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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dwarf Galaxies at Cosmic Noon: New JWST Constraints on Satellite Models and Subhalo Tidal Evolution

The advent of JWST has revolutionized the study of faint satellite galaxies at z ≳ 1, enabling statistical constraints on galaxy evolution and the galaxy–halo connection in a previously unexplored mass and redshift regime. We compare satellite abundances at 1 < z < 3.5 from recent JWST observations with predictions from cosmological dark-matter-only zoom-in simulations. We identify and quantify several sources of biases that can impact theoretical satellite counts, finding that assumptions about subhalo tidal evolution introduce the largest uncertainty in predictions for the satellite mass function. Using a flexible galaxy disruption model, we explore a range of disruption scenarios, spanning hydrodynamically motivated and idealized prescriptions, to bracket plausible physical outcomes. We show that varying galaxy durability can change the predicted satellite mass functions by a factor of ∼3.5. The JWST data and our fiducial model are consistent within 1σ–2σ across the full redshift (1 < z < 3.5) and stellar mass (M⋆ > 10 7 M ⊙ ) range probed. We find evidence that subhalos are at least as long-lived as predicted by hydrodynamic simulations. Our framework will enable robust constraints on the tidal evolution of subhalos with future observations. This work presents the first direct comparison between cosmological models and observations of the high-redshift satellite population in this low-mass regime. These results showcase JWST’s emerging power to test structure formation in the first half of the Universe in a new domain and to constrain the physical processes driving the evolution of low-mass galaxies across cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Transport model predictions of 225 Ac production cross sections via energetic p, d and α irradiation of 232 Th targets

Monte Carlo transport codes PHITS and MCNP6 were used to calculate the production cross sections of 225,227 Ac, 227,229 Th, 223,225 Ra, and 229,230,231 Pa via the bombardment of a 232 Th target with energetic protons, deuterons, and α-particles. The incident projectile energies ranged between 10 and 800 MeV/nucleon. When possible, the predicted production cross sections were compared with the available experimental data and other predictions. Here, the degree of the codes’ abilities to match the measured data provides a qualitative assessment of the codes’ abilities to predict data from similar, but unmeasured, projectile/target systems. In addition, a comparison between calculated cross sections and data may provide insight into possible improvements in the physics models employed by those transport codes.

223Ra↗
DOE OSTI2024

The impact of sampling scale: A comparison of methods for estimating external contaminant exposure in free-ranging wildlife

The impacts of contaminants on wildlife are dose dependent, and thus being able to track or predict exposure following contamination events is important for monitoring ecosystem health. However, the ability to track exposure in free-ranging wildlife is often severely limited. Consequently, researchers have predominantly relied on simple methods for estimating contaminant exposures in wildlife with little regard for spatial contaminant heterogeneity or an animal’s use of diverse habitats. We evaluated the influence sampling scale (i.e., how finely contaminant distribution and organism’s spatial use of the landscape is mapped) has on (1) realism and (2) conservativeness of exposure estimates. To do this, we monitored the actual exposure of wild boar (Sus scrofa) in Fukushima, Japan to radioactive contamination using GPS27 coupled contaminant monitors placed on individual animals. We compared empirical exposures to estimates generated by combining varying amounts of information about an individual boar’s location and/or movement, with the distribution of contamination on the landscape. Here we found that the most realistic exposure estimates were produced when finer-scale contaminant distribution surveys (e.g., airborne surveys) were combined with more accurate estimates of an individual’s space use (e.g., home ranges or core areas). Importantly, estimates of exposure based on single point surveys at a trap site (a simple method commonly used in the literature), did not correlate with actual exposure rates, suggesting dose-effects studies using this method may result in spurious conclusions. These results suggest that researchers seeking realistic estimates of exposure, such as in dose-effect studies, should ensure they have adequately accounted for fine-scale contaminant distribution patterns and areas of higher use by study organisms. However, conservative estimates of exposure i.e., intentionally over-predicting exposure as is done in initial tiers of ecological risk analyses) were not as scale sensitive and could be achieved with a single known location and coarse contaminant distribution maps.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

A review of spatial Markov models for predicting pre-asymptotic and anomalous transport in porous and fractured media

Heterogeneity across a broad range of scales in geologic porous media often manifests in observations of non-Fickian or anomalous transport. While traditional anomalous transport models can successfully make predictions in certain geological systems, increasing evidence suggests that assumptions relating to independent and identically distributed increments constrain where and when they can be reliably applied. A relatively novel model, the Spatial Markov model (SMM), relaxes the assumption of independence. The SMM belongs to the family of correlated continuous time random walks and has shown promise across a wide range of transport problems relevant to natural porous media. It has been successfully used to model conservative as well as more recently reactive transport in highly complex flows ranging from pore scales to much larger scales of interest in geology and subsurface hydrology. In this review paper we summarize its original development and provide a comprehensive review of its advances and applications as well as lay out a vision for its future development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems

Abstract. The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. With O(N 3 ) scaling with the number of electrons N, DMC has the potential to be a reference method for larger systems that are not accessible to more traditional methods such as CCSD(T). Assessing the accuracy of DMC for smaller molecules becomes the stepping stone in making the method a reference for larger systems. We show that when coupled with quantum machine learning (QML)-based surrogate methods, the computational burden can be alleviated such that quantum Monte Carlo (QMC) shows clear potential to undergird the formation of high-quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: the fixed-node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons-set-based QML (AQML) models. Numerical evidence presented includes converged DMC results for over 1000 small organic molecules with up to five heavy atoms used as amons and 50 medium-sized organic molecules with nine heavy atoms to validate the AQML predictions. Finally, numerical evidence collected for Δ-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

First-principles simulation of a shocked H-He mixture along the principal Hugoniot

Recent laser-shock experiments on an H–He mixture containing 11% helium (atomic fraction) have suggested the presence of an immiscibility region inside Jupiter. Reflectivity measurements were used as the primary diagnostic of H–He demixing, with discontinuities in the optical reflectivity proposed as a signature of phase separation under conditions relevant to Jupiter's interior. Here, we investigate shock-compressed H–He using ab initio molecular dynamics simulations with optical properties evaluated within the Kubo–Greenwood formalism. The equation of state and ionic configurations were obtained using the thermal Tr 2 SCANL meta-GGA exchange–correlation (XC) functional, while optical properties were computed using the recently developed RS-KDT0 range-separated thermal hybrid XC, which provides state-of-the-art accuracy for band-gap predictions in the warm dense matter regime. The calculated reflectivity shows overall good agreement with experimental measurements; however, no discontinuity is observed at elevated temperatures. Moreover, the reflectivity predictions for the mixed system are consistent with the experimental measurements in the temperature range where the mixture is inferred to be demixed. Furthermore, these results suggest that reflectivity alone may not provide a unique or sensitive diagnostic of H–He demixing at low helium concentrations under these conditions.

Density functional calculations↗
DOE OSTI2020

Synthesis and structural characterization of the new Zintl phases Ba 3 Cd 2 P 4 and Ba 2 Cd 2 P 3 . Rare example of small gap semiconducting behavior with negative thermopower within the range 300 K-700 K

The new Zintl phases Ba 3 Cd 2 P 4 and Ba 2 Cd 2 P 3 have been synthesized using Pb flux, which allowed for the growth of 4-5 mm large crystals. The structures were determined utilizing single-crystal X-ray diffraction methods. Both compounds crystalize in the monoclinic crystal system (space group C2/m (No. 12)) and their structures are closely related. The structure of Ba 3 Cd 2 P 4 can be seen as being comprised of divalent Ba atoms and conjoined CdP 4 tetrahedra in the form of [Cd 2 P 4 ] 6- layers. Within the layers, homoatomic P–P bonds are present, which if cleaved, leave two infinite [CdP 3 ] 7- chains running along the crystallographic b-axis. The other structure, that of Ba 2 Cd 2 P 3 , can be rationalized as also having divalent Ba atoms and conjoined CdP4 tetrahedra in the form of [Cd 2 P 3 ] 6- layers. These layers, again, can be visualized as chains that run down the crystallographic b-axis, which are further connected by P-P dimers. Electronic band structure calculations show that each structure has an optimal number of valence electrons, and therefore conform to the Zintl-Klemm concept. Accordingly, the two compounds can be considered small band gap semiconductors, with band gaps of ca. 0.1 eV and 0.6 eV for Ba3Cd2P4 and Ba 2 Cd 2 P 3 , respectively. Electrical resistivity measurements show that Ba3Cd2P4 displays a large resistivity value at room temperature and an experimental band gap of ca. 0.05 eV, which fits reasonably well with the theoretical predictions. Thermopower measurements show that throughout the temperature range 300 K-700 K, Ba 3 Cd 2 P 4 displays a negative Seebeck coefficient. Here, the extremum value of -84 μV is reached at 630 K, suggestive of an n-type semiconductor, a rarity among Zintl phases.

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

Expanded verification and validation studies of hypersonic aerodynamics with multiple physics-fidelity models

Hypersonic aerothermodynamics is an important domain of modern multiphysics simulation. The Multi-Fidelity Toolkit is a simulation tool being developed at Sandia National Laboratories to predict aerodynamic properties for compressible flows from a range of physics fidelities and computational speeds. These models include the Reynolds-averaged Navier–Stokes (RANS) equations, the Euler equations with momentum-energy integral technique (MEIT), and modified Newtonian aerodynamics with flat-plate boundary layer (MNA+FPBL) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal code- and solution-verification activities, as well as model validation with uncertainty considerations. Code verification activities on the MNA+FPBL model build on previous work by focusing on the viscous portion of the model. Viscous quantities of interest are compared against those from an analytical solution for flat-plate, inclined-plate, and cone geometries. The code verification methodology for the MEIT model is also presented. Test setup and results of code verification tests on the laminar and turbulent models within MEIT are shown. Solution-verification activities include grid-refinement studies on simulations that model the HIFiRE-1 wind tunnel experiments. These experiments are used for validation of all model fidelities. A thorough validation comparison with prediction error and uncertainty is also presented. Three additional HIFiRE-1 experimental runs are simulated in this study, and the solution verification and validation work examines the effects of the associated parameter changes on model performance. Finally, a study is presented that compares the computational costs and fidelities from each of the different models.

42 ENGINEERING↗
DOE OSTI2021

Climate-driven deoxygenation elevates fishing vulnerability for the ocean's widest ranging shark

Climate-driven expansions of ocean hypoxic zones are predicted to concentrate pelagic fish in oxygenated surface layers, but how expanding hypoxia and fisheries will interact to affect threatened pelagic sharks remains unknown. Here, analysis of satellite-tracked blue sharks and environmental modelling in the eastern tropical Atlantic oxygen minimum zone (OMZ) shows shark maximum dive depths decreased due to combined effects of decreasing dissolved oxygen (DO) at depth, high sea surface temperatures, and increased surface-layer net primary production. Multiple factors associated with climate-driven deoxygenation contributed to blue shark vertical habitat compression, potentially increasing their vulnerability to surface fisheries. Greater intensity of longline fishing effort occurred above the OMZ compared to adjacent waters. Higher shark catches were associated with strong DO gradients, suggesting potential aggregation along suitable DO gradients contributed to habitat compression and higher fishing-induced mortality. Fisheries controls to counteract deoxygenation effects on shark catches will be needed as oceans continue warming.

Vedor, Marisa↗
DOE OSTI2024

Towards predictive control of reversible nanoparticle assembly with solid-binding proteins

Although a broad range of ligand-functionalized nanoparticles and physico-chemical triggers have been exploited to create stimuli-responsive colloidal systems, little attention has been paid to the reversible assembly of unmodified nanoparticles with non-covalently bound proteins. Previously, we reported that a derivative of green fluorescent protein engineered with oppositely located silica-binding peptides mediates the repeated assembly and disassembly of 10-nm silica nanoparticles when pH is toggled between 7.5 and 8.5. We captured the subtle interplay between interparticle electrostatic repulsion and their protein-mediated short-range attraction with a multiscale model energetically benchmarked to collective system behavior captured by scattering experiments. Here, in this work, we show that both solution conditions (pH and ionic strength) and protein engineering (sequence and position of engineered silica-binding peptides) provide pathways for reversible control over growth and fragmentation, leading to clusters ranging in size from 25 nm protein-coated particles to micrometer-size aggregate. We further find that the higher electrolyte environment associated with successive cycles of base addition eventually eliminates reversibility. Our model accurately predicts these multiple length scales phenomena. The underpinning concepts provide design principles for the dynamic control of other protein- and particle-based nanocomposites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Predicting the Solubility of Inorganic Ion Pairs in Water

Abstract Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali‐counterions can inform natural and synthetic aqueous processes. In the study of POMs ([Nb 24 O 72 H 9 ] 15− , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali‐POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ‐profile) and use an artificial neural network (ANN) to predict all three described alkali‐anion solubility trends: amphoteric, normal (Li + >Na + >K + >Rb + >Cs + ), and anomalous (Cs + >Rb + >K + >Na + >Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution‐phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14− , and provides guidelines to exploit alkali–POM interactions for new POMs discovery.

Rahman, Tasnim↗
DOE OSTI2023

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows

This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Characterization of Lanthanum Monazite Surface Chemistry and Crystal Morphology through Density Functional Theory and Experimental Approaches

Monazite is a rare earth element (REE)-containing mineral that consists of (REE)PO 4 formal units and is one of the most important sources of these critical materials. The concentration of REEs from mined monazite ore often involves froth flotation, which is a beneficiation process that enhances the efficiency of downstream processing. The effectiveness of froth flotation is largely governed by the ability of collector agents to selectively bind to monazite particles. Thus, a molecular-level understanding of monazite interfacial chemistry is integral to the design of effective collector agents. To address this need, we performed density functional theory (DFT) calculations and a variety of experimental techniques to characterize La-monazite and elucidate its crystal morphology. Interestingly, we find minimal differences in the predicted morphologies of La-monazite for hydrous and anhydrous environments, which are largely dominated by low-index facets (e.g., {110}, {100}, and {010}). Indexing of synthesized La-monazite crystals via X-ray diffraction also uncovers {110} and {100} as the predominant facets. The average surface energies of 0% and 100% water coverage La-monazite crystals were predicted to be 0.87 and 0.76 J/m 2 , respectively, while calorimetry suggests values of 1.30 and 1.15 J/m 2 , respectively. The apparent discrepancies between the theoretical and experimental values are expected and attributed to defects present in physical crystals, in contrast to the perfect mineral surfaces in simulations. The difference in surface energy between the 0% and 100% water coverage morphologies predicted by theory is consistent with the value measured via calorimetry. DFT reveals a wide range of adsorption energies for water across the studied facets, but in all cases, water is predicted to strongly bind to monazite surfaces with an average adsorption energy of -92.7 kJ/mol for a La-monazite single crystal. Finally, this study provides the groundwork necessary for the rational design of froth flotation collector agents by granting molecular-level insight into the predominant facets of monazite.

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

Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004)

Background: We sought to predict clinically meaningful changes in physical, sexual, and psychosocial well-being for women undergoing cancer-related mastectomy and breast reconstruction 2 years after surgery using machine learning (ML) algorithms trained on clinical and patient-reported outcomes data. Patients and Methods: We used data from women undergoing mastectomy and reconstruction at 11 study sites in North America to develop three distinct ML models. We used data of ten sites to predict clinically meaningful improvement or worsening by comparing pre-surgical scores with 2 year follow-up data measured by validated Breast-Q domains. We employed ten-fold cross-validation to train and test the algorithms, and then externally validated them using the 11th site’s data. We considered area-under-the-receiver-operating-characteristics-curve (AUC) as the primary metric to evaluate performance. Results: Overall, between 1454 and 1538 patients completed 2 year follow-up with data for physical, sexual, and psychosocial well-being. In the hold-out validation set, our ML algorithms were able to predict clinically significant changes in physical well-being (chest and upper body) (worsened: AUC range 0.69–0.70; improved: AUC range 0.81–0.82), sexual well-being (worsened: AUC range 0.76–0.77; improved: AUC range 0.74–0.76), and psychosocial well-being (worsened: AUC range 0.64–0.66; improved: AUC range 0.66–0.66). Baseline patient-reported outcome (PRO) variables showed the largest influence on model predictions. Conclusions: Machine learning can predict long-term individual PROs of patients undergoing postmastectomy breast reconstruction with acceptable accuracy. This may better help patients and clinicians make informed decisions regarding expected long-term effect of treatment, facilitate patient-centered care, and ultimately improve postoperative health-related quality of life.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Improved departure from nucleate boiling prediction in rod bundles using a physics-informed machine learning-aided framework

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is a regulatory limit for the licensing of pressurized water reactors (PWRs) worldwide. Despite the abundance of predictive tools available to the reactor thermal-hydraulics community, the path for an accurate CHF model remains elusive. This work approaches the prediction of DNB through a physics-informed machine learning-aided framework (PIMLAF) with the objective of achieving superior predictive capabilities for a rod bundle. In view of the limitations in existing macro-scale physics-driven tools, an improved mechanistic model is first proposed, leveraging key concepts in the liquid sublayer dryout and bubble crowding mechanisms. Furthermore, the proposed mechanistic model is able to predict DNB in different heater geometries for a broad range of flow conditions without the need for recalibration. This model is then incorporated as the physics-informed component of the hybrid framework PIMLAF, which takes advantage of established understanding in the field (i.e., domain knowledge [DK]) and uses machine learning (ML) to capture undiscovered information from the mismatch between the actual and DK-predicted output. Two bundle-related case studies using the PWR subchannel and bundle tests (PSBT) database are carried out to illustrate the PIMLAF’s improved performance over traditional approaches for both interpolation and extrapolation purposes. In light of the PIMLAF’s promising potential to reduce prediction error, reactor vendors are encouraged to leverage their in-house experimental efforts and apply the hybrid framework to potentially achieve margin reductions in the minimum DNB ratio (MDNBR) for the designs of interest.

42 ENGINEERING↗
DOE OSTI2021

Development of a Turbulent Liquid Spray Atomization Model for Diesel Engine Simulations (Final Technical Report)

This project addresses the systematic lack of predictive capabilities by spray models within engine CFD codes. We develop a new modeling approach to predict the breakup of diesel sprays based on recent literature showing that liquid turbulence plays a fundamental role in spray atomization. A new body of quantitative validation data is also developed as a critical element of the project, leveraging the joint capabilities of Georgia Tech’s high-pressure continuous-flow spray chamber and Argonne National Lab’s near-nozzle x-ray diagnostics at the Advanced Photon Source. This project contributes spatially-resolved measurements of drop size distribution within well-characterized diesel injectors, Spray A and D, from the Engine Combustion Network (ECN) to the engine combustion community for the first time. Utilizing this new body of measurements, we validate and demonstrate a new spray model for diesel sprays, termed the KH-Faeth model, that predicts global and local spray characteristic more accurately than the widely adopted and employed KH model. Predicted drop size distributions are seen to predict measured drops sizes both quantitatively and predictively, with accurate response in droplet size distributions over a wide range of ambient density, injection pressure, and injector nozzle size (Spray A and D) without model tuning. The KH-Faeth model can reduce error in the predicted centerline droplet size profile by up to 80% for ECN Spray D simulations when compared to use of the widely employed KH model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Grain Boundary Segregation Suppresses Local Short‐Range Ordering in Nanocrystalline High‐Entropy Alloys

Multi-principal-element alloys like high-entropy alloys (HEAs) have potential applications in many engineering fields due to their unique mechanical/functional properties. While HEAs are generally considered random solid solutions, recent studies revealed that they are prone to short-range-ordering (SRO) due to the complex multi-pair-wise interactions among the constituent elements. Meanwhile, SROs' evolution can sometimes be deleterious, and it is necessary to have control over their evolution. Examining the AlCoCrFe-Zr model alloy, long-range ordering occurs following the expectation of enthalpic predictions. Advanced characterization techniques—transmission electron microscopy, high-energy synchrotron X-ray diffraction/pair distribution function, and atom probe tomography, reveal that SRO is suppressed in as-milled and GB-decorated NC-(AlCoCrFe)100-xZrx (x = 0–1.5 atomic %). Warren-Cowley coefficient calculations are further used to validate the suppression of SRO. Besides the low segregation enthalpies of Cr, Fe, and Zr, and the high-mixing enthalpy of Cr and Fe, the short diffusion path to GBs due to high-GB density in the NC-HEAs and the higher energy state of the GBs than the matrix promotes GB-segregation that further alters the matrix chemistry and consequently disfavors SRO formation within the matrix. Despite the GB-segregation of Cr, Fe, and Zr, the matrices and GBs remain in a random solid solution.

36 MATERIALS SCIENCE↗