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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Influence of Environment on Post-Detonation Chemistry and Debris Formation (Abbreviated Final Report: 20-SI-006)

Predicting, responding to, or interpreting the chemical record preserved in debris derived from nuclear events can be challenging due to chemical fractionation. Chemical fractionation is where different species of the evolving radionuclide inventory segregate and/or are lost from the system over the timescales of debris formation. Both historic data and recent research suggest that the interaction and character of the local environment may exert controls on chemical fractionation by influencing the cooling and evolution of the associated fireball as well as the composition of the vapor term and resultant speciation. Prior to this work, an integrated platform permitting dynamic and concurrent consideration of physical and chemical evolution of early time post-detonation event environments did not exist. Our work merged historic data and experimental approaches to support development of a computational framework able to simulate fundamental processes (e.g., entrainment of local environment, oxidation chemistry, and cooling time scales) that may perturb the radionuclide inventory captured in post-detonation debris. Work with historic debris confirmed that entrained environmental material affect debris composition, structure, and radionuclide incorporation. Complementary work utilizing a readily controllable and tunable benchtop setup (a plasma flow reactor) simulated the late cooling of a nuclear fireball (e.g., T < 6000 K) and bounded the sensitivity of actinide speciation and particle size distribution to variations in oxygen concentration and cooling rates. Concurrent laser ablation and laser heating experiments were used to investigate the chemistry and physics of processes occurring in vaporized and/or rapidly heated actinides and other elements in the presence of oxygen. A more computationally efficient microphysical model was developed for predicting and evolving size distributions of particles forming from mixed vapor terms and simulating particle formation processes under a variety of extreme conditions. Continued study of historic nuclear event film confirmed that shockwave data and physics codes agree to within the uncertainty of the data. Good agreement was achieved for thermal emission from an airburst, however the paucity of low-temperature molecular opacity data for mixtures of air, bomb debris, entrained dirt, and water vapor complicate agreement for more elaborate scenarios. A multiphysics code (ALE3D) was modified to bring the necessary physics and chemistry, including these new data and insights, onto a single platform. Code development included improved initialization of large physical systems, modernization of chemistry capabilities, and modifications to enable inclusion of particle transport.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
DOE OSTI2024

Debiasing with Diffusion: Probabilistic Reconstruction of Dark Matter Fields from Galaxies with CAMELS

Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between DM density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in galaxy formation models, which remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct DM fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and subgrid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying DM fields from the given stellar density fields while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes ≈500 times larger than those it was trained on and across different galaxy formation models. The code for reproducing these results can be found athttps://github.com/victoriaono/variational-diffusion-cdm✎.

Astronomy & Astrophysics↗
DOE OSTI2020

Effect of processing hydrogen pressure on magnetic properties of HDDR Nd-Fe-B magnet

High coercivity and anisotropic Nd-Fe-B powders for making anisotropic bonded magnets have been prepared by hydrogenation-disproportionation-desorption-recombination (HDDR). In this work, we found that the processing hydrogen pressure is intimately correlated with the formation of c-axis crystal texture of Nd 2 Fe 14 B phase. There exists a critical hydrogen pressure, P crit , during hydrogen-disproportionation stage, that promotes the formation of c-axis crystal texture. When processing hydrogen pressure is higher than P crit , the formation of c-axis texture deteriorates and results in isotropic Nd-Fe-B magnetic powders. The existence of P crit is ascribed to the thermodynamics and kinetics origin during HDDR process.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Biochemical basis for the formation of organ-specific volatile blends in mint

Above-ground material of members of the mint family is commercially distilled to extract essential oils, which are then formulated into a myriad of consumer products. Most of the research aimed at characterizing the processes involved in the formation of terpenoid oil constituents has focused on leaves. We now demonstrate, by investigating three mint species, peppermint ( Mentha ˣ piperita L.), spearmint ( Mentha spicata L.) and horsemint ( Mentha longifolia (L.) Huds.; accessions CMEN 585 and CMEN 584), that other organs – namely stems, rhizomes and roots – also emit volatiles and that the terpenoid volatile composition of these organs can vary substantially from that of leaves, supporting the notion that substantial, currently underappreciated, chemical diversity exists. Differences in volatile quantities released by plants whose roots had been dipped in a Verticillium dahliae -spore suspension (experimental) or dipped in water (controls) were evident: increases of some volatiles in the root headspace of mint species that are susceptible to Verticillium wilt disease (peppermint and M. longifolia CMEN 584) were detected, while the quantities of certain volatiles decreased in rhizomes of species that show resistance to the disease (spearmint and M. longifolia CMEN 585). To address the genetic and biochemical basis underlying chemical diversity, we took advantage of the newly sequenced M. longifolia CMEN 585 genome to identify candidate genes putatively coding for monoterpene synthases (MTSs), the enzymes that catalyze the first committed step in the biosynthesis of monoterpenoid volatiles. The functions of these genes were established by heterologous expression in Escherichia coli , purification of the corresponding recombinant proteins, and enzyme assays, thereby establishing the existence of MTSs with activities to convert a common substrate, geranyl diphosphate, to (+)-α-terpineol, 1,8-cineole, γ-terpinene, and (–)-bornyl diphosphate, but were not active with other potential substrates. In conjunction with previously described MTSs that catalyze the formation of (–)-β-pinene and (–)-limonene, the product profiles of the MTSs identified here can explain the generation of all major monoterpene skeletons represented in the volatiles released by different mint organs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Large-Area Periodic Arrays of Atomically Flat Single-Crystal Gold Nanotriangles Formed Directly on Substrate Surfaces

The advancement of nanoenabled wafer-based devices requires the establishment of core competencies related to the deterministic positioning of nanometric building blocks over large areas. Within this realm, plasmonic single-crystal gold nanotriangles represent one of the most attractive nanoscale components but where the formation of addressable arrays at scale has heretofore proven impracticable. In this work, a benchtop process is presented for the formation of large-area periodic arrays of gold nanotriangles. The devised growth pathway sees the formation of an array of defect-laden seeds using lithographic and vapor-phase assembly processes followed by their placement in a growth solution promoting planar growth and threefold symmetric side-faceting. The nanotriangles formed in this high-yield synthesis distinguish themselves in that they are epitaxially aligned with the underlying substrate, grown to thicknesses that are not readily obtainable in colloidal syntheses, and present atomically flat pristine surfaces exhibiting gold atoms with a close-packed structure. As such, they express crisp and unambiguous plasmonic modes and form photoactive surfaces with highly tunable and readily modeled plasmon resonances. The devised methods, hence, advance the integration of single-crystal gold nanotriangles into device platforms and provide an overall fabrication strategy that is adaptable to other nanomaterials.

42 ENGINEERING↗
DOE OSTI2023

Formate as an Energy Source to Allow Sugar Fermentation with No Net CO2 Generation: Integration of Electrochemistry with Fermentation

This poster summarizes at a high-level the scope of various groups' work on Formate as an Energy Source to Allow Sugar Fermentation with No Net CO2 Generation: Integration of Electrochemistry with Fermentation. The goals of this research are to develop and demonstrate an integrated process that electrochemically generates formate from CO2 and use the formate as an energy source for the fermentation of sugars to fatty acid methyl esters (FAME) without net CO2 generation wherein formate provides reducing equivalents for sugar fermentation and a chemical looping reactor system takes advantage of intermittent low-cost electricity from wind and solar resources.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2020

Impact of Dispersion Solvent on Ionomer Thin Films and Membranes

Perfluorosulfonic acid (PFSA) ionomers are an important class of materials that many electrochemical devices rely on as their ion-conducting electrolyte. Often, PFSA films are prepared through solution-processing techniques. Previous research has demonstrated that the solvent environment affects PFSA dispersion conformation, but it is not clear to what extent (if at all) these conformational effects persist for thin films and membranes upon casting, nor how dispersion solvent impacts film formation during the drying process. Here in this paper, we explore these questions by systematically examining the effect of Thin Film all length scales affected by dispersion solvent water and n-propanol mixtures on PFSA thin-film formation and structure, membrane structure, and resulting properties. Using a combination of in situ, time-resolved grazing incidence X-ray scattering and tender wide-angle X-ray scattering, it is shown that films prepared from high-water-concentration dispersions exhibit stronger interactions and arrangement upon drying, and possess larger network domain sizes than those prepared from low-water-concentration dispersions. These stronger interactions likely manifest in greater network connectivity, as evidenced by enhanced conductivity for membranes and decreased water uptake for thin films. Significantly, these solvent-induced differences persist even after thermal annealing. It is clear that the dispersion solvent choice is a critical parameter controlling PFSA nano- and mesoscale structure and presents an important dial with which to direct PFSA macroscale properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data from: “Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats”

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Failure analysis of solid oxide fuel cells nickel-yttria stabilized zirconia anode under siloxane contamination

In this study, the failure process of the solid oxide fuel cell (SOFC) Ni-yttria-stabilized zirconia (YSZ) anode is investigated with D4 siloxane (octamethylcyclotetrasiloxane) contamination. In order to evaluate the influence of the electrochemical reaction on the siloxane deposition process, the SOFC experiments were operated at open circuit voltage (OCV) and 50 mA cm -2 conditions at 800°C. During the failure process, electrochemical, morphology and exhaust gas component analysis testing are conducted at the critical points. An equivalent circuit model and corresponding microstructure parameter calculations for separated physicochemical processes were utilized for the quantitative analysis of the failure process. Further, the results confirm the siloxane chemical adsorption deposition mechanism proposed in previous work. As a result, the failure of the anode was attributed to the gas diffusion blockage by dense silicon dioxide layer formation. The anode failure process with siloxane contamination is faster when the anode is operated under polarization.

25 ENERGY STORAGE↗
DOE OSTI2022

Investigating the origin of observed central dips in radial metallicity profiles

ABSTRACT Radial metallicity trends provide a key indicator of physical processes such as star formation and radial gas migration within a galaxy. Large integral field unit surveys allow for detailed studies of these radial variations, with recent observations detecting central dips in the metallicity, which may trace the impact of various evolutionary processes. However, the origin of these dips has not been conclusively determined, with suggestions that they may be diagnostic dependent. In this paper, we use the Sloan Digital Sky Survey IV Mapping Nearby Galaxies at Apache Point Observatory survey to investigate whether the observed dips represent genuine decreases in the central metallicity, or whether they could be an artefact of the diagnostic used. Using a sub-sample of 758 local star-forming galaxies at low inclinations, we investigate in detail the impact of using different strong line diagnostics on the shapes of the returned profiles, and the prevalence of dips. We find no clear evidence of the dips being caused by changing values of the ionization parameter within galaxies. To investigate physical causes, we explore both global and spatially resolved parameters, finding that galaxies exhibiting central dips in the O3N2 metallicity profile have on average lower H α equivalent width values out to $R/R_{\rm {e}} \sim 1.5$, and higher values of DN(4000) in the central regions. We additionally find a higher prevalence of dips in galaxies with high stellar mass, and lower values of global specific star formation rate, suggesting a possible link to central quenching. Nevertheless, these results are dependent on the diagnostic used, suggesting caution should be taken when interpreting observed features in galaxy metallicity gradients.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Simulations of Hypervelocity Impact Plasmas

The space environment is fraught with complex plasmas spanning a wide range of densities and temperatures. Much of space plasma research has focused on large-scale changes in the ambient plasma and fields from the Sun to the Earth’s atmosphere, driven primarily by the solar wind. Yet one particular type of space plasma, which forms from a hypervelocity impact (HVI), remains poorly understood. Hypervelocity impactors include both meteoroids and space debris. Meteoroids are naturally occurring objects in space that travel between 11 and 72.8 km/s and originate primarily from comets and asteroids. In contrast, space debris are human-made objects with speeds typically < 11 km/s. Hypervelocity impactors routinely hit spacecraft, yet the physics behind the formation of the plasma and the dynamics of its expansion remain largely unknown. The complexity of this phenomenon necessitates a research approach that includes both experimental studies and numerical simulation in order to understand the underlying physical processes that occur upon formation and expansion of the impact plasma. Our research has focused on providing a comprehensive understanding of plasma generated by hypervelocity impacts by meteoroids and space debris on spacecraft in order to characterize the behavior of the expanding plasma and its interactions with the ambient environment. Previously. we conducted experimental campaigns at a dust accelerator facility that can accelerate particles up to 60 km/s, which is representative of meteoroid speeds, and at a light gas gun facility that can accelerate larger projectiles up to 7 km/s, which is representative of orbital debris. The experiments included plasma, optical and radio frequency (RF) sensors in order to understand the dynamics and associated RF emission resulting from hypervelocity plasmas. For this research, we developed and applied machine learning algorithms to identify which type of impactor would produce RF and physics-based models to determine the source of the RF. This was a one-year research program that resulted in 2 refereed journal publications (uploaded separately).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Probing Dynamic Phase Changes in Electrochemical Systems with Cryogenic and in Situ Analytical Electron Microscopy (Final Technical Report)

In this project, we focus on the advancement of our newly developed, unique in situ and cryogenic analytical electron microscopy (AEM) techniques and the development of additional high-end capabilities to quantitatively elucidate the mechanisms of resistive state changes in solid-state electrochemical materials for non-volatile, neuromorphic memory. The objectives of the proposed research are (1) to characterize and elucidate the mechanisms by which electronic state changes occur in various solid-state devices to build guiding principles for electrochemically activated smart materials; (2) to advance our understanding of such mechanisms by coupling cryogenic and in situ techniques to control kinetics of the observed mechanisms while mitigating probe-induced damage; and (3) to utilize additional advanced techniques including electron-beam induced current and quantum mechanical simulations in order to identify the underlying fundamental science of such processes, including roles of defect formation and its effect on phase nucleation, as well as dynamics of these processes in the electrochemical systems at the nanometer scale.

25 ENERGY STORAGE↗
DOE OSTI2021

Noble metal catalysts and processes for reforming of methane and other hydrocarbons

Processes for converting methane and/or other hydrocarbons to synthesis gas (i.e., a gaseous mixture comprising H 2 and CO) are disclosed, in which at least a portion of the hydrocarbon(s) is reacted with CO 2 . At least a second portion of the methane may be reacted with H 2 O (steam), thereby improving overall thermodynamics of the process, in terms of reducing endothermicity (ΔH) and the required energy input, compared to “pure” dry reforming in which no H2O is present. Catalysts for such processes advantageously possess high activity and thereby can achieve significant levels of methane conversion at temperatures below those used conventionally under comparable conditions. These catalysts also exhibit high sulfur tolerance, in addition to reduced rates of carbon (coke) formation, even in the processing (reforming) of heavier (e.g., naphtha boiling-range or jet fuel boiling-range) hydrocarbons. The robustness of the catalyst translates to high operating stability. A representative catalyst comprises 1 wt-% Pt and 1 wt-% Rh as noble metals, on a cerium oxide support.

Marker, Terry L.↗
DOE OSTI2022

Investigation of near-field jet stability of a single-hole injector based on fast X-ray phase contrast imaging and image feature matching

Fuel spray is very effective in controlling the combustion process to improve engine performance and reduce emissions. Understanding the spray unstability during the injection process is of importance to improve the control of spray characteristics and engine operation. In this study, near-field biodiesel jets were recorded using fast X-ray phase contrast imaging and the flow features inside the jet were extracted using Speeded Up Robust Features (SURF) method. Here, the image similarity by feature matching was successfully used to represent the near-field jet stability. Based on the jet stability, an injection process can be divided into five stages: an unstable stage at needle opening, a partially stable transition stage at needle opening, a stable stage, a stable transition stage at needle closing and an unstable stage at needle closing. The ranges of needle lift for five stages were also determined. The jet unstability is highly related to the cavitation formation and gas purging process during needle opening. The variation of stable jet feature is dependent on the needle lift at needle closing. Higher needle lift for the similar jet feature at needle opening indicates a hydraulic delay compared to needle closing. Finally, the possible reasons of jet feature formation and feature detection used on the multi-hole injector are discussed.

33 ADVANCED PROPULSION SYSTEMS↗