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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Safe and efficient surface operations↗
DOE OSTI2024

Enabling the broader adoption of fusion simulation on complex geometry

This project addressed a key barrier to advanced fusion and nuclear simulation: the difficulty of performing high-fidelity Monte Carlo neutronics directly on complex, real-world CAD geometry. Traditional workflows require engineers to rebuild CAD models as simplified constructive solid geometry, a time-consuming and error-prone process that limits design iteration and broader adoption of simulation tools. The goal of this Phase I SBIR was to make CAD-based neutronics practical, accessible, and robust for industrial and research users. During the project, Coreform significantly enhanced the Direct Accelerated Geometry Monte Carlo (DAGMC) workflow and fully integrated it into Coreform Cubit as a first-class capability. Major achievements include optimized material assignment and surface meshing workflows, substantial performance improvements to geometry imprinting and preparation, native export of DAGMC models, and new visualization tools to support OpenMC source definition and lost-particle debugging. Coreform also expanded Cubit’s capabilities as a full OpenMC preprocessor, including the ability to convert OpenMC constructive solid geometry models back into CAD for visualization, multiphysics coupling, and debugging. In collaboration with Argonne National Laboratory, the project delivered comprehensive new DAGMC documentation and training materials, transforming DAGMC from a research-oriented tool into a production-ready workflow. Results were disseminated through tutorials, conference training, and multiple well-attended webinars demonstrating integrated CAD-based neutronics and multiphysics workflows. Overall, this project demonstrated that high-fidelity Monte Carlo simulations can be performed directly on complex CAD geometry, reducing setup time, improving usability, and enabling faster, more informed design decisions for fusion and nuclear energy systems.

42 ENGINEERING↗
DOE OSTI2024

Engineering Active Sites of Metal/Metal Oxide Catalysts by Oxide Ligand Overlayers

Abstract Engineering sites of supported metal catalysts is essential to enhancing activity and selectivity. Such enhancement is typically achieved by particle size modification, surface alloying, or attaching molecular ligands. Yet, control strategies for complex, multifunctional molecules and catalysts, where selectivity is crucial, are lacking. Here, we demonstrate that submonolayer WO x with tunable coverage preferentially decorates well‐coordinated Pt terrace sites as a stable ligand. By combining experimental kinetics with probe molecules, in situ spectroscopies, and first‐principles modeling, we show that the WO x coverage on Pt modifies the metal‐to‐acid site balance while retaining the acid strength intact and results in optimal reactivity for metal‐acid catalyzed reactions at a specific metal, size, and support‐dependent WO x coverage. The oxide can also alter the reactant adsorption mode, reversing selectivity and pathways from terrace‐ to step‐dominated, as evidenced in furfural decarbonylation and hydrogenation. The insights open avenues for improving metal/metal oxide catalysts beyond the specific system.

Zhou, Jiahua↗
DOE OSTI2020

The influence of wintertime SST variability in the Western North Pacific on ENSO diversity

Abstract Sea surface temperature anomalies (SSTa) in the Western North Pacific (WNP) have been linked to the development of El Niño Southern Oscillation (ENSO) events a full year in advance. However, the contribution of the WNP precursor to the temporal evolution and spatial complexity of ENSO remains unclear. Using the preindustrial experiment of the Community Earth System Model as the control climate, a partially coupled experiment is conducted in which WNP SSTa are restored to the model climatology. By comparing the perturbed experiment to the control, we are able to clearly characterize ENSO’s response to WNP SST variability. We find that the WNP is predominantly linked to eastern Pacific (EP) ENSO events. Without SST variability in the WNP, central Pacific (CP) ENSO events are more likely to develop. This variation in ENSO flavor is controlled by how the WNP projects onto wind stress anomalies in the western equatorial Pacific, which in turn impacts the discharge and recharge of ocean heat during the ENSO cycle. Specifically, the removal of SSTa in the WNP weakens the buildup of ocean heat in the western equatorial Pacific, which then hinders the development of EP-type events.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Comparison of Observations and Predictions of Daytime Planetary-Boundary-Layer Heights and Surface Meteorological Variables in the Columbia River Gorge and Basin During the Second Wind Forecast Improvement Project

The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗
DOE OSTI2024

Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

The complex reaction network of catalytic biomass conversions often involves hundreds of surface intermediates and thousands of reaction steps, greatly hindering the rational design of metal catalysts for these conversions. Here, we present a framework of machine learning (ML)-accelerated first-principles studies for the hydrodeoxygenation (HDO) of propanoic acid over transition metal surfaces. The microkinetic model (MKM) is initially parametrized by ML-predicted energies and iteratively improved by identifying the rate-determining species and steps (RDS), computing their energies by density functional theory (DFT), and reparameterizing the MKM until all the RDS are computed by DFT. The Gaussian process (GP) model performs significantly better than the linear ridge regression model for predicting both the adsorption free energies and transition state free energies. Parameterized with energies from the GP model, only 5–20% of the full reaction network has to be computed by DFT for the MKM to possess DFT-level accuracy for the TOF and dominant reaction pathway. While the linear ridge regression model performs worse than the GP model, its performance is greatly improved when only transition states are predicted by the regression model and adsorption energies are computed by DFT. Overall, we find that a high accuracy in adsorption free energies is more important for a reliable MKM than a high accuracy in TS free energies. Lastly, based on the GP model with GOH and GCHCHCO as catalyst descriptors, we build two-dimensional volcano plots in activity and selectivity that can help design promising alloy catalysts for HDO reactions of organic acids.

adsorption↗
DOE OSTI2021

Synthesis and Structure–Activity Characterization of a Single-Site MoO 2 Catalytic Center Anchored on Reduced Graphene Oxide

Molecularly derived single-site heterogeneous catalysts can bridge the understanding and performance gaps between conventional homogeneous and heterogeneous catalysis, guiding the rational design of next-generation catalysts. While impressive advances have been made with well-defined oxide supports, the structural complexity of other supports and the nature of the grafted surface species present an intriguing challenge. In this study, single-site Mo(=O) 2 species grafted onto reduced graphene oxide (rGO/MoO 2 ) are characterized by XPS, DRIFTS, powder XRD, N 2 physisorption, NH 3 -TPD, aqueous contact angle, active site poisoning assay, Mo EXAFS, model compound single-crystal XRD, DFT, and catalytic performance. NH 3 -TPD reveals that the anchored MoO 2 moiety is not strongly acidic, while Mo 3d 5/2 XPS assigns the oxidation state as Mo(VI), and XRD shows little rGO periodicity change on MoO 2 grafting. Contact angle analysis shows that MoO 2 grafting consumes rGO surface polar groups, yielding a more hydrophobic surface. The rGO/MoO 2 DRIFTS assigns features at 959 and 927 cm –1 to the symmetric and antisymmetric Mo=O stretching modes, respectively, of an isolated cis-(O=Mo=O) moiety, in agreement with DFT computation. Moreover, the Mo EXAFS rGO/MoO 2 structural data are consistent with isolated (C–O) 2– Mo(=O) 2 species having two Mo=O bonds and two Mo–O bonds at distances of 1.69(3) and 1.90(3) Å, respectively. rGO/MoO 2 is also more active than the previously reported AC/MoO 2 catalyst, with reductive carbonyl coupling TOFs approaching 1.81 × 10 3 h –1 . rGO/MoO 2 is environmentally robust and multiply recyclable with 69 ± 2% of the Mo sites catalytically significant. Altogether, rGO/MoO 2 is a structurally well-defined and versatile single-site Mo(VI) dioxo heterogeneous catalytic system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel

Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries and electrocatalysts. Experimental characterizations of such materials by operando microscopy produce rich image datasets, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

The morphology and evolution of Ganymede and Calisto

Images of Ganymede and Callisto, Jupiter's two largest moons, among the largest known predominantly icy planetary objects, were obtained by the two Voyager spacecraft. Voyager images were used to investigate the surface characteristics, geologic processes, and internal evolution of Ganymede and Callisto. Ganymede shows two principal types of terrain: one dark, old, and heavily cratered; and another brighter, younger, and characterized by complex patterns of grooves. Voyager imagers were used to determine photometric properties of surface features on both bodies at phase angles up to 120 deg. Surface temperatures are calculated for the major terrain types. Callisto is found to be somewhat warmer than Ganymede. The temperature difference between grooved and cratered terrain on Ganymede is small. A model for the origin of grooved terrain is considered in which extension creates broad, downdropped rift zones in the crust that are filled with water or ice from below.

Squyres, S. W.↗
NASA NTRS1986

Plasma interactions and surface/material effects

A discussion on plasma interactions and surface/material effects is summarized. The key issues in this area were: (1) the lack of data on the material properties of common spacecraft surface materials; (2) lack of understanding of the contamination and decontamination processes; and (3) insufficient analytical tools to model synergistic phenomena related to plasma interactions. Without an adequate database of material properties, accurate system performance predictions cannot be made. The interdisciplinary nature of the surface-plasma interactions area makes it difficult to plan and maintain a coherent theoretical and experimental program. The shuttle glow phenomenon is an excellent example of an unanticipated, complex interaction involving synergism between surface and plasma effects. Building an adequate technology base for understanding and predicting surface-plasma interactions will require the coordinated efforts of engineers, chemists, and physicists. An interdisciplinary R and D program should be organized to deal with similar problems that the space systems of the 21st century may encounter.

Mandel, M.↗
NASA NTRS1987

Tribology theory versus experiment

Tribology, the study of friction and wear of materials, has achieved a new interest because of the need for energy conservation. Fundamental understanding of this field is very complex and requires a knowledge of solid-state physics, material science, chemistry, and mechanical engineering. This paper is meant to be didactic in nature and outlines some of the considerations needed for a tribology research program. The approach is first to present a simple model, a field emission tip in contact with a flat surface, in order to elucidate important considerations, such as contact area, mechanical deformations, and interfacial bonding. Then examples from illustrative experiments are presented. Finally, the current status of physical theories concerning interfacial bonding are presented.

Ferrante, John↗
NASA NTRS1992

Greenhouse warming by minor gases on early Mars

The early atmospheres of Earth and Mars were non-oxidizing mixtures likely derived from volcanic outgassing of a silicate mantle, with some fraction of the volatiles also contributed by impacting comets and meteorites. Here the authors investigate the potential of minor atmospheric constituents produced by ultraviolet and auroral chemistry to contribute to the thermal opacity of early Earth and Mars atmospheres. Using a very simple two-stream thermal opacity model, the authors show that HCN at 10 parts per million (ppm) and N2O at 100 ppm can each block radiation in thermal infrared windows sufficiently to increase the surface temperature by 7 K separately, or 14 K together. Small quantities of other species are also produced in such experiments. Some of these have especially complex infrared spectra and should be further investigated for their potential to help close windows in the CO2 + H2O infrared transmission. Enhancement of greenhouse warming by minor atmospheric species different from those present in today's atmosphere may have played important roles in the climate of early Earth and Mars.

Heinrich, M. N.↗
NASA NTRS2009

Enceladus: An Active Cryovolcanic Satellite

Enceladus is one of the most remarkable satellites in the solar system, as revealed by Cassini's detection of active plumes erupting from warm fractures near its south pole. This discovery makes Enceladus the only icy satellite known to exhibit ongoing internally driven geological activity. The activity is presumably powered by tidal heating maintained by Enceladus 2:1 mean-motion resonance with Dione, but many questions remain. For instance, it appears difficult or impossible to maintain the currently observed radiated power (probably at least 6 GW) in steady state. It is also not clear how Enceladus first entered its current self-maintaining warm and dissipative state initial heating from non-tidal sources is probably required. There are also many unanswered questions about Enceladus interior. The silicate fraction inferred from its density of 1.68 g per cubic centimeter is probably differentiated into a core, though we have only indirect evidence for differentiation. Above the core there is probably a global or regional water layer, inferred from several models of tidal heating, and an ice shell thick enough to support the ~1 kilometer amplitude topography seen on Enceladus. It is possible that dissipation is largely localized beneath the south polar region. Enceladus surface geology, ranging from moderately cratered terrain to the virtually crater-free active south polar region, is highly diverse, tectonically complex, and remarkably symmetrical about the rotation axis and the direction to Saturn. South polar activity is concentrated along the four tiger stripe fractures, which radiate heat at temperatures up to at least 167 K and are the source of multiple plumes ejecting ~200 kilograms per second of H2O vapor along with significant N2 (or C2H4), CO2, CH4, NH3, and higher-mass hydrocarbons. The escaping gas maintains Saturn's neutral gas torus, and the plumes also eject a large number of micron-sized H2O ice grains that populate Saturn's E-ring. The mechanism that powers the plumes is not well understood, and whether liquid water is involved is a subject of active debate (but likely nonetheless). Enceladus provides a promising potential habitat for life in the outer solar system, and the active plumes allow the unique opportunity for direct sampling of that zone. Enceladus is thus a prime target for Cassini's continued exploration of the Saturn system, and will be a tempting target for future missions.

Spencer, J. R.↗
NASA NTRS2017

Modeling a Packed Bed Reactor Utilizing the Sabatier Process

A numerical model is being developed using Python which characterizes the conversion and temperature profiles of a packed bed reactor (PBR) that utilizes the Sabatier process; the reaction produces methane and water from carbon dioxide and hydrogen. While the specific kinetics of the Sabatier reaction on the RuAl2O3 catalyst pellets are unknown, an empirical reaction rate equation1 is used for the overall reaction. As this reaction is highly exothermic, proper thermal control is of the utmost importance to ensure maximum conversion and to avoid reactor runaway. It is therefore necessary to determine what wall temperature profile will ensure safe and efficient operation of the reactor. This wall temperature will be maintained by active thermal controls on the outer surface of the reactor. Two cylindrical PBRs are currently being tested experimentally and will be used for validation of the Python model. They are similar in design except one of them is larger and incorporates a preheat loop by feeding the reactant gas through a pipe along the center of the catalyst bed. The further complexity of adding a preheat pipe to the model to mimic the larger reactor is yet to be implemented and validated; preliminary validation is done using the smaller PBR with no reactant preheating. When mapping experimental values of the wall temperature from the smaller PBR into the Python model, a good approximation of the total conversion and temperature profile has been achieved. A separate CFD model incorporates more complex three-dimensional effects by including the solid catalyst pellets within the domain. The goal is to improve the Python model to the point where the results of other reactor geometry can be reasonably predicted relatively quickly when compared to the much more computationally expensive CFD approach. Once a reactor size is narrowed down using the Python approach, CFD will be used to generate a more thorough prediction of the reactors performance.

Reactor↗
NASA NTRS2023

Neural Radiance Fields for Shadow Removal and Relighting of Satellite Imagery

Multi-view stereo photogrammetric techniques have been conventionally utilized to generate Global Digital Elevation Models (GDEM) of planetary and lunar surfaces. However, these methods, relying on conventional feature detectors, are often subject to inaccuracies caused by changes in lighting conditions, including diffuse reflection and harsh shading. This has limited the ability of these methods to accurately reconstruct shadowed regions in orbital imagery, such as highly shaded urban areas and the permanently shadowed regions (PSRs) located in the lunar surface, which are critical targets for NASA's Artemis program. Neural Radiance Fields (NeRFs) offer a novel solution to these limitations by breaking away from traditional photogrammetric assumptions of ridged, opaque surfaces. NeRFs are capable of reconstructing 3D objects with variably transmissive properties, such as fluids, PSRs, and reflective surfaces. In this work, we demonstrate the robustness of NeRFs in generating high-fidelity 3D models of terrain from highly shaded orbital imagery acquired from small satellites in low earth orbit (LEO). Our results showcase the ability of NeRF to overcome the limitations of traditional photogrammetric methods and provide a promising solution for reconstructing complex scenes in challenging lighting conditions.

Caleb Ashmore Adams↗
DOE OSTI2021

M3 milestone: Advanced contact 2021

This report describes advanced contact approaches and implementations in FY21 for use in emerging reactors with geometries that differ from LWR/EBR-II cylindrical fuel. Contact remains a challenging and crucially important component of our modeling toolkit. While significant improvements have been made through mortar and automatic differentiation, we explored advanced approaches (i.e., dual mortar) that yields better conditioned systems and will improve the robustness and performance of BISON contact problems. This effort is relevant to emerging reactor types, such as micro reactors and the corresponding complex and numerous contact surfaces. In this report, improvements of the mortar-based contact formulation are described. Benchmark problems and BISON assessment cases are included to demonstrate the improvements and advantages that are brought by the recent capability. Remaining issues are discussed and corresponding development plans are outlined.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗