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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Working Toward a Healthy Planet

Using information from NASA s Earth Science Public Health Applications Program, Dr. Maynard will address how remote sensing data and associated technologies can be used toward a better understanding of the links among human health, the environment and weather/climate - and, how this increased understanding plus improved information sharing can empower local health and environmental decision-makers to better predict climate-related health problems, take preventive measures, and improve response actions. Remotely-sensed data and observations are providing powerful new tools for addressing climate and environment-related human health problems through increased capabilities for monitoring and surveillance of parameters useful to such problems as infectious and vector-borne diseases, air and water quality, harmful algal blooms, UV radiation, contaminant and pathogen transport in air and water, and thermal stress. NASA s multi-disciplinary scientific team is demonstrating how satellites from their unique vantage point in space can serve as sentinels for weather, climate, and health problems through studies on asthma, malaria, Rift Valley Fever, Asian and African dust, and West Nile Virus

Maynard, Nancy G.↗
DOE OSTI2025

Leveraging High-resolution Molecular Composition of Soil Organic Matter to Enhance Carbon Cycling Modeling

Soils store more carbon than the atmosphere and vegetation combined, yet Earth system models still struggle to predict how this vast reservoir will respond to environmental change. A central limitation is that most soil biogeochemical models represent organic matter using bulk conceptual pools or chemically homogeneous fractions, preventing direct use of rapidly expanding molecular-scale datasets. Here we develop and test a new soil decomposition framework that explicitly integrates high-resolution information on organic matter composition. First, we construct a molecularly informed litter decomposition module in which plant inputs are partitioned into five functional compound classes—carbohydrates, proteins, lignin-like aromatics, lipids, and carbonyls—using a molecular mixing model calibrated to solid-state 13 C Nuclear Magnetic Resonance (NMR) spectra. Class-specific kinetics, lignin-dependent physical protection, and substrate-driven microbial carbon use efficiency allow the module to capture metabolic tradeoffs associated with enzyme production and nutrient limitation. We then embed this litter module within a microbially explicit whole-soil model that tracks the transformation of these compound classes through particulate organic matter, dissolved organic matter, mineral-associated organic matter, and microbial biomass. High-resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FTICR-MS) data are used to link internal pools to measurable soil organic matter fractions and to constrain key process parameters. Applications at soil-core and ecosystem scales demonstrate that the new model reproduces observed soil respiration dynamics while providing mechanistic attribution of CO 2 fluxes to specific chemical classes and pools. Compared to existing frameworks such as the Community Land Model soil biogeochemistry module and the Millennial model, our approach maintains competitive predictive skill while substantially improving interpretability and opportunities for data–model integration. This work illustrates a viable pathway for leveraging molecular-scale observations to reduce structural uncertainty in soil carbon–climate feedback projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Kinetic Model for Predicting Trace Gas Uptake and Reaction

A model is developed to describe trace gas uptake and reaction with applications to aerosols and microdroplets. Gas uptake by the liquid is formulated as a coupled equilibria that links gas, surface, and bulk regions of the droplet or solution. Previously, this framework was used in explicit stochastic reaction-diffusion simulations to predict the reactive uptake kinetics of ozone with droplets containing aqueous aconitic acid, maleic acid, and sodium nitrite. With the use of prior data and simulation results, a new equation for the uptake coefficient is derived, which accounts for both surface and bulk reactions. Lambert W functions are used to obtain closed form solutions to the integrated rate laws for the multiphase kinetics; similar to previous expressions that describe Michaelis-Menten enzyme kinetics. Together these equations couple interface and bulk processes over a wide range of conditions and do not require many of the limiting assumptions needed to apply resistor model formulations to explain trace gas uptake and reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Machine-learning-based estimates of global natural vegetated wetland methane emissions (2000–2025)

Wetlands are the largest natural source of atmospheric methane (CH 4 ), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH 4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1° × 1° resolution. We apply this framework to a global dataset of natural vegetated wetland CH 4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000–2020 emissions through 2025. In the test data (∼ 30 % of the total dataset), the emulator achieved a global R 2 of 0.65 ± 0.003 (mean ± 95 % CI, hereafter) and an RMSE of 5.49 ± 0.12×10 -3 Tg CH 4 yr −1 . The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH 4 emissions for 2021–2025 (157.8 ± 2.4 Tg CH 4 yr −1 ) are not significantly higher (∼ 0.05 Tg CH 4 yr −1 ) than the 2000–2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 and 0.35 ± 0.03 Tg CH 4 yr −1 , respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (−0.95 ± 0.19 and -0.11 ± 0.02 Tg CH 4 yr −1 , respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Li, Mengze [National University of Singapore (Sing↗
NASA NTRS2001

Earth: Earth Science and Health

A major new NASA initiative on environmental change and health has been established to promote the application of Earth science remote sensing data, information, observations, and technologies to issues of human health. NASA's Earth Sciences suite of Earth observing instruments are now providing improved observations science, data, and advanced technologies about the Earth's land, atmosphere, and oceans. These new space-based resources are being combined with other agency and university resources, data integration and fusion technologies, geographic information systems (GIS), and the spectrum of tools available from the public health community, making it possible to better understand how the environment and climate are linked to specific diseases, to improve outbreak prediction, and to minimize disease risk. This presentation is an overview of NASA's tools, capabilities, and research advances in this initiative.

Maynard, Nancy G.↗
NASA NTRS1997

Land-use in Amazonia and the Cerrado of Brazil: State of Knowledge and GIS Database

We have assembled datasets to strengthen the LargeScale Biosphere Atmosphere Experiment in Amazonia (LBA). These datasets can now be accessed through the Woods Hole Research Center homepage (www.whrc.org), and will soon be linked to the Pre-LBA homepages of the Brazilian Space Research Institute's Center for Weather and Climate Prediction (Instituto de Pesquisas Espaciais, Centro de Previsao de Tempo e Estudos Climaticos, INPE/CPTEC) and through the Oak Ridge National Laboratory, Distributed Active Archive Center (ORNL/DMC). Some of the datasets that we are making available involved new field research and/or the digitization of data available in Brazilian government agencies. For example, during the grant period we conducted interviews at 1,100 sawmills across Amazonia to determine their production of sawn timber, and their harvest intensities. These data provide the basis for the first quantitative assessment of the area of forest affected each year by selective logging (Nepstad et al, submitted to Nature). We digitized the locations of all of the rural households in the State of Para that have been mapped by the Brazilian malaria combat agency (SUCAM). We also mapped and digitized areas of deforestation in the state of Tocantins, which is comprised largely of savanna (cerrado), an ecosystem that has been routinely excluded from deforestation mapping exercises.

Nepstad, Daniel C.↗
NASA NTRS2004

Improved Mars Upper Atmosphere Climatology

The detailed characterization of the Mars upper atmosphere is important for future Mars aerobraking activities. Solar cycle, seasonal, and dust trends (climate) as well as planetary wave activity (weather) are crucial to quantify in order to improve our ability to reasonably depict the state of the Mars upper atmosphere over time. To date, our best information is found in the Mars Global Surveyor (MGS) Accelerometer (ACC) database collected during Phase 1 (Ls = 184 - 300; F10.7 = 70 - 90) and Phase 2 (Ls = 30 - 90; F10.7 = 90 - 150) of aerobraking. This database (100 - 170 km) consists of thermospheric densities, temperatures, and scale heights, providing our best constraints for exercising the coupled Mars General Circulation Model (MGCM) and the Mars Thermospheric General Circulation Model (MTGCM). The Planetary Data System (PDS) contains level 0 and 2 MGS Accelerometer data, corresponding to atmospheric densities along the orbit track. Level 3 products (densities, temperatures, and scale heights at constant altitudes) are also available in the PDS. These datasets provide the primary model constraints for the new MGCM-MTGCM simulations summarized in this report. Our strategy for improving the characterization of the Mars upper atmospheres using these models has been three-fold : (a) to conduct data-model comparisons using the latest MGS data covering limited climatic and weather conditions at Mars, (b) to upgrade the 15-micron cooling and near-IR heating rates in the MGCM and MTGCM codes for ad- dressing climatic variations (solar cycle and seasonal) important in linking the lower and upper atmospheres (including migrating tides), and (c) to exercise the detailed coupled MGCM and MTGCM codes to capture and diagnose the planetary wave (migrating plus non-migrating tidal) features throughout the Mars year. Products from this new suite of MGCM-MTGCM coupled simulations are being used to improve our predictions of the structure of the Mars upper atmosphere for the upcoming MRO aerobraking exercises in 2006. A Michigan website, containing MTGCM output fields from previous climate simulations, is being expanded to include new MGCM-MTGCM simulations addressing planetary wave influences upon thermospheric aerobraking fields (densities and temperatures). In addition, similar MTGCM output fields have been supplied to the MSFC MARSGRAM - 200X empirical model, which will be used in mission operations for conducting aerobraking maneuvers.

Bougher, S. W.↗
DOE OSTI2022

Confronting the water potential information gap

Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil–plant–atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. Here, we end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the ‘potential’ to substantially reduce conceptual and modelling uncertainties.

58 GEOSCIENCES↗
NASA NTRS2020

Loads Correlation of a Full-Scale Proprotor on the Tiltrotor Test Rig

In 2018, a full-scale isolated proprotor test was conducted in the USAF National Full-Scale Aerodynamics Complex (NFAC) at NASA Ames. The test article was the 3-bladed Bell 699 research rotor derived from the AW609 rotor. For this test, the NASA Tiltrotor Test Rig (TTR) and rotor were installed in the 40- by 80-foot test section. Correlations between the 2018 test data and predictions from the comprehensive analysis CAMRAD II for blade and yoke (flexbeam) loads and rotor torque are presented. The full range of conversion to helicopter modes is covered: conversion 30-, 45-, and 60-deg TTR yaw, and helicopter 75- and 90-deg TTR yaw. The flap moment correlation is reasonable to good; the pitch link load and torsion moment are uniformly underpredicted. The measured 2P lag moment and 2P torque are not captured by the analysis. The inability to predict the 2P component is currently attributed to the analytical assumption of a perfect gimbal, whereas the actual test gimbal may be operating imperfectly and thus introducing the 2P harmonic. A new analytical model that accounts for non-ideal gimbal operation is needed.

Kottapalli, Sesi↗
DOE OSTI2026

NGEE Arctic Integrated Modeling (IM3): Improved snow-vegetation interaction

This data product represents the integration of new code capability for arctic tundra snow-vegetation-terrain interactions into the Energy Exascale Earth System Model (E3SM), through the E3SM Land Model (ELM) component. This code integration is the result of collaborative effort between the NGEE Arctic project and the E3SM project. The NGEE Arctic project developed a total of six Integrated Modeling (IM) modules informed by observations and experiments. New ELM capability represented by this data product (IM3) falls into three categories: 1) Downscaling from gridcell to topographic unit level when working through the existing coupler bypass code. 2) Four new parameters (taper, stocking, bendresist, and vegshape) have been added to ELM to allow for flexible definition of snow-vegetation interactions. 3) Vegshape and bendresist parameters are used to calculate the fraction of leaf area and/or stem area buried by snow for a given snow depth. This data record consists of a single document (pdf format) that describes the theoretical basis for the snow-vegetation-terrain interactions added to ELM, and describes the modifications made to the ELM code. The Methods section of this metadata record includes a link to the public E3SM code repository where the exact code modifications as integrated in E3SM can be accessed. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Thornton, Peter E [ORNL] (ORCID:0000000247595158)↗
DOE OSTI2025

Life cycle greenhouse gas emissions and carbon intensity of U.S. fuel use and projection for the next 10 years-based on built capacity and expansion plans

The U.S. Inflation Reduction Act of 2022 supports biofuel production expansion through the 45Z clean fuel production tax credit, replacing previous 40A and 40B credits. This follows on the Renewable Fuel Standard from the Energy Policy Act of 2005 and its expansion in 2007. States like California, Oregon, and Washington also offer clean fuel credits. Meanwhile, federal agencies, including the U.S. Department of Energy, have advanced alternative fuel technologies through research and development funding. The surging interest in the biofuel industry has spurred the demand for biofuel supplies in the markets, although achieving profitability for advanced biofuels and low-carbon e-fuels remains challenging. This study aims to track U.S. alternative fuel production capacity expansion plans over the next 10 years and estimate impacts on greenhouse gas (GHG) emissions. By tracking built capacity and industry announcements of planned expansion, this study complements other studies which use models to predict changes in energy technologies and the associated GHG implications. Modeled projections of future technologies are often criticized for over or underestimating the cost and potential role of new technologies. The study focuses on sustainable aviation fuel, renewable diesel, ethanol, biodiesel, and renewable natural gas. Using facility-level data, we conducted a bottom-up analysis linking biofuel production pathways with corresponding pathways and parameterizations in the Argonne R&D GREET model. Results indicate that biofuel capacity could reach 3.8 exajoules in 2035, potentially reducing U.S. GHG emissions by 179 million tonnes, including the full life cycle. This corresponds to a 20% reduction in transportation and 5% in industry sector emissions by 2035, or a 3.6% reduction in economy-wide emissions. Overall, this study shows that while biofuel production capacity in the U.S. is expanding, the capacities remain limited compared to fuel demand. Uncertainty regarding the durability and extension of incentives may be dampening the pace of growth. Meanwhile, demonstrating the commercial potential for alternative fuels and climbing the learning curve for new technologies could lead to an increased pace of expansion in later years. This study offers insights for bioenergy stakeholders, highlighting biofuel technologies' contribution to U.S. energy system and emissions reduction over time based on producers' plans.

Biofuel Producers↗
NASA NTRS1990

Operational testing of a figure of merit for overall task performance

An overall indicator or figure of merit (FOM), for the quality of pilot performance is needed to define optimal workload levels, predict system failure, measure the impact of new automation in the cockpit, and define the relative contributions of subtasks to overall task performance. A normative FOM was developed based on the calculation of a standard score for each component of a complex task. It reflected some effects, detailed in an earlier study, of the introduction of new data link technology into the cockpit. Since the technique showed promise, further testing was done. A new set of data was obtained using the recently developed Multi-Attribute Task Battery. This is a complex battery consisting of four tasks which can be varied in task demand, and on which performance measures can be obtained. This battery was presented to 12 subjects in a 20 minute trial at each of three levels of workload or task demand, and performance measures collected on all four tasks. The NASA-TLX workload rating scale was presented at minutes 6, 12, and 18, of each trial. A figure of merit was then obtained for each run of the battery by calculating a mean, SD, and standard score for each task. Each task contributed its own proportion to the overall FOM, and relative contributions changed with increasing workload. Thus, the FOM shows the effect of task changes, not only on the individual task that is changed, but also on the performance of other tasks and of the whole task. The cost to other tasks of maintaining constant performance on an individual task can be quantified.

Lemay, Moira↗
NASA NTRS1987

An expert system for scheduling requests for communications links between TDRSS and ERBS

An ERBS-TDRSS Contact Planning System (ERBS-TDRSS CPS) is described which uses a graphics interface and the NASA Transportable Inference Engine. The procedure involves transfer of the ERBS-TDRSS Ground Track Orbit Prediction data to the ERBS flight operations area, where the ERBS-TDRSS CPS automatically generates requests for TDRSS service. As requested events are rejected, alternative context sensitive strategies are employed to generate new requested events until a schedule is completed. A report generator builds schedule requests for separate ERBS-TDRSS contacts.

Mclean, David R.↗
NASA NTRS1987

An expert system for scheduling requests for communications Links between TDRSS and ERBS

An ERBS-TDRSS Contact Planning System (ERBS-TDRSS CPS) is described which uses a graphics interface and the NASA Transportable Interference Engine. The procedure involves transfer of the ERBS-TDRSS Ground Track Orbit Prediction data to the ERBS flight operations area, where the ERBS-TDRSS CPS automatically generates requests for TDRSS service. As requested events are rejected, alternative context sensitive strategies are employed to generate new requested events until a schedule is completed. A report generator builds schedule requests for separate ERBS-TDRSS contacts.

Mclean, David R.↗
NASA NTRS1990

A new approach to the analysis of Mira light curves

Two different but complementary methods for predicting Mira luminosities are presented. One method is derived from a Fourier analysis, it requires performing deconvolution, and its results are not certain due to the inherent instability of deconvolution problems. The other method is a learning method utilizing artificial intelligence techniques where a light curve is presented as an ordered sequence of pseudocycles, and rules are learned by linking the characteristics of several consecutive pseudocycles to one characteristic of the future cycle. It is observed that agreement between these methods is obtainable when it is possible to eliminate similar false frequencies from the preliminary power spectrum and to improve the degree of confidence in the rules.

Mennessier, M. O.↗
DOE OSTI2024

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

An energetic link between order and strength in metals: A nanocrystalline strength limit in high-entropy alloys and intermetallic compounds

The metallurgy and materials communities have long understood and exploited fundamental links between chemical and structural ordering in metallic solids to tailor their mechanical properties. We extend these ideas to include prediction of the nanocrystalline strength limit in high-entropy alloys and intermetallic compounds, where a breakdown occurs in the classical Hall-Petch strengthening behavior. The highest reported strength achievable through alloying has rapidly climbed and given rise to new classifications of materials with extraordinary properties, with a notable case being nanocrystalline metals. High-entropy alloys (chemically disordered, concentrated solid solutions) and intermetallic compounds are two boundary cases of how tailored order can be used to manipulate mechanical behavior. Here, we show that the complex electronic-structure mechanisms governing the peak strength of alloys and pure metals can be reduced to a few physically meaningful parameters based on their atomic arrangements and used – with no fitting parameters – to predict the maximum strength of these materials. This includes a generalized energy-based accounting for the degree of structural and chemical ordering that allows for rapid and reasonably accurate prediction of peak strength (validated in the nanocrystalline limit) as a function of temperature. Predictions of maximum strength based on the activation energy (with all materials properties derived from DFT calculations or experiments) for a stress-driven transition to an amorphous state is shown to accurately describe the breakdown in Hall-Petch behavior at the smallest crystallite sizes for pure metals, intermetallic compounds, high-entropy alloys, and metallic glasses. Further, this activation energy is also shown to be directly proportional to interstitial electronic charge density, which is a good predictor of ductility, stiffness (moduli), and phase stability in high-entropy alloys and solid metals generally. The proposed framework suggests the possibility of coupling ordering and intrinsic strength to mechanisms like dislocation nucleation, hydrogen embrittlement, and transport properties, such as through correlations between the activation energies for amorphization with stacking-fault and grain boundary energies. It additionally opens the prospect for greatly accelerated structural materials design and development to address materials challenges limiting more sustainable and efficient use of energy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modelling the Liquid Waste Operation at the Savannah River Site

Modeling Successes Dounreay – Site Closure Optimization and Risk Reduction Re-structuring and streamlining a complex project plan to achieve site decommissioning in reduced time and at reduced cost, through automated task prioritization. Idaho AMWTP – Multi-stream Optimization Speeding up a complex facility with many process lines, by utilizing all of the process lines more efficiently and reducing overall plant downtime, by coordinating repairs and shutdowns. AWE –Waste Repackaging and Relocation Relocating schedule for waste material from an old storage facility to a new purpose-built one, safely, and in a timely fashion that removed the need to reassure the safety of the old facility. Sellafield – Effluent, Sludge and HAW Treatment Improving a wide range of chemical engineered processes including aqueous effluent treatment, chemically-reactive sludge management and waste vitrification processing. Strategic Petroleum Reserve – Cavern Management Linking chemistry models of petroleum prediction, through engineering cavern management to financial modeling of gasoline sales to benefit US government

Jung, Andrew W.↗