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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Role of Oxidation Resistance in High Temperature Alloy Selection for a Future with Green Hydrogen

Hydrogen is being considered integral to the future energy landscape but there is limited mechanistic understanding and a lack of predictive models to describe the combined effects of alloy and gas composition, temperature, thermal cycling and water vapor contents on the oxidation behavior of high-temperature materials. Experimental evaluations were combined with coupled thermodynamic-kinetic modeling to investigate the oxidation behavior of five representative Ni-based superalloys in two water vapor contents (10%, 60% H 2 O) under thermal cycling (1-h, 100-h cyles) conditions at 800°C and 1000°C. The alloy with the highest Ti content demonstrated the poorest cyclic oxidation behavior while the alloy with highest Cr and Al contents was expected to continue to support protective formation of a compact Al 2 O 3 scale. Finally, accelerated degradation of the chromia-forming alloys was observed in the higher water vapor content but the impact on transient oxidation of the alumina-forming alloys needs further investigations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A theoretical calculation of stacking fault energy of Ni alloys: The effects of temperature and composition

Combining cluster expansion (CE) method with one dimensional axial Ising model, this work investigated the effects of alloying elements and configurational variations due to temperature on the stacking fault energy (SFE) of FCC Ni binary alloys. Ensembles of large numbers of atomistic structures, each with more than ~400 atoms, were generated to consider sufficient long-range chemical disorder and temperature effects due to configurational entropy. Here, a Monte Carlo Metropolis algorithm was used to generate these structures, whose energies were then evaluated based on the effective cluster interactions obtained from CE. As a baseline, this work had shown the SFE of pure Ni and Al to be 127 mJ/m 2 and 137 mJ/m 2 , respectively, which agreed with the experimental values of 125 mJ/m 2 and 150 mJ/m 2 reported in the literature. Additions of Al, Ti, Cr and Co to pure Ni were found to decrease the SFE to different extents. Although temperature does not strongly influence the SFE of the FCC Ni-Al and Ni-Cr binary alloys, it can lead to significant changes to the SFE of the FCC Ni-Ti and Ni-Co alloys. While effects of temperature and composition on SFE observed in this work were calculated from binary Ni alloys, the general trends are nonetheless expected to be valid in the γ phase of multicomponent Ni superalloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Extending Interfaces in 3D to Achieve Superior Nanoscale Strength in Ti/Nb Nanolaminates

Tuning the atomic-level structure of nanolaminates enables high strength, increased deformability, and the ability to absorb and mitigate damage due to varied crystalline defects, including dislocations. Here, we present the enhanced strength of Ti/Nb nanolaminates containing thick 3D interfaces (3DIs), relative to their chemically abrupt 2D counterparts. We examine the effects of crystallographic alignment and compositional gradients on mechanical behavior via experiments and phase-field-dislocation dynamics (PFDD) modeling. Mechanical testing reveals that nanolaminates containing thicker 3DIs demonstrate a 28% hardness enhancement compared with sharp-interface nanolaminates. PFDD modeling shows that the critical resolved shear stress (CRSS) increases with the 3DI thickness. Gradual compositional transitions in 3DIs were confirmed via scanning transmission electron microscopy and high-resolution transmission electron microscopy, showing sharp crystallographic transitions and a heightened interface topography. The findings establish a positive function between the 3DI thickness and mechanical robustness for hexagonal-closest-packed-containing composites, emphasizing the role of defect–interface interactions in tailoring the mechanical performance and providing a foundation for future interfacial engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2022

CFD-DEM Modeling of Autothermal Pyrolysis of Corn Stover with a Coupled Particle- and Reactor-Scale Framework

Autothermal operation of fast pyrolysis is an efficient process-intensification technique wherein exothermic oxidation reactions are used to overcome the heat-transfer bottleneck of conventional pyrolysis. The development of accurate, reliable modeling toolsets is imperative to generating a deeper understanding of biomass autothermal pyrolysis systems to support scale-up and industrial deployment. This modeling effort describes the development of single-particle and reactor models which incorporate detailed reaction schemes and simultaneous exothermic oxidation reactions. The particle-scale model was parameterized for corn stover feedstock with particle morphology, density, ash content, and biopolymer composition, all of which impact the emergent conversion characteristics during pyrolysis. Results were then used to parameterize a reactor-scale autothermal pyrolysis model, which was developed using a coarse-grained computational fluid dynamic-discrete element method. The simulation results compared well with experimental results, with the predicted bio-oil, light gas, and biochar yield within 3.0 wt% of the experimental yields. Further analyses were performed to test the influence of equivalence ratio, biomass injection position, and particle size distribution on autothermal pyrolysis. The analysis of the physio-chemical properties of the fluid and solid phase inside the reactor and at the reactor outlet help reveal important process interactions of autothermal pyrolysis.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2024

Selenium interaction with iron minerals: Quantitative comparison of sorption and coprecipitation impacts on mobility

Given the significance of selenium (Se) as a micronutrient, the radioactive nature of some of its isotopes, and its affinity to iron (Fe) minerals, extensive research has been conducted on the sorption mechanisms between Se and these minerals. Here, in this study, we employ sorption data sourced from the L-SCIE database and coprecipitation data from available literature to achieve the following objectives: i) establish coherence between adsorption and coprecipitation processes, ii) quantitatively evaluate the importance of these processes in nuclear waste repository science, and iii) propose a forward-looking approach for integrating coprecipitation into reactive transport models. Our findings indicate that a correlation between Se adsorption and coprecipitation can be established using the λ formalism. The comparable log(λ Se(IV) /λ Se(VI) ) ratios derived from adsorption and coprecipitation experiments suggest that these processes can be quantitatively compared and evaluated using our numerical approach. Across all iron oxide phases examined, coprecipitation leads to significantly greater immobilization of Se compared to adsorption. Specifically, for hydrous ferric oxide, hematite, and goethite, coprecipitation is predicted to result in 100–1000 times more Se immobilization compared to adsorption, irrespective of the Se oxidation state (Se(IV) or Se(VI)); notably stronger immobilization potential via coprecipitation was observed for magnetite. The modeling approach and quantitative analysis presented herein clearly highlight the importance of including coprecipitation processes when simulating Se (and other elements) transport, particularly under conditions where mineral compositions are transient or evolving with time. Neglecting coprecipitation in models is likely to lead to significant overestimates of migration.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Fast Reactor Physics Model Verification Studies using ARC and PyARC Workflows

PyARC was recently developed at Argonne National Laboratory to automate many of the tasks required in the ARC (Argonne Reactor Computation) fast reactor simulation workflow, from input file generation, code execution, data transfer between ARC codes, and output postprocessing. PyARC will likely be the path forward to train new users of the ARC codes with the goal of wide adoption by the national laboratories, academia, and industry. In particular, for the ANL-JAEA collaboration under the Civil Nuclear Working Group (CNWG) project agreement NE-01, PyARC will be used to model the Joyo and EBR-II reactors for comparisons with measured data and calculated results from JAEA (Task 3: Fast Reactor Fuel and Core). As an additional avenue for verification and validation, this report investigates the use of PyARC towards a variety of existing ARC-based reactor models, in order to understand its efficacy in replicating the behavior of base ARC codes and better understand any limitations within modeling realistic fast reactor problems. To this end, PyARC was used to model the Joyo MKI, RBEC Benchmark-M, PRISM Mod-B, and EBR-II Run 138B cores, and its results were compared to those from existing ARC-based models. It was found that for hexagonal-based geometries PyARC was able to replicate the behavior of ARC codes to within 10 pcm for small reactor cores, and ~150pcm difference in eigenvalue for larger cores. These discrepancies are attributed primarily to differences in local mesh refinement options between ARC and PyARC, which currently cannot be resolved with PyARC’s latest version (1.6.0). In some of these cases, PyARC was used to model steady-state problems with initial core compositions originating from a prior REBUS depletion calculation. While PyARC was not designed to support such steady-state calculations, workarounds were applied to replicate the behavior of ARC-based calculations as closely as possible. Thus, these results demonstrate the wide extent to which they can be applied to fast reactor problems while still providing immense benefit to the user in terms of automating and standardizing common routines within the fast reactor analysis workflow. This study concluded that PyARC will be suitable for modeling the steady-state conditions of the EBR-II and Joyo fast reactors as part of the CNWG project agreement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

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

Efforts to stabilize composite localization elements

Localization finite elements seek to provide a robust framework for modeling ductile failure. They utilize the same constitutive model as the bulk material through the introduction of a length scale in a specialized deformation gradient that regularizes displacement discontinuity. Similar to many other elements, localization elements exhibit locking and associated pressure oscillations under incompressible plastic flow, which is a critical issue when attempting to model pressure-driven damage evolution. These issues can be drastically improved through what are essentially reduced integration techniques for the Jacobian and pressure, but there seem to be pressure-related instabilities that persist and are specific to localization elements. This memo summarizes recent efforts to mitigate and understand this problem, mostly for the 12-node composite wedge localization element in particular. At this point, it remains unclear whether the pressure fields within any localization element can be sufficiently stabilized in order to properly model failures that include softening or fracture.

36 MATERIALS SCIENCE↗
DOE OSTI2024

STRUCTURAL MODELING TO SUPPORT POST-YIELD ACCEPTANCE CRITERIA FOR SPENT NUCLEAR FUEL CLADDING

Spent nuclear fuel (SNF) is evaluated for structural failure during storage and transportation scenarios. The U.S. Department of Energy’s Spent Fuel and Waste Science and Technology (SFWST) program has sponsored significant research in quantifying mechanical loads on SNF during storage and transportation scenarios using experimental and modeling methods. The SFWST program has also performed significant research on measuring the mechanical behavior of irradiated SNF as defueled cladding segments and cladding with fuel pellets to measure composite behavior. This paper considers some of the key material data from the Sibling Pin testing and uses structural modeling and analysis methods that have been informed by testing to consider post-yield acceptance criteria for SNF cladding structural analysis. Test data published by Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL) are the foundation for informing the material behavior of the models developed in this study. In particular, four-point bend (4PB) tests of fueled and defueled cladding segments provide significant information about the bending failure mode of SNF. ORNL’s 4PB test data is on fueled cladding segments, so the composite behavior of SNF is demonstrated. This paper describes PNNL’s coincident beam model that was developed to approximate the composite behavior of SNF. This paper also presents PNNL’s structural dynamic finite element models of a cask tip-over scenario, which is predicted to cause the strongest mechanical loads on SNF of all postulated storage and transportation scenarios. SNF bending loads predicted in the cask tip-over scenario and cladding acceptance criteria beyond yield are considered, with justification based on the Sibling Pin test data. ASME Boiler and Pressure Vessel code stress intensity limits are also considered. The ultimate goal of this work is to aid in the justification of structural acceptance criteria for SNF cladding beyond the cladding’s irradiated yield strength for use in structural analysis of all storage and transportation scenarios.

Klymyshyn, Nicholas A.↗
DOE OSTI2022

A Phase-Field Study on Internal to External Oxidation Transition in High-Temperature Structural Alloys

Structural alloys applied at high temperatures rely on an external dense layer of oxide scale for protection. As some reactive alloy components are selectively oxidized internally, understanding how these dispersed metal oxide particles reach the surface to form a protective scale—the so-called internal to external oxidation transition—is crucial for designing these alloys. While the literature is replete with experimental studies on oxidation of alloys, there is a lack of computational studies in this realm due to the complex nature of coupled reaction and diffusion processes in multicomponent multi-phase alloy systems. In this work, we apply a recently developed phase-field model to simulate the oxidation processes under different compositions and nucleation scenarios to gain insights into how a continuous oxide scale can be established. The results show that while alloy composition is critical for internal to external oxidation transition, the oxide nuclei size, shape and distribution also have significant impact on the transition kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements

Abstract Terrestrial ring current dynamics are a critical part of the near‐space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time‐varying distribution of ring current proton using geomagnetic indices and solar wind parameters with their history as input. We train an artificial neural network (ANN) model to reproduce proton fluxes measured by the Radiation Belt Storm Probes Ion Composition Experiment instrument onboard Van Allen Probes. By choosing optimal feature parameters and their history length, the model results show a high correlation and a small error between model specifications and satellite measurements. The modeled results well capture energy‐dependent proton dynamics in association with geomagnetic storms, including inward radial diffusion, acceleration and decay. Our ANN model produces proton fluxes with their corresponding 3D spatiotemporal variations, capturing the latitudinal distribution and local time asymmetry that are consistent with observations and that can further inform theory.

Li, Jinxing↗
DOE OSTI2024

Comprehensive Cradle to Grave Life Cycle Analysis of On-Road Vehicles in the United States based on GREET

To properly compare and contrast the environmental performance of one vehicle technology against another, it is necessary to consider their production, operation, and end-of-life fates. Since 1995, Argonne’s GREET® life cycle analysis model (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) has been annually updated to model and refine the latest developments in fuels and materials production, as well as vehicle operational and composition characteristics. Updated cradle-to-grave life cycle analysis results from the model’s latest release are described for a wide variety of fuel and powertrain options for U.S. light-duty and medium/heavy-duty vehicles. Light-duty vehicles include a passenger car, sports utility vehicle (SUV), and pick-up truck, while medium/heavy-duty vehicles include a Class 6 pickup-and-delivery truck, Class 8 day-cab (regional) truck, and Class 8 sleeper-cab (long-haul) truck. Powertrain coverage includes internal combustion (spark ignition and compression ignition) engines, hybrid electric, plug-in hybrid, full battery electric, and fuel cell vehicles powered by conventional and low carbon energy sources. The results offer insights into the current state of these technologies, as well as a projection of the likely environmental implications of future fuel and vehicle advancements through a time-series evaluation of life cycle greenhouse gas emissions.

Kelly, Jarod C.↗
DOE OSTI2022

Emulation of neutron-irradiated microstructure of austenitic 21Cr32Ni model alloy using dual-ion irradiation

Here, in this study, the capability of heavy-ion irradiation to emulate neutron irradiation was demonstrated on an austenitic 21Cr32Ni type ternary model alloy. The model alloy used in this study is chemically analogous but compositionally simpler than of alloy 800H, which is a candidate austenitic Ni alloy which has been proposed for use in Generation IV reactors. The microstructure of the 21Cr32Ni model alloy irradiated in the BOR-60 fast reactor to 17.1 dpa and 35 dpa at ~380°C was characterized using transmission electron microscopy (TEM). The 17.1 dpa BOR-60 irradiated microstructure was then compared with the microstructure of the same material developed under dual-ion (DI) irradiation using various He/dpa ratios between 0.1 and 16.6 appm He/dpa in the temperature range of 430°C-500°C. The results showed that both neutron and DI irradiation of 21Cr32Ni model alloy produced dislocations in the form of a dislocation network as well as {111}-type faulted dislocation loops, cavities, and radiation-induced Ni enrichment at radiation-induced sinks. When the dose and the He/dpa ratio were kept similar to those in neutron irradiation, DI irradiation of the 21Cr32Ni model alloy at 460°C resulted in over-nucleation of small cavities and in a high density of faulted dislocation loops compared to those observed in the fast-neutron irradiated alloy of the same heat irradiated at ~380°C. The optimal condition for reproducing the neutron-irradiated microstructure was DI irradiation at 460°C and 0.1 appm He/dpa. In that case, the faulted loop and cavity size distributions in the BOR-60 irradiated 21Cr32Ni model alloy samples closely matched with those measured in the DI irradiated 21Cr32Ni model alloy sample. The fact that the He/dpa is an order of magnitude smaller than the helium generation rate for fast neutron irradiation, stops over nucleation and allows for the development of a similar microstructure as for neutron irradiation.

21Cr32Ni model alloy↗
DOE OSTI2024

Thermal and dynamic partition of dumbbell interstitials in complex concentrated alloys

Complex concentrated alloys (CCAs) are promising candidates for applications in extreme conditions, such as irradiation where interstitial mediated diffusion is important. In CCAs with N principal elements, $\frac{N(N+1)}{2}$ types of dumbbell interstitials exist. Currently, there is no way to predict the thermal partition (fractional concentration at equilibrium) and the dynamic partition (fractional time an interstitial spends during diffusion) of each type of dumbbell interstitial. To mitigate this issue, this work proposes a theoretical model for computing the equilibrium concentrations and thermal partition of dumbbell interstitials in CCAs and validates the model using grand canonical Monte Carlo simulations. Lattice kinetic Monte Carlo simulations show that the thermal partition is equivalent to the dynamic partition, and both are governed by composition and formation energies of dumbbells. As a result, the model proposed provides a foundation for understanding radiation enhanced diffusion and induced segregation in CCAs under irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rate-Based Absorption Modeling for Postcombustion CO 2 Capture with Additively Manufactured Structured Packing

Carbon capture using amine-based solvents in an absorption process is a leading candidate for reducing greenhouse gas emissions in industrial flue gas streams. To reduce operating costs and associated parasitic energy of these processes, process intensification utilizing additively manufactured structured packing has emerged as a new technology to manage exothermic reactions during absorption while improving CO 2 capture. A rate-based model framework has been developed for these novel packings that incorporates the mass and heat transfer phenomena for amine-based absorption of CO 2 . The rate-based model framework is first benchmarked using available solubility data and pilot plant data for aqueous monoethanolamine. The model validation shows accurate prediction of both CO 2 equilibrium partial pressures and ion speciation for solubility data as well as CO 2 capture, temperature profile, and solvent composition for pilot plant data in the absorption column. The model framework is then applied toward predicting the CO 2 capture performance of additively manufactured structured packing. Simulations agree with experimental data in predicting the CO 2 capture and the capture performance increase due to cooling within the structured packing device. Finally, advantages of this rate-based model framework are the utilization of correlations that may predict mass transfer and heat transfer coefficients of the packing based on the geometric properties of the device and the implementation of the model framework in open-source programming.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Synthetic observables for electron-capture supernovae and low-mass core collapse supernovae

Stars in the mass range from 8 M ⊙ to 10 M⊙ are expected to produce one of two types of supernovae (SNe), either electron-capture supernovae (ECSNe) or core-collapse supernovae (CCSNe), depending on their previous evolution. Either of the associated progenitors retain extended and massive hydrogen-rich envelopes and the observables of these SNe are, therefore, expected to be similar. In this study, we explore the differences in these two types of SNe. Specifically, we investigate three different progenitor models: a solar-metallicity ECSN progenitor with an initial mass of 8.8 M ⊙ , a zero-metallicity progenitor with 9.6 M ⊙ , and a solar-metallicity progenitor with 9 M ⊙ , carrying out radiative transfer simulations for these progenitors. Here, we present the resulting light curves for these models. The models exhibit very low photospheric velocity variations of about 2000 km s -1 ; therefore, this may serve as a convenient indicator of low-mass SNe. The ECSN has very unique light curves in broad-bands, especially the U band, and does not resemble any currently observed SN. This ECSN progenitor being part of a binary will lose its envelope for which reason the light curve becomes short and undetectable. The SN from the 9.6 M ⊙ progenitor exhibits also quite an unusual light curve, explained by the absence of metals in the initial composition. The artificially iron-polluted 9.6 M ⊙ model demonstrates light curves closer to normal SNe IIP. The SN from the 9 M ⊙ progenitor remains the best candidate for so-called low-luminosity SNe IIP like SN 1999br and SN 2005cs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Volatile organic compound emissions from 4D printing: Effects of material composition and external stimulus

4D printing of stimuli-responsive materials extends 3D printing by enabling the fabricated structures to transform their shapes and properties over time in response to external stimuli. Numerous research efforts have been dedicated to developing new smart materials, enhancing material printability, and ensuring time-evolving properties. Meanwhile, the use of smart materials and external stimuli in 4D printing has introduced the possibility of air emissions that can potentially deteriorate the indoor air quality at the workplace and pose continuous health hazards to users during the production and use phases. These potential air emissions caused by 4D printing have not yet been assessed in current literature, leading to unknown occupational hazards and human health effects. This study focuses on stereolithography-based 4D printing with constrained thermo-mechanics and builds an emission model to quantify the volatile organic compound emissions from printing, shape programming, and shape recovery stages. The established model mathematically links the emission characteristics with material compositional design and stimuli-response mechanisms. Additionally, shape fixity and recovery abilities are considered to analyze the trade-off between the air emissions and stimuli-response performance of 4D printed parts. Case study results suggest that the methacrylate-based thermo-responsive material with higher glass transition temperature leads to higher air emissions, surpassing the permissible exposure level in the indoor environment. By altering the thermo-temporal conditions, a 61.29% reduction in emission yield can be achieved while ensuring a satisfactory shape memory performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analytical Modeling of Exoplanet Transit Spectroscopy with Dimensional Analysis and Symbolic Regression

Abstract The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra, which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmospheric processes. The deep-learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. As a proof of concept, we successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot-Jupiter exoplanets to derive a corresponding analytical formula. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables and reduce the number of independent inputs, which improves the performance of the symbolic regression. The dimensional analysis also allowed us to mathematically derive and properly parameterize the most general family of degeneracies among the input atmospheric parameters that affect the characterization of an exoplanet atmosphere through transit spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗