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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Sintering of Lunar and Simulant Glass

Most oxygen-extraction techniques are temperature-dependent, with higher temperatures resulting in higher oxygen yield. An example is hydrogen reduction, in which the optimum process temperature is 1050 C. However, glass-rich lunar soil begins to show the effects of sintering at temperatures of 900 C or lower. Sintering welds particles together due to viscous relaxation of the glass in the sample. One approach to avoid problems related to sintering, such as difficulty in removing waste material from the reactor, is to keep the soil in motion. One of several methods being studied to accomplish this is fluidized-bed processing techniques, in which the grains are kept in motion by the action of flowing reductant gas. The spent material can be removed from the chamber while still fluidized, or the fluidizing motion can continue until the material has cooled below approx. 500 C. Until end-to-end prototypes are built that can remove the heated soil, the most practical option is to keep the bed fluidized while cooling the waste material. As ISRU technology advances, another option will become valuable, which is to intentionally sinter the material to a great enough extent that it becomes a brick. The free iron in lunar soil is magnetic, and ferromagnetic bricks can be manipulated by robotic systems using electromagnetic end effectors. Finally, if an electromagnetic field is applied to the soil while the brick is being formed, the brick itself will become a magnet. This property can be used to create self-aligning bricks or other building materials that do not require fasteners. Although sintering creates a challenge for early lunar surface systems, knowledge gained during prototype development will be valuable for the advanced lunar outpost.

Cooper, Bonnie L.↗
NASA NTRS2011

Determining the Local Abundance of Martian Methane and its 13-C/l2-C and D/H Isotopic Ratios for Comparison with Related Gas and Soil Analysis on the 2011 Mars Science Laboratory (MSL) Mission

Understanding the origin of Martian methane will require numerous complementary measurements from both in situ and remote sensing investigations and laboratory work to correlate planetary surface geophysics with atmospheric dynamics and chemistry. Three instruments (Quadrupole Mass Spectrometer (QMS), Gas Chromatograph (GC) and Tunable Laser Spectrometer (TLS)) with sophisticated sample handling and processing capability make up the Sample Analysis at Mars (SAM) analytical chemistry suite on NASA s 2011 Mars Science Laboratory (MSL) Mission. Leveraging off the SAM sample and gas processing capability that includes methane enrichment, TLS has unprecedented sensitivity for measuring absolute methane (parts-per-trillion), water, and carbon dioxide abundances in both the Martian atmosphere and evolved from heated soil samples. In concert with a wide variety of associated trace gases (e.g. SO2, H2S, NH3, higher hydrocarbons, organics, etc.) and other isotope ratios measured by SAM, TLS will focus on determining the absolute abundances of methane, water and carbon dioxide, and their isotope ratios: 13C/12C and D/H in methane; 13C/12C and 18O/17O/16O in carbon dioxide; and 18O/17O/16O and D/H in water. Measurements near the MSL landing site will be correlated with satellite (Mars Express, Mars 2016) and ground-based observations.

Webster, Christopher R.↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2024

Water use and radiation balance of miscanthus and corn on marginal land in the coastal plain region of North Carolina

Abstract Miscanthus is a perennial grass that can yield substantial amounts of biomass in land areas considered marginal. In the Coastal Plain region of North Carolina, marginal lands are typically located in coarse‐textured soils with low nutrient retention and water‐holding capacity, and high erosivity potential. Little is known about miscanthus water use under these conditions. We conducted a study to better understand the efficiency with which miscanthus uses natural resources such as water and radiant energy to produce harvestable dry biomass in comparison to corn, a typical commodity crop grown in the region. We hypothesized that under non‐limiting soil water conditions, miscanthus would have greater available energy and water use rates owing to its greater leaf area, thus leading to greater agronomic yields. Conversely, these effects would be negated under drought conditions. Our measurements showed that miscanthus intercepted more radiant energy than corn, which led to greater albedo (by 0.05), lower net radiation (by 4% or 0.4 MJ m −2 day −1 ), and lower soil heat flux (by 69% or 1.0 MJ m −2 day −1 ) than corn on average. Consequently, miscanthus had greater available energy (by 7% or 0.6 MJ m −2 day −1 ) and water use rates (by 14% or 0.5 mm day −1 ) than corn throughout the growing season on average, which partially confirmed our hypothesis. Greater water use rates and radiation interception by miscanthus did not translate to greater water‐use (1.5 g kg −1 vs. 1.6 g kg −1 ) and radiation‐use (0.9 g MJ −1 vs. 1.1 g MJ −1 ) efficiencies than corn. Compared to literature values, our data indicated that water and radiation availability were not limiting at our study site. Thus, it is likely that marginal land features present at the Coastal Plain region such as low soil fertility and high air temperatures throughout the growing season may constrain agronomic yields even if soil water and radiant energy are non‐limiting.

Carvalho, Henrique D. R.↗
DOE OSTI2024

Short communication: Cosmogenic noble gas depletion in soils by wildfire heating

Measurements of cosmic-ray-produced beryllium-10, neon-21, and helium-3 in quartz in a soil profile from a forested landscape in the Oregon Coast Range show that the cosmogenic noble gases 21 Ne and 3 He are depleted relative to 10Be in the shallow subsurface. The noble gases are mobile in mineral grains via thermally activated diffusion and 10 Be is not, implying that noble gas depletion is the result of surface heating by wildfires and subsequent mixing of partially degassed quartz downward into the soil. Cosmogenic noble gas depletion by wildfire heating of soils is a potential means of estimating wildfire intensity and/or frequency over pre-observational timescales.

58 GEOSCIENCES↗
DOE OSTI2026

Surface Energy Balance System (SEBS) Instrument Handbook

A surface energy balance system (SEBS) has been installed collocated with each deployed eddy correlation flux measurement system (ECOR) after 2010 at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Southern Great Plains (SGP) observatory, North Slope of Alaska (NSA) observatory, Eastern North Atlantic (ENA) observatory, and the first, second, and third ARM Mobile Facilities (AMF1, AMF2, AMF3). A SEBS was also deployed at the Tropical Western Pacific (TWP) site, before it was decommissioned. Data from these sites, including the retired TWP, are available in the ARM Data Center. The SEBS consists of upwelling and downwelling solar and infrared radiometers within one net radiometer, a wetness sensor, and soil temperature, soil moisture, and soil heat flow measurements. The SEBS measurements allow the comparison of ECOR sensible and latent heat fluxes with the energy balance determined from the SEBS and provide information on wetting of the sensors for data quality purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types

Evapotranspiration is the major component of the water cycle, so a correct estimate of this variable is fundamental. The purpose of the present research is to assess the monthly scale accuracy of six meteorological data-based models in the prediction of evapotranspiration (ET) losses by comparing the modelled fluxes with the observed ones from eight sites equipped with eddy covariance stations which differ in terms of vegetation and climate type. Three potential ET methods (Penman-Monteith, Priestley-Taylor, and Blaney-Criddle models) and three actual ET models (the Advection-Aridity, the Granger and Gray, and the Antecedent Precipitation Index method) have been proposed. The findings show that the models performances differ from site to site and they depend on the vegetation and climate characteristics. Indeed, they show a wide range of error values ranging from 0.18 to 2.78. It has been not possible to identify a single model able to outperform the others in each biome, but in general, the Advection-Aridity approach seems to be the most accurate, especially when the model calibration in not carried out. It returns very low error values close to 0.38. When the calibration procedure is performed, the most accurate model is the Granger and Gray approach with minimum error of 0.13 but, at the same time, it is the most impacted by this process, and therefore, in a context of data scarcity, it results the less recommended for ET prediction. The performances of the investigated ET approaches have been furthermore tested in case of lack of measured data of soil heat fluxes and net radiation considering using empirical relationships based on meteorological data to derive these variables. Results show that, the use of empirical formulas to derive ET estimates increases the errors up to 200% with the consequent loss of model accuracy.

net radiation↗
NASA NTRS1981

Use of thermal inertia determined by HCMM to predict nocturnal cold prone areas in Florida

The HCMM transparency scenes for the available winter of 1978-1979 were evaluated; scenes were identified on processed magnetic tapes; other remote sensing information was identified; and a soil heat flux model with variable-depth thermal profile was developed. The Image 100 system was used to compare HCMM and GOES transparent images of surface thermal patterns. Excellent correspondence of patterns was found, with HCMM giving the greater resolution. One image shows details of thermal patterns in Florida that are attributable to difference in near surface water contents. The wide range of surface temperatures attributable to surface thermal inertia that exist in the relatively flat Florida topography is demonstrated.

Allen, L. H., Jr.↗
NASA NTRS1985

Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 2: Application of Equilibrium Evaporation Model to Estimate Evapotranspiration by Remote Sensing Technique

In a humid region like Japan, it seems that the radiation term in the energy balance equation plays a more important role for evapotranspiration then does the vapor pressure difference between the surface and lower atmospheric boundary layer. A Priestley-Taylor type equation (equilibrium evaporation model) is used to estimate evapotranspiration. Net radiation, soil heat flux, and surface temperature data are obtained. Only temperature data obtained by remotely sensed techniques are used.

Kotoda, K.↗
NASA NTRS1985

A monthly forecast experiment: Preliminary report

An experiment on monthly forecasts with eight winter cases is being carried out, using a 1980 general circulation model (GCM), which incorporates a set of dubgrid-scale physics characterized by the Mellon-Yamada turbulence closure (hierarchy level 2.5), the Monin-Obukhov parameterization for the layer next to the ground surface, Manabe's cumulus parameterization, and the soil heat conduction. The sample cases adopted are for the month of January in the years from 1977 to 1983, which include the extraordinarily severe winter of 1977 and the most pronounced E1 Nino year of 1983. Each case is predicted by prescribing the climatologically normal sea surface temperature as the lower boundary conditions and by using an ensemble means of three individual integrations. These integrations start with three different initial conditions based on the Level data generated separately at the Geophysical Fluid Dynamics Laboratory, the National Meteorological Center, and the European Centre for Medium Range Weather Forecasts.

Miyakoda, K.↗
NASA NTRS1987

Aircraft and satellite thermographic systems for wildfire mapping and assessment

Two complementary sensors, the DAEDALUS DEI-1260 Multispectral Scanner aboard the NASA U-2 aircraft and the Advanced Very High Resolution Radiometer aboard National Oceanographic and Atmospheric Administration orbiting satellites were tested for their applicability in monitoring and predicting parameters such as fire location, temperature and rate of spread, soil heating and cooling rates, and plume characteristics and dimensions. In addition, the satellite system was tested for its ability to extend the relationships found between fire characteristics and biospheric consequences to regional and global scales. An overall system design is presented, and special requirements are documented for the application of this system for fire research and management.

Brass, J. A.↗
NASA NTRS1989

Measurements of micrometeorological parameters for testing large scale models

This annual report discusses work accomplished on the FIFE (First International Satellite Land-Surface Climatology) Project. It contains manuscripts and reports during the past year of Grant NAG 5-389. Of its six chapters, three treat soil heat flux, and two deal with information about the FIFE sites. The first chapter on net radiation and the fourth chapter are to be presented at the Agricultural and Forest Meteorology Conference to be held in March 1989 in Charleston, South Carolina.

Kanemasu, E. T.↗
NASA NTRS1991

Use of ground-based remotely sensed data for surface energy balance calculations during Monsoon '90

Surface energy balance was evaluated at a semiarid watershed using direct and indirect measurements of the turbulent fluxes, a remote technique based on measurements of surface reflectance and temperature, and conventional meteorological information. Comparison of remote estimates of net radiant flux and soil heat flux densities with measured values showed errors on the order of +/-40 W/sq m. To account for the effects of sparse vegetation, semi-empirical adjustments to aerodynamic resistance were required for evaluation of sensible heat flux density (H). However, a significant scatter in estimated versus measured latent heat flux density (LE) was still observed, +/-75 W/sq m over a range from 100-400 W/sq m. The errors of H and LE estimates were reduced to +/-50 W/sq m when observations were restricted to clear sky conditions.

Moran, M. S.↗