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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
DOE OSTI2024

Rotating cylinder electrode in reactive CO 2 capture: Identifying active C species via transport, VLE models and kinetics

Here, this article explores technical challenges and potential methodologies for understanding electrochemical Reactive CO 2 Capture (RCC) mechanisms. RCC offers potential energy cost advantages by directly converting captured CO 2 into fuels and chemicals, unlike traditional carbon capture and utilization (CCU) processes that require sequential capture, concentration, and compression. However, direct conversion of captured CO 2 introduces complexity due to additional equilibrium buffer reactions, making it challenging to identify active species for reduction in electrochemical studies. This article discusses methods to integrate transport, thermodynamics, and kinetics concepts to identify active carbon sources in RCC. Vapor‐Liquid Equilibrium (VLE) and transport models are validated against experimental results obtained in a gastight rotating cylinder electrode reactor and are shown as useful tools for studying RCC in heterogeneous electrocatalysts across different capture agents, solvents, and temperatures. This article establishes an experimental framework for advancing research in electrochemical RCC.

Electrocatalysis↗
DOE OSTI2023

Decontamination Strategies to Increase Fuel and Product Conversion Yields of Municipal Solid Waste

Since the implementation of China’s Green Fence and National Sword policies, many areas lost a market for heavily contaminated municipal solid waste (MSW). Approximately 17.2 million tons of paper and 26.9 million tons of plastic were landfilled in the United States in 2018. This non-recyclable waste that would normally be landfilled can be converted into fuels and chemicals, but common contaminants that are present in non-recyclable waste can negatively affect low and high-temperature conversion yields. In addition, MSW is a heterogeneous feedstock whose composition can vary widely across geographical areas and socio-economic statuses. This presents a unique challenge for providing a consistent feedstock to biorefineries around the world. Techniques that are commonly used in other industries, in addition to pre-processing methods used for traditional biomass, can be used to increase conversion yields. Drawing on established techniques, as well as experimental decontamination strategies, non-recyclable MSW could represent a viable alternative to agricultural residues for biofuel production. Although many of the techniques explored in this chapter have been used extensively in other industries, the feedstock composition needs to be carefully considered when choosing prospective decontamination methods. It is likely that there is not a “one-size-fits-all” decontamination method for MSW. In conclusion, a pre-processing and decontamination plan will rely heavily on the composition of the individual waste stream.

09 BIOMASS FUELS↗
DOE OSTI2026

From bulk to surface: Structure and dynamics of amorphous alumina from deep potential molecular dynamics

Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains challenging. We employ Deep Potential (DP) molecular dynamics to generate large-scale, ab initio -quality models of amorphous Al 2 O 3 bulk glasses and melt-quenched free surfaces, enabling a quantitative analysis of both structure and relaxation dynamics with statistical confidence inaccessible to direct ab initio simulation. The trained DP model reproduces experimental liquid and glass structure, captures the cooling-rate dependence of the bulk glass transition, and corrects systematic biases in the polyhedral populations predicted by widely used classical force fields. At the free surface, mass density recovers to bulk values over ~10 Å, while local coordination requires a slightly wider subsurface region to fully converge. The outermost layer is oxygen-enriched, exhibits altered polyhedral connectivity with contracted Al–O bonds, and hosts a broad population of under-coordinated motifs (notably AlO 3 and OAl 2 ) whose abundances are governed by glass stability. These under-coordinated surface motifs exhibit distinct vibrational signatures and occur as locally paired Lewis acid and Brønsted base sites consistent with bond-valence compensation, yet remain spatially dispersed rather than aggregating into extended clusters. Despite this pronounced structural heterogeneity, surface relaxation and the glass-transition temperature remain comparable to their bulk counterparts, suggesting that the disordered surface is kinetically stable once formed. Together, these results establish a molecular-level picture of amorphous alumina surfaces and demonstrate the capability of machine-learned potentials to resolve structure–property relationships in disordered oxide interfaces.

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

Correlating structural changes in thermoresponsive hydrogels to the optical response of embedded plasmonic nanoparticles

Stimuli-responsive microgels, composed of small beads with soft, deformable polymer networks swollen through a combination of synthetic control over the polymer and its interaction with water, form a versatile platform for development of multifunctional and biocompatible sensors. The interfacial structural variation of such materials at a nanometer length scale is essential to their function, but not yet fully comprehended. Here, we take advantage of the plasmonic response of a gold nanorod embedded in a thermoresponsive microgel (AuNR@PNIPMAm) to monitor structural changes in the hydrogel directly near the nanorod surface. By direct comparison of the plasmon response against measurements of the hydrogel structure from dynamic light scattering and nuclear magnetic resonance, we find that the microgel shell of batch-polymerized AuNR@PNIPMAm exhibits a heterogeneous volume phase transition reflected by different onset temperatures for changes in the hydrodyanmic radius (R H ) and plasmon resonance, respectively. The new approach of contrasting plasmonic response (a measure of local surface hydrogel structure) with R H and relaxation times paves a new path to gain valuable insight for the design of plasmonic sensors based on stimuli-responsive hydrogels.

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

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗
DOE OSTI2020

Multiple Degradation Mechanisms in Reinforced Concrete Structures, Modeling and Risk Analysis

The overarching goal of this project is to complement ongoing Department of Energy (DOE) Light Water Reactor Sustainability (LWRS)-funded Grizzly concrete modeling development efforts by providing improved multi-physics models for incorporation into the Grizzly and BlackBear codes. Objectives of this work identified at the outset of this project include: 1. Coupling between mechanical damage and transport processes. The mechanical damage will be characterized by nonlinear mechanical constitutive models appropriate for concrete used in nuclear power plants (NPPs). 2. Improved representation of the coupling among the transport process, building on the current moisture and thermal transport models in Grizzly. The parameters for coupling among heat and mass transport in the multi-physics model will be experimentally determined. 3. Coupling among different scale levels of concrete constituents, fine and coarse aggregates, cement paste, hydration products, and pore structure. 4. Probabilistic analysis of the random nature of the heterogeneous concrete structure and the environmental factors (ambient temperature and humidity) and their multiple effects on concrete deterioration. 5. Benchmark verification, validation and uncertainty quantification of the thermo-hygro- chemo-mechanical (THCM) concrete formulation. The end goal of this work was to provide a comprehensive and robust simulation capability that can be applied at the engineering scale for analysis of realistic deterioration scenarios in NPP concrete structures.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The Preparation and Characterization of Co–Ni Nanoparticles and the Testing of a Heterogenized Co–Ni/Alumina Catalyst for CO Hydrogenation

Samples of well-controlled nanoparticles consisting of alloys of cobalt and nickel of different atomic ratios were synthesized using wet chemical methods with oleylamine as the solvent and the reducing agent. These materials were characterized by a variety of techniques, including high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), X-ray energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). Small amounts of heterogenized catalysts were prepared using alumina as the support. However, the potential for use of Co–Ni catalysts in CO hydrogenation was explored using a larger amount of Co–Ni/alumina catalyst prepared from standard aqueous impregnation methods and tested in a continuously stirred tank reactor (CSTR) for Fischer–Tropsch synthesis (FTS). Results are compared to a reference catalyst containing only cobalt. The heterogenized catalysts were characterized using synchrotron methods, including temperature programmed reduction with extended X-ray absorption fine structure spectroscopy and X-ray absorption near edge spectroscopy (TPR-EXAFS/XANES). The characterization results support intimate contact between Co and Ni, strongly suggesting alloy formation. In FTS testing, drawbacks of Ni addition included decreased CO conversion on a per gram catalyst basis, although Ni did not significantly impact the turnover number of cobalt, and produced slightly higher light gas selectivity. Benefits of Ni addition included an inverted induction period relative to undoped Co/Al2O3, where CO conversion increased with time on-stream in the initial period, and the stabilization of cobalt nanoparticles at a lower weight % of Co.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1979

Orbiting passive microwave sensor simulation applied to soil moisture estimation

A sensor/scene simulation program was developed and used to determine the effects of scene heterogeneity, resolution, frequency, look angle, and surface and temperature relations on the performance of a spaceborne passive microwave system designed to estimate soil water information. The ground scene is based on classified LANDSAT images which provide realistic ground classes, as well as geometries. It was determined that the average sensitivity of antenna temperature to soil moisture improves as the antenna footprint size increased. Also, the precision (or variability) of the sensitivity changes as a function of resolution.

Newton, R. W.↗
NASA NTRS1991

Symposium (International) on Combustion, 23rd, Universite d'Orleans, France, July 22-27, 1990, Proceedings

The present symposium on combustion discusses reaction kinetics, NO(x) kinetics, premixed, diffusion, and nonsteady flames, turbulent combustion, hazardous waste, fluidized bed combustion, coal boilers and furnaces, engines, heterogeneous kinetics, heterogeneous, droplet, and microgravity combustion, and high-temperature synthesis. Attention is given to reactions of biphenyl, methylnaphthalenes, and phenanthrene with atomic oxygen in the gas phase, the oxidation of ortho-xylene, the effects of water on combustion kinetics at high pressure, and the formation and measurement of N2O in combustion systems. Topics addressed include large ions in premixed benzene-oxygen flames, the structure and kinetics of CH4/N2O flames, the propagation of unsteady hydrogen premixed flames near flammability limits, and the basic structure of lean propane flames. Also considered are OH measurements of piloted diffusion flames of nitrogen-diluted methane near extinction, waste combustion, preferential oxidation of carbon surfaces, and reburning mechanisms in a pulverized coal combustor.

Source record↗
DOE OSTI2022

Thermal measurement apparatus and methods for anisotropic thermal materials

A system for characterizing thermal properties of thermally anisotropic heterogeneous samples includes a heating element, a first temperature sensing device, a second temperature sensing device, and a computing system. The heating element is positioned at a first location within a sample and heats the sample. The first temperature sensing device outputs data indicative of temperatures of the first location to the computing device. The second temperature sensing device outputs data indicative of temperatures of the second location to the computing device. The computing device computes a thermal conductivity of the sample based upon the temperatures of the first location. The computing device further outputs an indication of a portion of the sample to which the thermal conductivity pertains based upon the second temperatures.

Reyes, Karla Rosa↗
DOE OSTI2023

Thermal measurement apparatus and methods for anisotropic thermal materials

A system for characterizing thermal properties of thermally anisotropic heterogeneous samples includes a heating element, a first temperature sensing device, a second temperature sensing device, and a computing system. The heating element is positioned at a first location within a sample and heats the sample. The first temperature sensing device outputs data indicative of temperatures of the first location to the computing device. The second temperature sensing device outputs data indicative of temperatures of the second location to the computing device. The computing device computes a thermal conductivity of the sample based upon the temperatures of the first location. The computing device further outputs an indication of a portion of the sample to which the thermal conductivity pertains based upon the second temperatures.

Reyes, Karla Rosa↗
DOE OSTI2026

3D TRISO particle-explicit compact meshing

The TRI-structural ISOtropic (TRISO) layered fuel particle is a robust nuclear fuel form offering enhanced safety and performance for advanced reactor concepts, including high-temperature gas-cooled reactors and other Generation IV designs. These poppy-seed-sized particles are embedded in a graphite matrix to form fuel elements that must withstand elevated temperatures and high burn-up levels. The heterogeneous nature of these fuel elements — comprising thousands of randomly distributed TRISO particles — produces complex stress fields and thermal gradients that one- and two-dimensional models cannot accurately capture. While three-dimensional modeling has improved predictions of dimensional changes, internal pressure buildup, and fission product transport under irradiation, current approaches rely on homogenized material properties that are known to have considerable divergence from experimental observations. This work presents a methodology for optimized random packing of TRISO fuel compacts and full three-dimensional mesh generation within the BISON fuel performance code, with each particle coating layer individually discretized. The resulting mesh was demonstrated through heat conduction simulations under representative in-reactor operating conditions, showing strong agreement with expected behavior. This capability enables detailed analysis of particle-to-particle interactions, matrix cracking mechanisms, and the statistical distribution of coating layer failures — all of which directly govern fuel performance and safety margins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Dissolved pyrogenic carbon leaching in soil: Effects of soil depth and pyrolysis temperature

We report Pyrogenic organic carbon (PyC) is found in soils as a heterogeneous mixture of thermally altered plant residues that range in their susceptibility to losses via mineralization, leaching, and erosion. Leaching of PyC as DOC (DPyC) within the soil profile is likely influenced by the chemical composition of solid PyC and the dominant soil processes and properties at a particular depth. Here we report the results of a 2-year laboratory decomposition and leaching study designed to investigate the interactive effects of pyrolysis temperature levels (no pyrolysis, 300 °C, and 450 °C) and soil depth (surface soil, 0–10 cm depth; subsurface soil, 50–70 cm depth) on losses of 13 C-labeled jack pine (Pinus banksiana) wood, wood pyrolyzed at 300 °C (PyC300) and wood pyrolyzed at 450 °C (PyC450) as dissolved organic carbon (DOC). Losses of wood and PyC in the 13 C- DOC pool were measured in leachates drained from soils once a month over the course of 1 year, and at one single leaching event after 2 years. We found that pyrolysis temperature levels interacted with time and soil depth to affect losses of wood and PyC as DOC throughout the 1-year incubation and leaching study, with greater DOC losses from wood in surface soil (0.73 ± 0.076% of added C) than in subsurface soil (0.40 ± 0.063% of added C) averaged across sampling time. Monthly DOC losses from PyC were not affected by soil depth. Cumulative data indicated a small contribution of wood (2.86 ± 0.07%), PyC300 (0.40 ± 0.04%), and PyC450 (0.16 ± 0.01%) to total DOC leached from soils. DOC losses from wood and PyC300 (as a proportion of added C) were greater in surface soil than in subsurface soil, whereas DOC losses from PyC450 were unaffected by differences between surface and subsurface soil. Losses of DOC were greater from PyC300 than from PyC450 in surface soil, with no significant differences in DOC losses between PyC300 and PyC450 in subsurface soil. One single leaching event at the end of the 2-year decomposition study resulted in higher DOC losses from wood in subsurface soil than in surface soil likely due to desorption, and no differences in DOC losses between PyC300 and PyC450 regardless of soil depth. Our results suggest strong interactions between the initial physicochemical composition of organic C inputs and soil properties (soil depth as a proxy) that control the mobility and transport of PyC and should be better represented in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ultrasonic measurement of temperature distributions in extreme environments: Electrical power plants testing in utility-scale steam generators

Thermal heterogeneities within energy conversion and storage, material processing, nuclear processes, aerospace, and military applications are often inaccessible to characterization by insertion sensors. When sensor deployment is possible, conventional pointwise temperature probes quickly degrade when inserted into harsh environments typical of such processes. We developed spatially-resolved ultrasonic thermometry to noninvasively measure the spatial distributions of thermal properties in such applications, even when sizable thermal gradients are present. Our method divides the path of ultrasonic propagation into segments bound by echogenic features, which create echoes in pulse-echo mode, encoding the information about interior temperature distributions. We use the acquired ultrasonic responses to estimate the internal temperature distributions by solving an inverse problem or concatenating segmental estimates. This work describes the implementation and industrial testing of the developed method at a coal-fired electrical power generation plant. We inserted an echogenically segmented Inconel 625 waveguide into the combustion zone of the utility-scale boiler and continuously acquired ultrasonic data while keeping sensitive components away from the damaging combustion environment. The accuracy of the time-dependent temperature distributions reconstructed from the ultrasonic measurements was comparable to that of thermocouples. The resiliency of ultrasonic thermometry to harsh combustion conditions was far superior to conventional insertion sensors. The measurements obtained during plant operation captured daily steam generation cycles in response to changing customer demand and intermittent contributions of renewable power sources to the power grid. These measurements have revealed new insights into the relationship between the dynamic power generation load and the conditions inside the steam generator. As a result, the successful industrial testing of spatially-resolved ultrasonic thermometry in solids indicates that the developed technology has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term thermal characterizations in extreme environments.

42 ENGINEERING↗
DOE OSTI2024

Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model

This study showcases a global, heterogeneously coupled total water level system wherein salinity and temperature outputs from a coarser-resolution (~12 km) ocean general circulation model are used to calculate density-driven terms within a global, higher-resolution (~2.5 km) depth-averaged total water level model. We demonstrate that the inclusion of baroclinic forcing in the barotropic model requires modification of the internal wave drag term to prevent excess degradation of tidal results compared to the barotropic model. By scaling the internal tide dissipation by an easy to calculate dissipation ratio, the resulting heterogeneously coupled model has complex root mean square errors (RMSE) of 2.27 cm in the deep ocean and 12.16 cm in shallow waters for the M 2 tidal constituent. While this represents a 10%–20% deterioration as compared to the barotropic model, the improvements in total water level prediction more than offset this degradation. Global median RMSE compared to observations of total water levels, 30-day sea levels, and non-tidal residuals improve by 1.86 (18.5%), 2.55 (42.5%), and 0.36 (5.3%) cm respectively. The drastic improvement in model performance highlights the importance of including density-driven effects within global hydrodynamic models and will help to improve the results of both hindcasts and forecasts in modeling extreme and nuisance flooding. With only an 11% increase in model run time compared to the fully barotropic total water level model, this approach paves the way for high resolution coastal water level and flood models to be used alongside climate models, improving operational forecasting of total water levels.

Blakely, Coleman Peter [University of Notre Dame, ↗