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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Bouger's Law Shell Ionospheric Transfer Function

Ionospheric transfer function (ITF) algorithms determine the effects of the ionosphere on an electromagnetic (EM) radio-frequency (RF) signal as it propagates through. In this report, the Bouger’s law shell model is outlined. This ITF is very similar to the Snell’s Law Shell ITF; however, in this formulation, plasma parameters in the ionosphere are allowed to change radially. This algorithm is expressed in the frequency domain. In this way, it is applied as linear time invariant (LTI) filter function. Signals in this report are assumed to have only a single component (i.e. x, y or z in a rectangular coordinate system). Multi-component signals can be treated simply by applying the specific ITF to each component separately.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Sensitivity and history match analysis of a carbon dioxide “huff-and-puff” injection test in a horizontal shale gas well in Tennessee

Due to improvements in well development, shale gas production has gained importance recently, especially in the United States. To improve gas production and develop a better understanding of shale reservoirs, researchers are conducting field tests to monitor CO 2 storage and enhanced gas recovery. Reservoir simulations can then utilize these field results for sensitivity studies, uncertainty analysis, history matching, gas production forecasts, and CO 2 storage capacity estimations. One such field test was performed for the Chattanooga Shale formation in Morgan County, Tennessee. Approximately 463 tonnes (510 tons) of CO 2 was successfully injected into a hydraulically fractured horizontal well over a thirteen-day period in March 2014. The injection test was achieved in four steps: pre-injection, injection, soaking, and flowback. In this paper, those steps were modeled with a reservoir simulator to match the historic production and injection rates with gas component compositions and forecast the production for five years. The reservoir fluid was modeled as a multi-component gas, including CH 4 , C 2 H 6 , C 3 H 8 , N 2 , and CO 2 . Langmuir constants, reservoir pressure, and fracture network volume were adjusted to match simulation results with observed production rates and gas composition. Results showed that, under the same reservoir conditions, each gas component behaves differently by way of compositional changes in production. CH4 behaves like N2, while CO 2 behaves like heavier hydrocarbons such as C 2 H 6 and C 3 H 8 . CO 2 plume results showed that, after injection, the produced CO 2 is mostly derived from fractured limestone (i.e., Fort Payne Formation) because the injected CO 2 is not adsorbed in limestone but rather by shale formations.

03 NATURAL GAS↗
DOE OSTI2023

Ab initio-based metric for predicting the protectiveness of surface films in aqueous media

Abstract Materials can passivate by forming surface films when placed in aqueous media. However, these films may or may not be stable, and their stability can be predicted by a metric called the Pilling-Bedworth Ratio (PBR). In this article, we extend PBR to predict passivation protectiveness of multi-component materials. We then evaluate this PBR (ePBR)’s effectiveness by comparing its predictions against experimental studies of 21 multi-element materials of diverse chemistries, with agreement for 17 of the materials. Finally, we encode the methodology to compute ePBR in a web-application to predict the protectiveness of 140,000+ materials in the Materials Project database.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions

We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.

Density functional theory calculations↗
DOE OSTI2020

Dynamic Atom Clusters on AuCu Nanoparticle Surface During CO Oxidation

Supported alloy nanoparticles are prevailing alternative low-cost catalysts for both heterogeneous and electrochemical catalytic processes. Selective interaction of an alloy component with a specific reactant induces a dynamic structural change of alloy nanoparticles under reaction conditions and largely controls their catalytic properties. However, such a multi-component dynamic-interaction-controlled evolution, both structural and chemical, remains far from clear. Herein, by using state-of-the-art environmental TEM, we directly visualize, in-situ at the atomic scale, the evolution of an AuCu alloy nanoparticle supported on CeO2 during CO oxidation. We find that gas molecules can “free” metal atoms on the {001} surface and form highly mobile atom clusters. Remarkably, we discover that CO exposure induces Au segregation and activation on nanoparticle surface, while O2 exposure leads to the segregation and oxidation of Cu on the particle surface. The as-formed Cu2O/AuCu interface may facilitate CO-O interaction corroborated by DFT calculations. These findings provide insights into the atomistic mechanisms on alloy nanoparticles during catalytic CO oxidation reaction, and to a broad scope of rational design of alloy nanoparticle catalysts.

Luo, Langli↗
DOE OSTI2020

Selective CO 2 electrocatalysis at the pseudocapacitive nanoparticle/ordered-ligand interlayer

Enzymes feature the concerted operation of multiple components around an active site, leading to exquisite catalytic specificity. Realizing such configurations on synthetic catalyst surfaces remains elusive. In this article, we report a nanoparticle/ordered-ligand interlayer that contains a multi-component catalytic pocket for high-specificity CO 2 electrocatalysis. The nanoparticle/ordered-ligand interlayer comprises a metal nanoparticle surface and a detached layer of ligands in its vicinity. This interlayer possesses unique pseudocapacitive characteristics where desolvated cations are intercalated, creating an active-site configuration that enhances catalytic turnover by two orders and one order of magnitude against a pristine metal surface and nanoparticle with tethered ligands, respectively. The nanoparticle/ordered-ligand interlayer is demonstrated across several metals with up to 99% CO selectivity at marginal overpotentials and onset overpotentials of as low as 27 mV, in aqueous conditions. Furthermore, in a gas-diffusion environment with neutral media, the nanoparticle/ordered-ligand interlayer achieves nearly unit CO selectivity at high current densities (98.1% at 400 mA cm -2 ).

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

RadioGalaxyNET: Dataset and novel computer vision algorithms for the detection of extended radio galaxies and infrared hosts

Abstract Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared hosts. In this paper, we introduce RadioGalaxyNET, a multimodal dataset, and a suite of novel computer vision algorithms designed to automate the detection and localization of multi-component extended radio galaxies and their corresponding infrared hosts. The dataset comprises 4 155 instances of galaxies in 2 800 images with both radio and infrared channels. Each instance provides information about the extended radio galaxy class, its corresponding bounding box encompassing all components, the pixel-level segmentation mask, and the keypoint position of its corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection. We benchmark several object detection algorithms on the dataset and propose a novel multimodal approach to simultaneously detect radio galaxies and the positions of infrared hosts.

Astronomy & Astrophysics↗
DOE OSTI2026

Characterizing the GD-1 Stream with DESI DR2 Data: Thin Stream and Hot Cocoon

GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($σ_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($σ_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.

Jarvis, Emma [Toronto U.] (ORCID:0009000656127336)↗
DOE OSTI2024

Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces

The objective of this project is to develop, and integrate, high-performance simulation tools capable of predicting plasma-facing component (PFC) operating lifetime and the impact of the evolving surface morphology of tungsten-based PFCs on plasma contamination, including the dynamic recycling of fuel species and tritium retention, in future magnetic fusion devices. Establishing a fundamental physical understanding and developing predictive capabilities of plasma-surface interactions (PSI) requires simultaneously addressing complex and diverse physics occurring over a wide range of length (Angstroms to meters) and time (femtoseconds to years) scales, as well as integrating extensive physical processes across the plasma–surface interface. This requires development of not only detailed physics models and computational strategies at each scale, but also algorithms and methods to couple them effectively in a way that can be robustly validated. Deploying these tools requires the continued development and coupling of leadership-scale computational codes to describe the boundary plasma and the evolving PFC surface, as well as a host of simulations that bridge disparate scales to address complex physical and computational issues at the plasma–surface interface in multi-component materials systems for magnetic fusion energy development beyond ITER.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Determining the extent of potential fugitive fluid migration from geologic carbon storage in hydrocarbon-bearing reservoirs: Insights from one-dimensional numerical modeling

Numerical modeling of Geologic Carbon Sequestration in permeable reservoirs initially containing hydrocarbons is conducted using the multi-phase, multi-component thermohydrologic simulator TOGA (TOUGH Oil, Gas, Aqueous; TOUGH stands for Transport Of Unsaturated Groundwater and Heat), to determine how phase and composition of the original fluids influence the extent of the zone where upward fugitive fluid migration could potentially occur, denoted R f . The area within R f comprises regions of substantially elevated pressure and free-phase CO 2 saturation, where a breach in reservoir sealing capacity would lead to upward fugitive fluid migration. The model examines the conditions within the storage reservoir that could lead to fugitive flow, but does not model the fugitive flow itself. A one-dimensional radial model of the storage reservoir is used, and three initial phase conditions are considered: single-phase aqueous, two-phase gas-aqueous, and three-phase oil-gas-aqueous. Components that may be present are H 2 O, CO 2 , CH 4 , C 4 H 10 , and C 10 H 22 . The most important factors controlling Rf are (1) the initial gas-phase saturation within the reservoir, and (2) the lateral extent of multi-phase initial conditions, particularly CO 2 . The composition of liquid and gas phases has a secondary effect. The impact of reservoir depth, thickness, injection rate, and hydrologic properties are also briefly examined, with thickness (or equivalently injection rate) having the biggest effect. These results can help to understand important trends in potential response of CO 2 -EOR fields being considered for dedicated CO 2 storage.

CO₂ plume migration↗
DOE OSTI2018

Mechanical properties of high-entropy alloys with emphasis on face-centered cubic alloys

HHigh-entropy alloys (HEAs), also known as multi-principal element alloys or multi-component alloys, have been the subject of numerous investigations since they were first described in 2004. The earliest HEA was the equiatomic CrMnFeCoNi “Cantor” alloy, but HEAs now encompass a broad class of metallic and ceramic systems. The concept of utilizing the high entropy of mixing to develop stable multi-element alloys may not be scientifically correct but has produced extraordinary mechanical properties in specific HEAs, mainly CrCoNi-based alloys, associated with their continuous work-hardening rate that is sustained to large plastic strains (~0.5) and at low temperatures. This, in combination with the high frictional forces on dislocations and a propensity for twinning, leads to outstandingly high fracture toughness values (exceeding 200 MPa·m 1/2 ) and resistance to shear-band formation under dynamic loading. The critical shear strain for the onset of adiabatic shear band formation is ~7 for the Cantor alloy, much higher than that for conventional alloys, suggesting superior ballistic properties. The slower diffusion rates resulting from the multi-element environment contribute to the excellent intermediate-temperature performance. We review the principal mechanical properties of these alloys with emphasis on the face-centered cubic systems, such as the CrCoNi-based alloys. Their favorable mechanical properties and ease of processing by conventional means suggest extensive utilization in many future structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Additive manufacturing of multiscale NiFeMn multi-principal element alloys with tailored composition

Nanostructured multi-principal element alloys (MPEAs) have been explored as next-generation engineering materials due to unique mechanical and functional properties which have significant advantages over traditional dilute alloys. However, the practical applications of nanostructured MPEAs are still limited due to the lack of scalable processing approaches to prepare a large quantity of nanostructured MPEAs, as well as lack of an efficient pathway for high-throughput discovery of better functional nanostructured MPEAs within their vast compositional space. Here we tackle these challenges by presenting an integrated approach by combining direct-ink-writing-based additive manufacturing, solid-state sintering, and chemical dealloying to manufacture hierarchically porous MPEAs. The hierarchical structure is comprised of macro- and micro-scale pores introduced via extrusion printing and polymer decomposition during sintering, as well as nanoscale pores formed via chemical dealloying. The macro- and micro-scale pores allow efficient dealloying of a large mass of material as the diffusion length that the corroding medium must penetrate remains at the scale of the ligaments formed after sintering (∼10 μm), despite the large volume of the 3D-printed samples. In addition, this integrated approach enables versatile control of the alloy composition via precisely tuning the ratio of elemental powders in the starting ink, thus offering a pathway for high-throughput discovery of novel functional MPEAs. As a case study, multiscale macro/micro/nanoporous NiFeMn MPEAs with three different compositions were investigated as catalysts to reduce the overpotential of oxygen evolution reaction (OER), where NiFeMn-based electrocatalysts display composition-dependent performance such that the overpotential measured at a current of 0.5 A g −1 for OER increases in the order of Ni 58 Fe 29 Mn 13 ⩽ Ni 64 Fe 26 Mn 10 < Ni 76 Fe 18 Mn 6 . This introduced manufacturing process offers new opportunities for scalable fabrication and rapid screening of nanostructured multi-component complex alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Melting temperature, emissivity, and thermal conductivity of rare-earth silicates for thermal and environmental barrier coatings

In recent years, rare-earth silicates have become the industry standard for coating state-of-the-art SiC ceramic matrix composite (CMC) gas turbine engine components, due to their low volatility, high melting point, and thermal shock resistance. Current research is focused on designing rare-earth silicate based thermal-environmental barrier coatings (T/EBCs) with improved resistance to CMAS (CaO-MgO-Al 2 O 3 -SiO 2 ), steam, and crack formation, while maintaining high temperature performance and stability. Here, in this work we compare the high temperature performance of a variety of single and multi-component rare-earth mono- and disilicates (MS, DS) and rare earth apatites by measuring their melting points and spectrally averaged visible emissivities using laser heating and radiation pyrometry. We also report room temperature thermal conductivity measured by time-domain thermoreflectance (TDTR).

Environmental barrier coatings↗
DOE OSTI2019

A general peridynamics model for multiphase transport of non-Newtonian compressible fluids in porous media

Herein a general state-based peridynamics model is developed to simulate transport of fluids in an arbitrary heterogeneous porous medium. The generality encompasses modeling of multiphase, multi-component flow of non-Newtonian and compressible fluids, which is often encountered in but not limited to subsurface reservoirs. Peridynamic model is especially useful for solving non-local problems, such as crack propagation, since it does not assume spatial continuity of field variables. Thus, the formulation presented here, combined with peridynamics-based damage model, can be used to simulate hydraulic fracturing with complex fluids. To demonstrate its capability to simulate multi-phase flow in porous media, the derived model is verified against the analytical Buckley-Leverett solution for immiscible Newtonian two-phase flow. Further, the non-Newtonian two-phase fluid flow in porous media is verified by simulating the polymer flood process involving immiscible displacement of a Newtonian fluid by a non-Newtonian fluid against a generalized solution obtained by Wu et al. The non-local solutions are shown to be consistent with the corresponding local solutions in limiting cases. Moreover, mass conservation of all the phases is satisfied, irrespective of discretization and extent of non-locality.

42 ENGINEERING↗
DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Radiation Chemistry Research for Improved Nuclear Fuel Cycles

Nuclear energy is a key component of a global, low-carbon energy future. One of the challenges facing large-scale deployment of nuclear power is the long-term disposition of used nuclear fuel. Due to the political and technical challenges of siting deep geologic storage facilities, reprocessing of used nuclear fuel is desirable to reduce the total volume and decay heat of waste requiring long-term storage, enabling efficient usage of geologic storage repositories. However nuclear fuel reprocessing is complicated by intense multi-component radiation fields that degrade solvent systems that are used for this purpose. Resulting radiolytic degradation can result in reduced performance over time, and generation of additional waste. Thus, an understanding of the radiation-induced chemistry of these reprocessing solvent systems is crucial to for the development of new, radiation-resistant processes for efficiently treating used nuclear fuel. Controlled irradiation experiments combined with high-performance chromatography and mass spectrometry have provided comprehensive knowledge of radiolytic degradation pathways and kinetics, which is needed to predict the effect of radiation-induced chemistry on the performance of the nuclear fuel reprocessing technology. This talk will give an overview of recent progress in research on the radiation chemistry of fuel reprocessing molecules conducted by the Idaho National Laboratory Center for Radiation Chemistry Research (CR2) in conjunction with the Molecular Mass Spectrometry and Chromatography group.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

In Situ Synthesized Homochiral Spiroborate Ester Metal–Organic Framework with Mono–, Di–, and Trivalent Cations

Here we report here a one–step method for synthesizing multi–component and in–situ–formed homochiral spiroborate–ester–based metal–organic framework CPM–B1. This unique material successfully integrates COF fragment spiroborate ester within the MOF and simultaneously incorporate homochirality and helicity. In addition, CPM–B1 is a rare example of framework materials that results from the cooperative assembly of three charge–complementary cations: +1 (lithium), +2 (cobalt), and +3 (boron). The sophistication of the co–assembly is further highlighted by the three structural roles of lithium ions. This unique structure contributes to its multi–functional properties such as ionic conductivity and catalytic activity for oxygen reduction reaction (by CPM–B1 carbonized material) and provides a new path to develop MOF materials with complex secondary building units and multi–functional applications.

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

Perspectives and challenges of ultra-high temperature ceramics for fusion plasma-facing applications

Ultra-high temperature ceramics (UHTCs) offer several potential advantages as plasma-facing components (PFCs) in fusion reactors due to their extreme melting points, tailorable thermal conductivity, and attractive unirradiated mechanical properties including fracture toughness comparable or superior to tungsten. Here, recent developments and material properties of UHTCs are briefly reviewed, along with an overview of limited studies on their responses to neutron irradiation and an evaluation of plasma-surface interactions. Five key research pathways, primarily focused on irradiation effects, for advancing UHTCs in PFC applications are discussed: (1) assessing irradiation effects on the coupled thermal–mechanical performance (2) addressing the lack of studies on irradiation, plasma-surface interactions, and their synergistic effects; (3) investigating high-temperature (>1000 °C) neutron irradiation effects critical for PFC performance; (4) optimizing multi-component UHTC compositions or composites to improve thermal or mechanical properties; (5) enhancing radiation resistance to mitigate microcracking and void swelling through strategies such as increasing sink strength by reducing grain size, introducing fine particles, and leveraging complex concentrated alloy concepts.

36 MATERIALS SCIENCE↗