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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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NASA NTRS1976

NASA/ESA CT-990 Spacelab simulation. Appendix A: The experiment operator

A joint NASA/ESA endeavor was established to conduct an extensive spacelab simulation using the NASA CV-990 airborne laboratory. The scientific payload was selected to perform studies in upper atmospheric physics and infrared astronomy with principal investigators from France, the Netherlands, England, and several groups from the United States. Two experiment operators from Europe and two from the U.S. were selected to live aboard the aircraft along with a mission manager for a six-day period and operate the experiments in behalf of the principal scientists. This appendix discusses the experiment operators and their relationship to the joint mission under the following general headings: selection criteria, training programs, and performance. The performance of the proxy operators was assessed in terms of adequacy of training, amount of scientific data obtained, quality of data obtained, and reactions to problems that arose in experiment operation.

Reller, J. O., Jr.↗
NASA NTRS1988

Atmospheric effects on earth rotation and polar motion

The variability in the earth's rotation rate not due to known solid body tides is dominated on time scales of about four years and less by variations in global atmospheric angular momentum (M) as derived from the zonal wind distribution. Among features seen in the length of day record produced by atmospheric forcing are the strong seasonal cycle, quasi-periodic fluctuations around 40-50 days, and an interannual signal forced by a strong Pacific warming event known as the El Nino. Momentum variations associated with these time scales arise in different latitudinal regions. Furthermore, winds in the stratosphere make a particularly important contribution to seasonal variability. Other related topics discussed here are: (1) comparisons of the M series from wind fields produced at different weather centers; (2) the torques that dynamically link the atmosphere and earth; and (3) longer-term nonatmospheric effects that can be seen upon removal of the atmospheric signal.an interestigapplication for climatological purposes is the use of the historical earth rotation series as a proxy for atmospheric wind variability prior to the era of upper-air data. Lastly, results pertaining to the role of atmospheric pressure systems in exciting rapid polar motion are presented.

Salstein, David A.↗
NASA NTRS2018

Modeling and Flight Performance of Supersonic Disk-Gap-Band Parachutes in Slender Body Wakes

NASA's ASPIRE (Advanced Supersonic Parachute Inflation Research and Experiments) project is investigating the supersonic deployment and inflation of full-scale parachutes. To achieve Mars-relevant conditions, the parachutes are deployed at high altitudes over Earth on a sounding rocket platform. During the flight test, Disk-Gap-Band parachutes of 21.5 meter diameter are deployed behind a slender payload 1/6th the diameter of the blunt Mars2020 capsule. Due to the differences in leading body geometry between the test flight and a parachute deployment at Mars, high fidelity numerical simulations of slender and blunt bodywakes, and of rigid parachutes behind them, were used to understand differences and similarities in the flow and the effect on parachute drag. The slender body wake is thinner, closes earlier, and presents a smaller wake deficit. Thus, a parachute deployed in the wake of a slender body is more likely to see a higher dynamic pressure than a parachute deployed behind a blunt body. In the presence of a parachute, the interaction of the unsteady wake with the parachute bow shock is stronger behind the blunt body. Simulations yield highly unsteady forces on the parachute, which was modeled as a rigid body. The mean parachute force behind a slender body is between 3 and 12 percent higher than behind a blunt body, depending on the angle of the parachute with the flow. As the angle of incidence increases, more of the parachute moves out of the leading body wakes, decreasing the sensitivity to leading body shape. To compare the flow past parachutes in Earth's and Mars' atmospheres, simulations were also performed in CO2. At the Mach number considered (1.75), the shock standoff distance ahead of the parachute, post-shock jump conditions, and the resulting parachute forces were found to be very similar in both air and CO2, indicating that a high altitude test is a good proxy for a Mars descent. The results of these numerical simulations and available data on past flight and wind tunnel tests of supersonic Disk-Gap-Band parachutes behind slender bodies were used to generate a parachute drag model for ASPIRE, which in turn was used to help design the flight test. The first flight test occurred in October 2017. The parachute was successfully deployed at Mach 1.77 and an altitude of 42 kilometers. Test instrumentation provided the atmospheric conditions, test vehicle trajectory, and the loads on the parachute along with detailed high-resolution imagery of the inflation process. Reconstruction of the flight test indicated that the measured forces on the parachute were within the model's bounds, although the model over-predicted the parachute force during the first few seconds. The parachute forces during the long subsonic period were well-predicted by the ASPIRE drag model.

Muppidi, Suman↗
DOE OSTI2024

Mass calibration of DES Year-3 clusters via SPT-3G CMB cluster lensing

We measure the stacked lensing signal in the direction of galaxy clusters in the DarkEnergy Survey Year 3 (DES Y3) redMaPPer sample, using cosmic microwave background (CMB)temperature data from SPT-3G, the third-generation CMB camera on the South PoleTelescope (SPT). Here, we estimate the lensing signal using temperature maps constructed fromthe initial 2 years of data from the SPT-3G 'Main' survey, covering 1500 deg$^{2}$ of the Southernsky. We then use this lensing signal as a proxy for the mean cluster mass of the DES sample. Thethermal Sunyaev-Zel'dovich (tSZ) signal, which can contaminate the lensing signal if notaddressed, is isolated and removed from the data before obtaining the mass measurement. In thiswork, we employ three versions of the redMaPPer catalogue: a Flux-Limited sample containing 8865clusters, a Volume-Limited sample with 5391 clusters, and a Volume&Redshift-Limited sample with4450 clusters. For the three samples, we detect the CMB lensing signal at a significance of12.4σ, 10.5σ and 10.2σ and find the mean cluster masses to be M$_{200m}$ = 1.66±0.13 [stat.]± 0.03 [sys.], 1.97±0.18 [stat.]± 0.05 [sys.],and 2.11±0.20 [stat.]± 0.05 [sys.]×10$^{14}$ M$_{⊙}$, respectively. Thisis a factor of ∼ 2 improvement relative to the precision of measurements with previousgenerations of SPT surveys and the most constraining cluster mass measurements using CMB clusterlensing to date. Overall, we find no significant tensions between our results and masses given byredMaPPer mass-richness scaling relations of previous works, which were calibrated using CMBcluster lensing, optical weak lensing, and velocity dispersion measurements from variouscombinations of DES, SDSS and Planck data. We then divide our sample into 3 redshift and 3richness bins, finding no significant discrepancies with optical weak-lensing calibrated masses inthese bins. We forecast a 5.7% constraint on the mean cluster mass of the DES Y3 sample withthe complete SPT-3G surveys when using both temperature and polarization data and including anadditional ∼ 1400 deg$^{2}$ of observations from the 'Extended' SPT-3G survey.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Microwave Satellite Data for Hydrologic Modeling in Ungauged Basins

An innovative flood-prediction framework is developed using Tropical Rainfall Measuring Mission precipitation forcing and a proxy for river discharge from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard the National Aeronautics and Space Administration's Aqua satellite. The AMSR-E-detected water surface signal was correlated with in situ measurements of streamflow in the Okavango Basin in Southern Africa as indicated by a Pearson correlation coefficient of 0.90. A distributed hydrologic model, with structural data sets derived from remote-sensing data, was calibrated to yield simulations matching the flood frequencies from the AMSR-E-detected water surface signal. Model performance during a validation period yielded a Nash-Sutcliffe efficiency of 0.84. We concluded that remote-sensing data from microwave sensors could be used to supplement stream gauges in large sparsely gauged or ungauged basins to calibrate hydrologic models. Given the global availability of all required data sets, this approach can be potentially expanded to improve flood monitoring and prediction in sparsely gauged basins throughout the world

Distributed hydrologic modeling↗
NASA NTRS2000

Satellite Observation of El Nino Effects on Amazon Forest Phenology and Productivity

Climate variability may affect the functioning of Amazon moist tropical forests, and recent modeling analyses suggest that the carbon dynamics of the region vary interannually in response to precipitation and temperature anomalies. However, due to persistent orbital and atmospheric artifacts in the satellite record, remote sensing observations have not provided quantitative evidence that climate variation affects Amazon forest phenology or productivity, We developed a method to minimize and quantify non-biological artifacts in NOAA AVHRR satellite data, providing a record of estimated forest phenological variation from 1982-1993. The seasonal Normalized Difference Vegetation Index (NDVI) amplitude (a proxy for phenology) increased throughout much of the basin during El Nino periods when rainfall was anomalously low. Wetter La Nina episodes brought consistently smaller NDVI amplitudes. Using radiative transfer and terrestrial biogeochemical models driven by these satellite data, we estimate that canopy-energy absorption and net primary production of Amazon forests varied interannually by as much as 21% and 18%, respectively. These results provide large-scale observational evidence for interannual sensitivity to El Nino of plant phenology and carbon flux in Amazon forests.

Asner, Gregory P.↗
DOE OSTI2025

Towards Online Machine Learning in DUNE Data Acquisition

Processing the large volumes of data produced by liquid argon time projection chamber (LArTPC) experiments presents a significant challenge, especially those at the scale of DUNE. This is a particular challenge when aiming to trigger on low-energy neutrinos from core-collapse supernovae, which are typically buried in a high-rate radiological background. To enable real-time event selection suitable for such rare signals, we are developing machine learning based data filtering methods. In order to demonstrate the feasibility of this approach, we implemented such pipeline using the ICEBERG detector at Fermilab as a small-scale LArTPC, with a focus on identifying Michel electrons as a proxy for low-energy neutrino interactions. This poster will present the current status of integrating these machine learning models into the data acquisition (DAQ) system of this detector.

Dalager, Olivia [Fermilab]↗
DOE OSTI2025

Greenhouse Gas Emissions and Decarbonization Potential of Global Fired Clay Brick Production

Fired clay bricks (FCBs) are a dominant building material globally due to their low cost and simplicity of production, especially in low- and middle-income countries. With a projected rising housing demand, commensurate growth in brick demand is anticipated, the production of which could result in significant greenhouse gas (GHG) emissions. Robust models are needed to estimate brick demand and emissions to systematically address decarbonization pathways. Few sources report production values; hence, we present two novel proxy models: (i) a consumption prediction model, relying on country-specific clay extraction data, dynamic building stock modeling, and average material intensity use allowing for projections to 2050; and (ii) a GHG emissions model, using literature-based data and production technology-specific inputs. Based on these models, the current global FCB consumption is estimated as 2.18 Gt annually, resulting in approximately 500 million tCO2e (1% of current global GHG emissions). If unaddressed, this fraction could increase to 3.5-5% in 2050 considering a moderate SSP 2-4.5 climate change mitigation scenario. Consequently, we explored three potential decarbonization pathways: (i) improving energy efficiency; (ii) shifting production to best practices; and (iii) replacing half of FCB demand with hollow concrete blocks, resulting in 27%, 49%, and 51% reduction in GHG emissions, respectively.

Olsson, Josefine A↗
NASA NTRS2004

Optical Strain Measurement of an Inflated Cylinder using Photogrammetry with Application to Scientific Balloons

A study is presented using photogrammetry to measure the biaxial strain in an inflated cylinder. Two cylinders constructed from polyethylene and each approximately 222 mm in diameter and 930 mm in length were studied. The first had a 0.038 mm wall thickness while the second had a 0.02 mm wall thickness. The cylinders were inflated to a maximum pressure of 1379 Pa. The strain was determined from data collected from 60 retro-reflective targets arranged in a 12 x 5 grid. The uncertainty of the measurement system was determined to be 0.08, 0.04 and 0.06 mm in the x, y, and z directions respectively. The hoop and meridional strains determined from displacement data were compared to values obtained from a finite element analysis of a related proxy problem. The predicted hoop strains showed good agreement over the entire range of pressures while the meridional strains showed good agreement at the lower pressures.

Blandino, Joseph R.↗
NASA NTRS2012

Groundwater and Terrestrial Water Storage

Groundwater is a vital resource and also a dynamic component of the water cycle. Unconfined aquifer storage is less responsive to short term weather conditions than the near surface terrestrial water storage (TWS) components (soil moisture, surface water, and snow). However, save for the permanently frozen regions, it typically exhibits a larger range of variability over multi-annual periods than the other components. Groundwater is poorly monitored at the global scale, but terrestrial water storage (TWS) change data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are a reasonable proxy for unconfined groundwater at climatic scales.

Rodell, Matthew↗
NASA NTRS2021

A persistently low level of atmospheric oxygen in Earth’s middle age

Resolving how Earth surface redox conditions evolved through the Proterozoic Eon is fundamental to understanding how biogeochemical cycles have changed through time. The redox sensitivity of cerium relative to other rare earth elements and its uptake in carbonate minerals make the Ce anomaly (Ce/Ce*) a particularly useful proxy for capturing redox conditions in the local marine environment. Here, we report Ce/Ce* data in marine carbonate rocks through 3.5 billion years of Earth’s history, focusing in particular on the mid-Proterozoic Eon (i.e., 1.8 – 0.8 Ga). To better understand the role of atmospheric oxygenation, we use Ce/ Ce* data to estimate the partial pressure of atmospheric oxygen (pO2) through this time. Our thermodynamics-based modeling supports a major rise in atmospheric oxygen level in the aftermath of the Great Oxidation Event (~ 2.4 Ga), followed by invariant pO2 of about 1% of present atmospheric level through most of the Proterozoic Eon (2.4 to 0.65 Ga).

Xiao-Ming Liu↗
NASA NTRS2012

Does Ocean Color Data Assimilation Improve Estimates of Global Ocean Inorganic Carbon?

Ocean color data assimilation has been shown to dramatically improve chlorophyll abundances and distributions globally and regionally in the oceans. Chlorophyll is a proxy for phytoplankton biomass (which is explicitly defined in a model), and is related to the inorganic carbon cycle through the interactions of the organic carbon (particulate and dissolved) and through primary production where inorganic carbon is directly taken out of the system. Does ocean color data assimilation, whose effects on estimates of chlorophyll are demonstrable, trickle through the simulated ocean carbon system to produce improved estimates of inorganic carbon? Our emphasis here is dissolved inorganic carbon, pC02, and the air-sea flux. We use a sequential data assimilation method that assimilates chlorophyll directly and indirectly changes nutrient concentrations in a multi-variate approach. The results are decidedly mixed. Dissolved organic carbon estimates from the assimilation model are not meaningfully different from free-run, or unassimilated results, and comparisons with in situ data are similar. pC02 estimates are generally worse after data assimilation, with global estimates diverging 6.4% from in situ data, while free-run estimates are only 4.7% higher. Basin correlations are, however, slightly improved: r increase from 0.78 to 0.79, and slope closer to unity at 0.94 compared to 0.86. In contrast, air-sea flux of C02 is noticeably improved after data assimilation. Global differences decline from -0.635 mol/m2/y (stronger model sink from the atmosphere) to -0.202 mol/m2/y. Basin correlations are slightly improved from r=O.77 to r=0.78, with slope closer to unity (from 0.93 to 0.99). The Equatorial Atlantic appears as a slight sink in the free-run, but is correctly represented as a moderate source in the assimilation model. However, the assimilation model shows the Antarctic to be a source, rather than a modest sink and the North Indian basin is represented incorrectly as a sink rather than the source indicated by the free-run model and data estimates.

Gregg, Watson↗
NASA NTRS1997

SBUV/2 Long-Term Measurements of Solar Spectral Variability

The NOAA-11 SBUV/2 spectral solar data have been corrected for long-term instrument changes to produce a 5.5 year data record during solar cycle 22 (December 1988 - October 1994). Residual drifts in the data at long wavelengths are +/- 1% or less. At 200-205 nm, where solar variations drive stratospheric photochemistry, these data indicate long-term solar changes of 5-7% from the maximum of Cycle 22 in April 1991 through the end of the NOAA-11 data record. Comparisons of NOAA-11 data with UARS SUSIM and SOLSTICE for the period October 1991 - October 1994, when all 3 instruments were operating simultaneously, show that the observed long-term variations in 200-205 nm irradiance agree to within 2%. This result is consistent with predictions from the Mg-2 proxy index. The SBUV/2 instruments represent a valuable resource for long-term solar UV activity studies because of their overlapping data records. In addition to the NOAA-11 data presented here, the NOAA-9 SBUV/2 instrument began taking data in March 1985 and is still operating, providing a complete record of Cycle 22 behavior from a single instrument. Three additional SBUV/2 instruments are scheduled to be launched between 1997 and 2003, which should permit full coverage of solar cycle 23.

DeLand, Matthew T.↗
NASA NTRS2009

Porting Gravitational Wave Signal Extraction to Parallel Virtual Machine (PVM)

Laser Interferometer Space Antenna (LISA) is a planned NASA-ESA mission to be launched around 2012. The Gravitational Wave detection is fundamentally the determination of frequency, source parameters, and waveform amplitude derived in a specific order from the interferometric time-series of the rotating LISA spacecrafts. The LISA Science Team has developed a Mock LISA Data Challenge intended to promote the testing of complicated nested search algorithms to detect the 100-1 millihertz frequency signals at amplitudes of 10E-21. However, it has become clear that, sequential search of the parameters is very time consuming and ultra-sensitive; hence, a new strategy has been developed. Parallelization of existing sequential search algorithms of Gravitational Wave signal identification consists of decomposing sequential search loops, beginning with outermost loops and working inward. In this process, the main challenge is to detect interdependencies among loops and partitioning the loops so as to preserve concurrency. Existing parallel programs are based upon either shared memory or distributed memory paradigms. In PVM, master and node programs are used to execute parallelization and process spawning. The PVM can handle process management and process addressing schemes using a virtual machine configuration. The task scheduling and the messaging and signaling can be implemented efficiently for the LISA Gravitational Wave search process using a master and 6 nodes. This approach is accomplished using a server that is available at NASA Ames Research Center, and has been dedicated to the LISA Data Challenge Competition. Historically, gravitational wave and source identification parameters have taken around 7 days in this dedicated single thread Linux based server. Using PVM approach, the parameter extraction problem can be reduced to within a day. The low frequency computation and a proxy signal-to-noise ratio are calculated in separate nodes that are controlled by the master using message and vector of data passing. The message passing among nodes follows a pattern of synchronous and asynchronous send-and-receive protocols. The communication model and the message buffers are allocated dynamically to address rapid search of gravitational wave source information in the Mock LISA data sets.

Thirumalainambi, Rajkumar↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

The Impacts of Recent Drought on Fire, Forest Loss, and Regional Smoke Emissions in Lowland Bolivia

In the southern Amazon relationships have been established among drought, human activities that cause forest loss, fire, and smoke emissions. We explore the impacts of recent drought on fire, forest loss, and atmospheric visibility in lowland Bolivia. To assess human influence on fire, we consider climate, fire, and vegetation dynamics in an area largely excluded from human activities since 1979, Noel Kempff Mercado National Park (NK) in northeastern Bolivia. We use data from five sources: the Moderate Resolution Imaging Spectroradiometer Collection 6 active fire product (2001-2015) (MODIS C6), Global Fire Weather Database (GFWED) data (1982-2015), MODIS land cover data (2001-2010), MODIS forest loss data (2000-2012), and the regional extinction coefficient for the southwestern Amazon (i.e., B(sub ext)), which is derived from horizontal visibility data from surface stations at the World Meteorological Organization (WMO) level (1973-2015). The B(sub ext) is affected by smoke and acts as a proxy for visibility and regional fire emissions. In lowland Bolivia from 2001 to 2015, interannual Drought Code (DC) variability was linked to fire activity, while from 1982 to 2015, interannual DC variability was linked to B(sub ext). From 2001 to 2015, the B(sub ext) and MODIS C6 active fire data for lowland Bolivia captured fire seasonality, and covaried between low- and high-fire years. Consistent with previous studies, our results suggest B(sub ext)t can be used as a longer-term proxy of regional fire emissions in southwestern Amazonia. Overall, our study found drought conditions were the dominant control on interannual fire variability in lowland Bolivia, and fires within NK were limited to the Cerrado and seasonally inundated wetland biomes. Our results suggest lowland Bolivian tropical forests were susceptible to human activities that may have amplified fire during drought. Human activities and drought need to be considered in future projections of southern Amazonian fire, in regard to carbon emissions and global climate.

Forest Loss↗
DOE OSTI2025

Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning

Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, eastern North Pacific and western North Pacific. A calibrator is trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

hurricane↗
NASA NTRS2017

High Sensitivity of Gross Primary Production in the Rocky Mountains to Summer Rain

n the catchments of the Rocky Mountains, peak snowpack is declining in response to warmer spring temperatures. To understand how this will influence terrestrial gross primary production (GPP), we compared precipitation data across the intermountain west with satellite retrievals of solar-induced fluorescence (SIF), a proxy for GPP. Annual precipitation patterns explained most of the spatial and temporal variability of SIF, but the slope of the response was dependent on site to site differences in the proportion of snowpack to summer rain. We separated the response of SIF to different seasonal precipitation amounts and found that SIF was approximately twice as sensitive to variations in summer rain than snowpack. The response of peak GPP to a secular decline in snowpack will likely be subtle, whereas a change in summer rain amount will have precipitous effects on GPP. The study suggests that the rain use efficiency of Rocky Mountain ecosystems is strongly dependent on precipitation form and timing.

solar-induced 15 Fuorescence (SIF)↗