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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Computer Modeling Of Atomization

Improved mathematical models based on fundamental principles of conservation of mass, energy, and momentum developed for use in computer simulation of atomization of jets of liquid fuel in rocket engines. Models also used to study atomization in terrestrial applications; prove especially useful in designing improved industrial sprays - humidifier water sprays, chemical process sprays, and sprays of molten metal. Because present improved mathematical models based on first principles, they are minimally dependent on empirical correlations and better able to represent hot-flow conditions that prevail in rocket engines and are too severe to be accessible for detailed experimentation.

Giridharan, M.↗
DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Interpreting High-resolution Spectroscopy of Exoplanets using Cross-correlations and Supervised Machine Learning

We present a new method for performing atmospheric retrieval on ground-based, high-resolution data of exoplanets. Our method combines cross-correlation functions with a random forest, a supervised machine-learning technique, to overcome challenges associated with high-resolution data. A series of cross-correlation functions are concatenated to give a “CCF-sequence” for each model atmosphere, which reduces the dimensionality by a factor of ∼100. The random forest, trained on our grid of ∼65,000 models, provides a likelihood-free method of retrieval. The precomputed grid spans 31 values of both temperature and metallicity, and incorporates a realistic noise model. We apply our method to HARPS-N observations of the ultra-hot Jupiter KELT-9b and obtain a metallicity consistent with solar (logM = − 0.2 ± 0.2). Our retrieved transit chord temperature (T=6000{sub −200}{sup +0}K) is unreliable as strong ion lines lie outside of the extent of the training set, which we interpret as being indicative of missing physics in our atmospheric model. We compare our method to traditional nested sampling, as well as other machine-learning techniques, such as Bayesian neural networks. We demonstrate that the likelihood-free aspect of the random forest makes it more robust than nested sampling to different error distributions, and that the Bayesian neural network we tested is unable to reproduce complex posteriors. We also address the claim in Cobb et al. 2019 that our random forest retrieval technique can be overconfident but incorrect. We show that this is an artifact of the training set, rather than of the machine-learning method, and that the posteriors agree with those obtained using nested sampling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Quarterly Management Document – FY20, 2nd Quarter, Physics-based Creep Simulations of Thick Section Welds in High Temperature and Pressure Applications

During the 2nd quarter of FY20, calibration of the creep model was made using experimental creep data on base metal at temperatures between 700-800 C. The model simulated primary and secondary creep behavior at these temperatures but tended to over estimate creep strain in the tertiary regime at 700 C while underestimating it at higher temperatures. Additional issues were also discovered and will be looked at in greater detail in the 3rd quarter of FY20.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mean-field model for the Curie-Weiss temperature dependence of coherence length in metallic liquids

The coherence length of the medium-range order (MRO) in metallic liquids is known to display a Curie-Weiss temperature dependence; its inverse is linearly related to temperature, and when extrapolated from temperatures above the glass transition, the coherence length diverges at a negative temperature with a critical exponent of unity. We propose a mean-field pseudospin model that explains this behavior. Specifically, we model the atoms and their local environment as Ising spins with antiferromagnetic exchange interactions. We further superimpose an exchange interaction between dynamical heterogeneities, or clusters of atoms undergoing cooperative motion. The coherence length in the metallic liquid is thus the correlation length between dynamical heterogeneities. Overall, our results reaffirm the idea that the MRO coherence length is a measure of point-to-set correlations, and that local frustrations in the interatomic interactions are prominent in metallic liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Influence of aluminum incorporation and aqueous conditions on metal ion release of high-Ni transition metal oxide nanomaterials

Developing a materials perspective of how to control the degradation and negative impact of complex metal oxides requires an integrated understanding of how these nanomaterials transform in the environment and interact with biological systems. Doping with aluminum is known to stabilize oxide materials, but has not been assessed cohesively from synthesis to environmental fate and biological impact. In the present study, the influence of aluminum doping on metal ion release from transition metal oxides was investigated by comparing aqueous transformations of lithium nickel cobalt aluminum oxide (LiNi 0.82 Co 0.15 Al 0.03 O 2 ; NCA) and lithium nickel cobalt oxide (LiNi 0.80 Co 0.20 O 2 ; NC) nanoparticles and by calculating the energetics of metal release using a density functional theory (DFT) and thermodynamics method. Two model environmental organisms were used to assess biological impact, and metal ion release was compared for NCA and NC nanoparticles incubated in their respective growth media: moderately hard reconstituted water (MHRW) for the freshwater invertebrate Daphnia magna (D. magna) and minimal growth medium for the Gram-negative bacterium Shewanella oneidensis MR-1 (S. oneidensis). Further, the amount of metal ions released was reduced for NCA compared to NC in MHRW, which correlated to changes in the modeled energetics of release upon Al substitution in the lattice. In minimal medium, metal ion release was approximately an order of magnitude higher compared to MHRW and was similar to the stoichiometry of the bulk nanoparticles for both NCA and NC. Interpretation of the release profiles and modeling indicated that the increase in total metal ion release and the reduced influence of Al doping arises from lactate complexation of metal ions in solution. The relative biological impacts of NC and NCA exposure for both S. oneidensis and D. magna were consistent with the metal release trends observed for minimal medium and MHRW, respectively. Together, these results demonstrate how a combined experimental and computational approach provides valuable insight into the aqueous transformations and biological impacts of complex metal oxide nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1997

Interaction of Burning Metal Particles

Multiple particle/droplet flames are ubiquitous in practical combustion systems, and thus the flame interaction processes are of great practical importance. This explains the strong current interest in interactive combustion phenomena. This research is aimed at the investigation of combustion parameters of microgravity model aerosols: relatively large uniform metal particles aerosolized in microgravity environment. An experiment consisting of creation and ignition of a metal multiparticle system in microgravity and high-speed video-recording of the combustion events will produce visual records of the development of individual particle flames, their interactions and the particle motion they induce simultaneously with the observation of the entire aerosol combustion process. Frame by frame analysis of the video-images taken using a high-speed movie camera will allow one to determine particle brightness temperatures and the decrease in particle diameter during combustion. Analysis of the experimental results and comparison with the results of single metal particle combustion experiments, conducted under similar microgravity conditions in the framework of a parallel program, will establish the relationship between single and multiple particle burning rates and combustion temperatures, concentrations at which the flame substructure forms rather than individual particle flames, efficiency of radiative heat transfer in metal aerosol combustion, what is the role of electrostatic forces in structuring the flame and the effect of that structure on the flame propagation rate. Although some details of fine particle aerosol clouds, such as the kinetics limited burning rate, radiative heat transfer in a system with a high specific surface, particle induced turbulence, etc., will probably not be very well simulated in the planned experiments, they are relatively well understood and can be accounted for using an adequate individual particle combustion model. On the other hand, the unknown particle interaction phenomena to be studied in this research program are generic and can be readily projected onto a cloud combustion model which would also incorporate a correction for particle size.

Dreizin, Edward L.↗
DOE OSTI2020

A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

First-principles Modeling and Design of Solid-State Interfaces for the Protection and Use of Lithium Metal Anodes

Li-ion batteries are one of the most advanced energy storage technologies in use today. Li-ion batteries are used in a multitude of applications ranging from consumer electronics, medical devices, sensors and grid storage. However, improving the capacity and energy density delivered by current Li-ion technology requires advanced materials research into novel chemical systems. In this project we have focused particularly in the use of solid-state electrolytes with lithium metal electrodes. Research into all solid-state batteries (ASSB) with Li metal electrodes has significantly expanded in recent years, however most studies reported experimental findings, which left substantial room for theoretical and modeling work as a tool to understand and determine design principles allowing reliable and safe use of ASSBs with Li metal. Among the remaining obstacles preventing reliable use of ASSBs with a Li metal electrode, the stability of the interface between the solid electrolyte and Li metal, and the propagation/dendrite formation of Li metal and resulting mechanical degradation of the electrolyte are key phenomenon that are yet to be fully understood. In the current project we have addressed these two coupled phenomena using first principles calculations and mesoscale continuum modeling. We have obtained chemical and electrochemical stability windows for several solid electrolyte materials. Additionally, from mathematical and numerical modeling of Li protrusion and dendrite initiation during plating and stripping we have determined design criteria in terms of chemical, electrochemical, and mechanical properties and operating conditions for which stable deposition can occur. We also considered the effects of mixed electronic-ionic conduction in solid electrolytes, which has more recently been suggested as another important mechanism involved in ASSB failure. Throughout our work we have successfully addressed important questions necessary for the use of ASSB’s. We have determined guiding principles for materials properties and operating conditions necessary to operate ASSB’s. And have proposed novel solid electrolyte materials with predicted chemical stability an ionic conductivity. Although this represents significant progress in our understanding, open questions remain in order to fully develop reliable and safely operate ASSBs with Li metal. Future work, building on this project will require further experimental, theoretical and simulation efforts to address remaining questions.

25 ENERGY STORAGE↗
DOE OSTI2023

Localization of Zn 2+ ions affects the structural folding and mechanics of Nereis virens Nvjp-1

Several biological organisms utilize metal-coordination bonds to produce remarkable materials, such as the jaw of the marine worm Nereis virens, where metal-coordination bonds yield remarkable hardness without mineralization. Though the structure of a major component of the jaw, the Nvjp-1 protein, has recently been resolved, a detailed nanostructural understanding of the role of metal ions on the structural and mechanical properties of the protein is missing, especially with respect to the localization of metal ions. In this work, atomistic replica exchange molecular dynamics with explicit water and Zn 2+ ions and steered molecular dynamics simulations were used to explore how the initial localization of the Zn 2+ ions impacts the structural folding and mechanical properties of Nvjp-1. We found that the initial distribution of metal ions for Nvjp-1, and likely for other proteins with high amounts of metal-coordination, has important effects on the resulting structure, with larger metal ion quantity resulting in a more compact structure. These structural compactness trends, however, are independent from the mechanical tensile strength of the protein, which increases with greater hydrogen bond content and uniform distribution of metal ions. Our results indicate that different physical principles underlie the structure or mechanics of Nvjp-1, with broader implications in the development optimized hardened bioinspired materials and the modeling of proteins with significant metal ion content.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Computer modeling of inversion layer MOS solar cells and arrays

A two dimensional numerical model of the inversion layer metal insulator semiconductor (IL/MIS) solar cell is proposed by using the finite element method. The two-dimensional current flow in the device is taken into account in this model. The electrostatic potential distribution, the electron concentration distribution, and the hole concentration distribution for different terminal voltages are simulated. The results of simple calculation are presented. The existing problems for this model are addressed. Future work is proposed. The MIS structures are studied and some of the results are reported.

Ho, Fat Duen↗
NASA NTRS1989

CO formation in the metal-rich ejecta of SN 1987A

A model for the formation of CO in the expanding ejecta of SN 1987A suggests that the 4.6-micron and 2.3 micron emissions noted in the SN spectrum may originate from the inner metal-rich regions of the ejecta. Also reported are the assignment of a 2.26-micron excess feature in the high-resolution IR spectra to the v = 2 to 0 overtone of CO(+) and the possible identification of a 2.95-micron feature with the v = 2 to 0 transition of electronically excited CO. The results suggest that the regions in which CO formation takes place must be ionized to a substantially lower degree than regions which dominate the line emission of heavier metals.

Petuchowski, S. J.↗
DOE OSTI2020

Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. In this work, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

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

Thermal-Mechanical Elastic-Plastic and Ductile Failure Model Calibrations for 304L Stainless Steel Alloy

Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The material of interest here is 304L stainless steel, and the objective of this work is to calibrate thermal-mechanical models: one for the constitutive behavior and another for failure. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of the models. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10 –4 and 10 3 1/s. It also included notched tension and shear-dominated compression hat tests specifically designed to calibrate the failure model. All test specimens were extracted from a single piece of plate to maintain consistency. The constitutive model adopted was a modular $J_2$ plasticity model with isotropic hardening that included rate and temperature dependence. A criterion for failure initiation based on a critical value of equivalent plastic strain fitted the failure data appropriately and was adopted. Possible ranges of the values of the parameters of the models were determined partially on historical data from calibrations of the same alloy from other lots and are given here. The calibration of the parameters of the models were based on finite element simulations of the various material tests using relatively ne meshes and hexahedral elements. When using the model in structural finite element calculations, however, element formulations and sizes different from those in the calibration are likely to be used. A brief investigation demonstrated that the failure initiation predictions can be particularly sensitive to the element selection and provided an initial guide to compensate for the effect of element size in a specific example.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating the effects of local environment on nitrogen vacancies in high-entropy metal nitrides

High-entropy metal nitrides are an important material class in a variety of applications, and the role of nitrogen vacancies is of great importance for understanding their stability and mechanical properties. Here, we study six different high-entropy nitrides with eight different metal species to build a predictive model of the nitrogen-vacancy formation energy. We construct sets of supercells that maximize the number of unique nitrogen environments for a given chemistry, and then use density-functional theory to calculate the energy density for all nitrogen sites, and the vacancy formation energies for the highest, lowest, and a median subset based on the energy densities. The energy density of nitrogen sites correlates with the vacancy formation energies, for binary, ternary, and high-entropy nitrides. A linear regression model predicts the vacancy formation energies using only the nearest-neighbor composition; across our eight metals, we find the largest vacancy formation energies next to Hf, then Zr, Ti, V, Cr, Ta, Nb, and the lowest near Mo. Additionally, we see that binary nitride data show qualitatively similar vacancy formation energy trends for high-entropy nitrides; however, the binary data alone are insufficient to predict the complex nitride behavior. Our model is both predictive and easily interpretable, and correlates with experimental data.

DeSilva, Charith R. [Univ. of Illinois at Urbana-C↗
DOE OSTI2022

Carbonylative Co- and Terpolymerizations of 10-Undecen-1-ol: A Route to Polyketoesters with Tunable Compositions

A strategy to synthesize branched polyketoesters from the carbonylative polymerization of bifunctional α,ω-alkenols such as 10-undecen-1-ol is presented. This strategy hinges on the competitive application of two related catalytic manifolds, alternating alkene/CO copolymerization, and alkene hydroesterification, which share a common metal acyl intermediate. Small molecule model studies of cationic Pd-catalyzed alkene carbonylation in the presence of alcohols demonstrate that the relative rates of ketone formation (through alternating alkene/CO insertion) and ester formation (through metal acyl alcoholysis) can be tuned across a wide range through judicious bis(phosphine) ligand design. Carbonylative polymerization of 10-undecen-1-ol with a (dppp(3,5-CF 3 ) 4 )Pd(OTs) 2 catalyst (dppp(3,5-CF 3 ) 4 = 1,3-bis[bis[3,5-bis(trifluoromethyl)phenyl]-phosphino]propane) led to the formation of high molecular weight polyketoesters with intermediate dispersity (M n > 20,000 g/mol, D- = 2.6) and a ketone/ester microstructure ratio of approximately 1:2. In these polymerization reactions, deploying electron-deficient bis(phosphines) to suppress deleterious alkene isomerization was the key to accessing the high molecular weight polymer. Further, terpolymerization reactions of 1-hexene/10-undecen-1-ol/CO or 1-fluoro-10-undecene/10-undecen-1-ol/CO by (dppp(3,5-CF 3 ) 4 )Pd(OTs) 2 were also successful. Furthermore, this proof of concept polymerization unlocks access to tunable polymer microstructures without extensive postpolymerization treatment.

10-undecen-1-ol↗