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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Radiative transfer in shrub savanna sites in Niger: Preliminary results from HAPEX-Sahel. Part 1: Modelling surface reflectance using a geometric-optical approach

To use optical remote sensing to monitor land surface-climate interactions over large areas, algorithms must be developed to relate multispectral measurements to key variables controlling the exchange of matter (water, carbon dioxide) and energy between the land surface and the atmosphere. The proportion of the ground covered by vegetation and the interception of photosynthetically active radiation (PAR) by vegetation are examples of two variables related to evapotranspiration and primary production, respectively. An areal-proportion model of the multispectral reflectance of shrub savanna, composed of scattered shrubs with a grass, forb or soil understory, predicted the reflectance of two 0.5 km(exp 2) sites as the area-weighted average of the shrub and understory or 'background' reflectances. Although the shaded crown and shaded background have darker reflectances, ignoring them in the area-weighted model is not serious when shrub cover is low and solar zenith angle is small. A submodel predicted the reflectance of the shrub crown as a function of the foliage reflectance and amount of plant material within the crown, and the background reflectance scattered or transmitted through canopy gaps (referred to as a soil-plant 'spectral interaction' term). One may be able to combine these two models to estimate both the fraction of vegetation cover and interception of PAR by green vegetation in a shrub savanna.

Franklin, Janet↗
NASA NTRS1995

Marshall Space Flight Center surface modeling and grid generation applications

The Solid Rocket Motors (SRM) used by NASA to propel the Space Shuttle employ gimballing nozzles as a means for vehicular guidance during launch and ascent. Gimballing a nozzle renders the pressure field of the exhaust gases nonaxisymmetric. This has two effects: (1) it exerts a torque and side load on the nozzle; and (2) the exhaust gases flow circumferentially in the aft-dome region, thermally loading the flexible boot, case-to-nozzle joint, and casing insulation. The use of CFD models to simulate such flows is imperative in order to assess SRM design. The grids for these problems were constructed by obtaining information from drawings and tabulated coordinates. The 2D axisymmetric grids were designed and generated using the EZ-Surf and GEN2D surface and grid generation codes. These 2D grids were solved using codes such as FDNS, GASP, and MINT. These axisymmetric grids were rotated around the center-line to form 3D nongimballed grids. These were then gimballed around the pivot point and the gaps or overlaps resurfaced to obtain the final domains, which contained approximately 366,000 grid points. The 2D solutions were then rotated and manipulated as appropriate for geometry and used as initial guesses in the final solution. The analyses were used in answering questions about flight criteria.

Williams, Robert W.↗
NASA NTRSDate not supplied

Machine Learning Airport Surface Model

Future needs of the National Airspace System require decision support tools to adopt a service-oriented architecture in alignment with the FAA’s vision for an Info-Centric NAS. To achieve this, many existing systems will need to undergo a digital transformation from a monolithic decision support tool to a service-oriented architecture where individual services are exposed through well defined Application Programming Interfaces (APIs). To enable this transformation, NASA has developed the Digital Information Platform as a cloud based foundation for development of aviation services with a special focus towards Artificial Intelligence and Machine Learning (ML) services. This paper describes the work required for the transformation of NASA’s legacy surface management system to a real-time ML based decision support system deployed in the cloud. Details of the Machine Learning Operations (MLOps) infrastructure and best practices are described which enabled the end-toend lifecycle management of ML within an integrated software system. Validation results are provided from an operational field evaluation where performance was benchmarked against the legacy approach.

Jeremy Coupe↗
DOE OSTI2022

Adsorption and exchange reactions of iodine molecules at the alumina surface: modelling alumina-iodine reaction mechanisms

Harnessing aluminum oxidation energy requires navigating the particle's passivation shell composed of alumina. The shell is a barrier to aluminum oxidation but can also exothermically react with halogenated species and therefore contribute to the overall energy generated during aluminum particle combustion. Fluorination reactions with alumina have been studied because fluorine is abundant in binder formulations that commonly surround aluminum particles in an energetic mixture. However, iodine has emerged as an alternative halogenated-based binder or oxidizer because iodine gas provides ancillary benefits such as chemical neutralization of biological agents or sterilization of contaminated environments. Furthermore, this study used density functional theory (DFT) calculations to evaluate potential reaction pathways for aluminum–iodine combustion. Relative to fluorinated fragments such as HF and F – , the adsorption energies associated with HI and I– are nearly triple the exchange reaction energy available from fluorination reactions with alumina (–189 and –278 kJ mol –1 for HI and I – , respectively). However, exchange reactions between iodinated species and the alumina surface are energetically unfavorable. These results explain that through adsorption, alumina surface exothermic reactions with iodine are more energetic than with fluorine fragments. Experiments performed with differential scanning calorimetry (DSC) confirm the higher magnitude of energy generated for iodination compared with fluorination reactions with alumina. Additionally, strong adsorption energies can promote synthesis of new shell chemistries. Adsorption in solution will promote alumina dissolution and iodine precipitation reactions to produce hydroxyl complexes and iodinated species synthesized on the surface of the particle, thereby replacing alumina with alternative passivation shell chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape

Arctic warming is altering vegetation and carbon dynamics with global implications, yet Earth System Model (ESM) predictions in the Arctic remain highly uncertain, in part due to historically limited data for model parameterization and validation. As such, ESMs typically represent Arctic ecosystems in an oversimplified manner. Recently, nine plant functional types (PFTs) designed to realistically represent tundra vegetation were integrated into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) and parameterized using plot-scale observations from a single site. Additional evaluation was needed to determine their transferability across the Arctic. Here, in this study, we evaluated whether refined representation of tundra vegetation improved model accuracy by conducting spatially explicit 100 × 100 m resolution ELM simulations on Alaska's Seward Peninsula. Simulations with the default two-PFT configuration and with the nine Arctic-specific PFTs were benchmarked against observations of net ecosystem exchange, gross primary production, and aboveground biomass from multiple data streams including an eddy covariance flux tower, flux chambers, and aircraft and unoccupied aerial system hyperspectral remote sensing. Evaluation revealed that Arctic-specific PFT simulations produced more realistic landscape-level carbon exchanges, and better captured observed heterogeneity in biomass and productivity, explaining 60%–70% of spatial variance (R 2 = 0.6–0.7) compared to just 12%–18% (R 2 = 0.12–0.18) with the default configuration. However, the refined model failed to reproduce observed aboveground biomass for highly productive alder-willow communities, requiring further evaluation of carbon allocation parameterizations for tall shrubs that are increasingly expanding across tundra landscapes. Our results demonstrate that enhanced representation of vegetation heterogeneity boosts predictive understanding of tundra carbon dynamics, facilitating regional to pan-Arctic model and remote-sensing scaling.

Murphy, Bailey A. [Oak Ridge National Laboratory (↗
DOE OSTI2022

Modeling surface spin polarization on ceria-supported Pt nanoparticles

In this work, we employ density functional theory simulations to investigate possible spin polarization of CeO 2 -(111) surface and its impact on the interactions between a ceria support and Pt nanoparticles. With a Gaussian type orbital basis, our simulations suggest that the CeO 2 -(111) surface exhibits a robust surface spin polarization due to the internal charge transfer between atomic Ce and O layers. In turn, it can lower the surface oxygen vacancy formation energy and enhance the oxide reducibility. We show that the inclusion of spin polarization can significantly reduce the major activation barrier in the proposed reaction pathway of CO oxidation on ceria-supported Pt nanoparticles. For metal-support interactions, surface spin polarization enhances the bonding between Pt nanoparticles and ceria surface oxygen, while CO adsorption on Pt nanoparticles weakens the interfacial interaction regardless of spin polarization. However, the stable surface spin polarization can only be found in the simulations based on the Gaussian type orbital basis. Furthermore, given the potential importance in the design of future high-performance catalysts, our present study suggests a pressing need to examine the surface ferromagnetism of transition metal oxides in both experiment and theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models

Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Activation of propane on Ag–PdO(101) model surfaces

Oxidation of alkanes remains a central challenge in catalysis due to the high activation barriers of C–H bonds and the thermodynamic favorability of complete oxidation. Palladium oxide (PdO), particularly its (101) facet, is known for its high reactivity in alkane oxidation, which is attributed to its coordinatively unsaturated palladium (Pd) and O atoms. In this study, we investigate the effect of silver (Ag) incorporation on the oxidation behavior of propane over PdO(101) using temperature-programmed reaction spectroscopy (TPRS) under controlled conditions. While pristine PdO(101) exhibits complete oxidation of propane with CO₂ and H₂O desorption at high temperatures (approximately 475 K), Ag incorporation induces a new CO₂ desorption peak at significantly lower temperatures (approximately 330 K). This shift is attributed to the formation of new active sites at the Ag–PdO(101) interface. Quantitative analysis reveals that low-temperature activity correlates with Ag coverage, while overall CO₂ production decreases, suggesting a redistribution of reactivity rather than an increase in active surface area. Activation energy estimations using the Redhead method confirm that C–H bond activation becomes more facile at the interface, with a 46 kJ/mol reduction compared to pristine PdO(101). These findings demonstrate that incorporating a less reactive metal such as Ag into PdO surfaces not only modifies the reaction energetics but also enables the design of bimetallic catalysts with improved selectivity for partial oxidation reactions.

Chemistry↗