention and mitigation is occurring. As is, the findings of this study and its methods provide managers with an approach for integrating Earth observations to make more informed decisions in the application and timing of urban forest management activities.

Central Park↗
NASA NTRSDate not supplied

Assessing Tree Health Conditions in New York City’s Central Park with Earth Observation Data

The Central Park Conservancy stewards New York City’s iconic Central Park with a mission to preserve the park for all. This mission is complicated by the spread of Dutch elm disease (DED) which has threatened the culturally and ecologically significant American elm tree (Ulmus americana). Central Park is home to one of the largest and last remaining urban concentrations of American elm and the Conservancy currently protects them through integrated pest management. This project is an interdisciplinary feasibility study that assessed the application of NASA Earth observations from 2014 to 2023 to detect changes in forest phenology possibly related to DED. Landsat 8 and 9 imagery was used to calculate a multiyear time series of the Normalized Difference Vegetation Index (NDVI) and quantify changes in land surface phenology. A pixel-based logistic regression analysis was performed using changes in NDVI, tree site locations, and recorded occurrences of trees infected with DED as inputs. The results of this analysis show that changes in NDVI derived from Landsat data are capable of detecting unhealthy tree canopies with 71% precision and healthy tree canopies with 41% precision. The study had uncertainties and limitations due to the spatial and temporal resolutions of Landsat, the natural variability in land surface phenology and NDVI, and the attempt to detect disease impacts while disease prevention and mitigation are occurring. As is, the findings of this study and its methods provide managers with an approach for integrating Earth observations to make more informed decisions in the application and timing of urban forest management activities.

John Hocknell↗
DOE OSTI2018

Theory-based Scaling of Energy Confinement Time for Future Reactor Design

The scaling of thermal energy confinement time plays important roles in tokamak fusion research. A simple log-linear scaling from regression analysis of a large experimental database has been widely used in terms of the engineering or dimensionless parameters. A particularly important example is the IPB98(y,2) scaling for ELMy H-mode plasmas developed for the ITER project [1].

Park, J.M.↗
DOE OSTI2018

Comparison of equivalence ratio transients on combustion instability in single-nozzle and multi-nozzle combustors

Low-emissions gas turbine combustion, achieved through the use of lean, premixed fueling strategies, is susceptible to combustion instability. The driving mechanism for this instability arises from fluctuations of pressure, fuel/air flow rate, and heat release rate. If these fluctuations are relatively in-phase, the combustion system will evolve to a self-excited state. The self-excited instability frequency and amplitude depend mainly on the operating condition and the geometry of the combustor. In this study, we consider the onset and decay of self-excited instabilities, resulting from transients in fuel/air ratio, in both single-nozzle and multi-nozzle combustors. In particular, we examine the differences in the instability onset and decay processes between these two flame configurations, as most gas turbine combustors have multiple nozzles, but most gas turbine combustor experiments utilize a single-nozzle. A nonlinear logistic regression analysis is applied to study the timescales of the decay and onset transients. Variations in the equivalence ratio change the heat release rate distribution inside the combustor, which is captured using chemiluminescence imaging. The normalized Rayleigh index, which shows the spatial distribution of the instability driving, is calculated to analyze the driving strength in different regions of the flame. Comparisons between the single- and multi-nozzle flame transients, including both center and outer flames for the multi-nozzle combustor, suggest that both confinements from the wall and flame-flame interaction are crucial to determining flame dynamics as the equivalence ratio transient changes the heat release rate distribution near corner recirculation zone and flame shear layers.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2019

Mass Variance from Archival X-ray Properties of Dark Energy Survey Year-1 Galaxy Clusters

Using archival X-ray observations and a log-normal population model, we estimate constraints on the intrinsic scatter in halo mass at fixed optical richness for a galaxy cluster sample identified in Dark Energy Survey Year-One (DES-Y1) data with the redMaPPer algorithm. We examine the scaling behavior of X-ray temperatures, $T_X$, with optical richness, $\lambda_{RM}$, for clusters in the redshift range $0.2 50\%$ complete for clusters with $\lambda_{RM} > 130$. Regression analysis on the two samples produces consistent posterior scaling parameters, from which we derive a combined constraint on the residual scatter, $\sigma_{\ln Tx | \lambda} = 0.275 \pm 0.019$. Joined with constraints for $T_X$ scaling with halo mass from the Weighing the Giants program and richness--temperature covariance estimates from the LoCuSS sample, we derive the richness-conditioned scatter in mass, $\sigma_{\ln M | \lambda} = 0.30 \pm 0.04\, _{({\rm stat})} \pm 0.09\, _{({\rm sys})}$, at an optical richness of approximately 70. Uncertainties in external parameters, particularly the slope and variance of the $T_X$--mass relation and the covariance of $T_X$ and $\lambda_{RM}$ at fixed mass, dominate the systematic error. The $95\%$ confidence region from joint sample analysis is relatively broad, $\sigma_{\ln M | \lambda} \in [0.14, \, 0.55]$, or a factor ten in variance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Search for an Exotic S=-2, Q=-2 Baryon Resonance in Proton-Proton Interactions at $\sqrt{s_{NN}}$ = 17.3 GeV

Searches and analyses of strong gravitational lenses are challenging due to the rarity and image complexity of these astronomical objects. Next-generation surveys (both ground- and space-based) will provide more opportunities to derive science from these objects, but only if they can be analyzed on realistic time-scales. Currently, these analyses are expensive. In this work, we present a regression analysis with uncertainty estimates using deep learning models to measure four parameters of strong gravitational lenses in simulated Dark Energy Survey data. Using only $gri$-band images, we predict Einstein Radius, lens velocity dispersion, lens redshift to within $10-15\%$ of truth values and source redshift to $30\%$ of truth values, along with predictive uncertainties. This work helps to take a step along the path of faster analyses of strong lenses with deep learning frameworks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Perspectives on the Calibration of CNN Energy Reconstruction in Highly Granular Calorimeters

We present a study which shows encouraging stability of the response linearity for a simulated high granularity calorimeter module reconstructed by a CNN model to miscalibration, bias, and noise effects. Our results also show an intuitive, quantifiable relationship between these factors and the calibration parameters. We trained a CNN model to reconstruct energy in the calorimeter module using simulated single-pion events; we then observed the response of the model under various miscalibration, bias, and noise conditions that affected the model input. From these data, we estimated linear response models to calibrate the CNN. We also quantified the relationship between these factors and the calibration parameters by regression analysis.

Akchurin, Nural↗