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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Quantitative SANS and multi-model analysis of spacer-dependent micellization of urea-based gemini surfactants

The micellization behavior of urea-based cationic gemini surfactants was investigated using small-angle neutron scattering (SANS) with multi-model form factor analysis. A homologous series of surfactants with urea group included in the hydrophobic tail and polymethylene spacers consisting of two to ten methylene units was analyzed using three form factor models: a core–shell ellipsoid and two variants of homogeneous ellipsoids. The results from all models show a consistent trend of the micelle structures, confirming that the spacer length critically influences micellar geometry, aggregation number, and hydration. The surfactant with four CH 2 groups in the spacer formed the largest micelles with the highest aggregation number, while longer spacers led to progressively smaller, more compact aggregates. The shell hydration—quantified as the volume fraction of heavy water within the hydrophilic region—decreased systematically with increasing spacer length due to enhanced hydrophobicity of the headgroup-spacer region. Intermicellar interactions, modeled as screened Coulomb interaction using the rescaled mean spherical approximation (RMSA), revealed the strongest electrostatic repulsion for the case of four methylene groups in the spacer, corresponding to the highest micellar charge and largest interparticle spacing. The observed spacer-dependent trends were robust across all modeling approaches, demonstrating that the spacer length serves as a key structural determinant of self-assembly in this type of urea-based gemini systems. These findings provide insight into the design of gemini surfactants with tailored aggregation behavior for applications in drug delivery, nanostructure templating, and solubilization technologies.

Core–shell ellipsoid model↗
DOE OSTI2022

Robust Optimization for the Day-Ahead Scheduling of Cascaded Hydroelectric Systems

Uncertain electricity prices resulting from the re-structuring of electricity market have brought new opportu-nities and challenges for hydroelectric producers. This paper presents a data-driven robust optimization approach for the day-ahead scheduling of cascaded hydroelectric systems (CHS) with electricity price uncertainty. In this paper, a minimum volume enclosing ellipsoid (MVEE) is adopted to construct an ellipsoidal uncertainty set that fully identifies and exploits the historical characteristics data. Here, a second-order conic optimization formulation of the robust counterpart is derived for efficient computation. A real-world case study is conducted to demonstrate the capability of the proposed optimization approach compared with the traditional robust optimization approach.

13 HYDRO ENERGY↗
NASA NTRS2015

The Dependence of Cirrus Gamma Size Distributions Expressed as Volumes in N(sub 0)-Lambda-Mu Phase Space and Bulk Cloud Properties on Environmental Conditions: Results from the Small Ice Particles in Cirrus Experiment (SPARTICUS)

The variability of cirrus ice microphysical properties is investigated using observations obtained during the Small Particles in Cirrus (SPARTICUS) campaign. An existing approach that represents a size distribution (SD) as a single gamma function using an ellipsoid of equally realizable solutions in (N(sub 0), lambda, mu) phase space is modified to automatically identify multiple modes in SDs and characterize each mode by such an ellipsoid. The modified approach is applied to ice crystals with maximum dimension D greater than15 micrometers collected by the 2-D stereo and 2-D precipitation probes on the Stratton Park Engineering Company Learjet. The dependencies of N(sub 0), mu, and lambda from each mode, total number concentration, bulk extinction, ice water content (IWC), and mass median maximum dimension D(sub mm) as a function of temperature T and cirrus type are then analyzed. The changes in the observed codependencies between N(sub 0), mu, and lambda, bulk extinction, IWC, and D(sub mm) with environmental conditions indicate that particles were larger at higher T during SPARTICUS. At most two modes were observed in any SD during SPARTICUS, with the average boundary between them at 115 micrometers, similar to past studies not using probes with shatter mitigating tips and artifact removal algorithms. The bimodality of the SDs increased with T. This and the differences in N(sub 0), mu, and lambda between the modes suggest that particles with smaller D nucleated more recently than particles with larger D, which grew via vapor deposition and aggregation. Because smaller crystals, whose concentrations are uncertain, make marginal contributions to higher order moments, the use of higher moments for evaluating model fields is suggested.

Ellipsoids↗
DOE OSTI2026

Effect of magneto-mechanical synergism in the process-structure correlation in Fe–C alloys: A phase-field modeling approach

Applied magnetic fields can alter phase equilibria and kinetics in steels; however, quantitatively resolving how magnetic, chemical, and elastic driving forces jointly influence the microstructure remains challenging. We develop a quantitative magneto-mechanically coupled phase-field model for the Fe–C system that couples a CALPHAD-based chemical free energy with demagnetization-field magnetostatics and microelasticity. Here, the model reproduces single- and multi-particle evolution during the α → γ inverse transformation at 1023 K under external fields up to 20 T, including ellipsoidal morphologies observed experimentally at 8 T. Chemically driven growth is isotropic; a magnetic interaction introduces an anisotropic driving force that elongates γ precipitates along the field into ellipsoids, while elastic coherency promotes faceting, yielding elongated cuboidal or “brick-like” particles under combined magneto-elastic coupling. Growth kinetics increase with C content, and decrease with field strength and misfit strain. Multi-particle simulations reveal dipolar interaction-mediated coalescence for field-parallel neighbors and ripening for field-perpendicular neighbors. Incorporating field-dependent diffusivity from experiment slows kinetics as expected; a first-principles-motivated anisotropic diffusivity correction is estimated to be small (<2%). These results establish a process-structure link for magnetically assisted heat treatments of Fe–C alloys and provide guidance for microstructure control via chemo-magneto-mechanical synergism.

Magnetic field↗
DOE OSTI2021

Anisotropy evolution of elastomeric foams during uniaxial compression measured via in-situ X-ray computed tomography

We have characterized the three-dimensional evolution of microstructural anisotropy of a family of elastomeric foams during uniaxial compression via in-situ X-ray computed tomography. Flexible polyurethane foam specimens with densities of 136, 160 and 240 kg/m 3 were compressed in uniaxial stress tests both parallel and perpendicular to the foam rise direction, to engineering strains exceeding 70%. The uncompressed microstructures show slightly elongated ellipsoidal pores, with elongation aligned parallel to the foam rise direction. The evolution of this microstructural anisotropy during deformation is quantified based on the autocorrelation of the image intensity, and verified via the mean intercept length as well as the shape of individual pores. Trends are consistent across all three methods. In the rise direction, the material remains transversely anisotropic throughout compression. Anisotropy initially decreases with compression, reaches a minimum, then increases up to large strains, followed by a small decrease in anisotropy at the largest strains as pores collapse. Compression perpendicular to the foam rise direction induces secondary anisotropy with respect to the compression axis, in addition to primary anisotropy associated with the foam rise direction. In contrast to compression in the rise direction, primary anisotropy initially increases with compression, and shows a slight decrease at large strains. These surprising non-monotonic trends and qualitative differences in rise and transverse loading are explained based on the compression of initially ellipsoidal pores. Microstructural anisotropy trends reflect macroscopic stress-strain and lateral strain response. These findings provide novel quantitative connections between three-dimensional microstructure and anisotropy in moderate density polymer foams up to large deformation, with important implications for understanding complex three-dimensional states of deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The CLAS12 High Threshold Cherenkov Counter

The High Threshold Cherenkov Counter (HTCC) is one of the detector systems of the CLAS12 spectrometer, and is used to generate a fast trigger signal in electron scattering experiments in the polar angle range from 5°to 35°. The HTCC is installed in front of the drift chambers and introduces a minimal amount of additional material within the acceptance. The HTCC is one unit whose core component is a multifocal mirror that consists of 60 lightweight ellipsoidal mirrors. It is important that the HTCC provides efficient coverage of the CLAS12 forward acceptance with no gaps. In order to achieve this, each sector of the CLAS12 Forward Detector is covered by 2 identical half-sector mirrors that focus Cherenkov light on 8 phototubes. The HTCC has a total of 48 channels with Electron Tubes 9823QKB photomultipliers that have a 5-in quartz face plate to detect Cherenkov light. The system provides rejection of charged -mesons with momenta below 4.8 GeV for the reliable identification of scattered electrons. In this paper the details of the design, construction, calibration, and performance results of the HTCC are presented

Sharabian, Y. G.↗
DOE OSTI2021

Structure and Shape of Surface-Mediated Assembly of Surfactants

Achieving controls on the self-assembly of cationic and block copolymer micelles has significant implications for advancing novel material systems for energy, environmental, and biological applications and for tuning fluid flow behavior in subsurface geologic environments. While it is known that the aggregation of micelles is influenced by the composition of the surrounding fluid, the influence of a solid interface has been less explored. In this study, we probe the organization of cetyltrimethylammonium bromide (CTAB) micelles in the absence and presence of the Pluronic P123 block copolymer and quartz substrate using transmission and grazing-incidence small-angle X-ray scattering measurements and classical molecular dynamics (MD) simulations. In the absence of the quartz interface, CTAB with and without P123 molecules assemble as ellipsoid core–shell micelles. Densification of the core on adding P123 is noted from the decrease in the core radius from 15.9 to 14.6 Å. Here, the presence of a quartz substrate causes the micelles to elongate, which is noted by the emergence of a power-law slope in the low q region (<0.02 Å –1 ). Moreover, in the presence of both P123 and the quartz substrate, the micelle shape changes from ellipsoid core–shell to cylindrical core–shell, and a significantly higher number of aggregates are formed. The higher number of aggregates, and faster aggregation kinetics are linked to the organization of the solvent structures as noted from the MD simulations. These findings demonstrate the effects of adding a block copolymer and including a quartz substrate on the self-assembly of cationic (CTAB) micelles, which can be used to inform the design of functional material systems that harness surfactant chemistries.

42 ENGINEERING↗
DOE OSTI2025

Atomic-scale imaging of frequency-dependent phonon anisotropy

Directly visualizing vibrational anisotropy in individual phonon modes is essential for understanding a wide range of intriguing optical, thermal and elastic phenomena in materials. Although conventional optical and diffraction techniques have been used to estimate vibrational anisotropies, they fall short in achieving the spatial and energy resolution necessary to provide detailed information. Here, we introduce a new form of momentum-selective electron energy-loss spectroscopy, which enables the element-resolved imaging of frequency- and symmetry-dependent vibrational anisotropies with atomic resolution. Vibrational anisotropies manifest in different norms of orthogonal atomic displacements, known as thermal ellipsoids. Using the centrosymmetric strontium titanate as a model system, we observed two distinct types of oxygen vibrations with contrasting anisotropies: oblate thermal ellipsoids below 60 meV and prolate ones above 60 meV. In non-centrosymmetric barium titanate, our approach can detect subtle distortions of the oxygen octahedra by observing the unexpected modulation of q-selective signals between apical and equatorial oxygen sites near 55 meV, which originates from reduced crystal symmetry and may also be linked to ferroelectric polarization. These observations are quantitatively supported by theoretical modelling, which demonstrates the reliability of our approach. The measured frequency-dependent vibrational anisotropies shed new light on the dielectric and thermal behaviours governed by acoustic and optical phonons. Furthermore, the ability to visualize phonon eigenvectors at specific crystallographic sites with unprecedented spatial and energy resolution opens new avenues for exploring dielectric, optical, thermal and superconducting properties.

Ferroelectrics and multiferroics↗
DOE OSTI2022

Substructure in the stellar halo near the Sun: I. Data-driven clustering in integrals-of-motion space

Context. Merger debris is expected to populate the stellar haloes of galaxies. In the case of the Milky Way, this debris should be apparent as clumps in a space defined by the orbital integrals of motion of the stars. Aims. Our aim is to develop a data-driven and statistics-based method for finding these clumps in integrals-of-motion space for nearby halo stars and to evaluate their significance robustly. Methods. We used data from Gaia EDR3, extended with radial velocities from ground-based spectroscopic surveys, to construct a sample of halo stars within 2.5 kpc from the Sun. We applied a hierarchical clustering method that makes exhaustive use of the single linkage algorithm in three-dimensional space defined by the commonly used integrals of motion energy E, together with two components of the angular momentum, L z and L ⊥ . To evaluate the statistical significance of the clusters, we compared the density within an ellipsoidal region centred on the cluster to that of random sets with similar global dynamical properties. By selecting the signal at the location of their maximum statistical significance in the hierarchical tree, we extracted a set of significant unique clusters. By describing these clusters with ellipsoids, we estimated the proximity of a star to the cluster centre using the Mahalanobis distance. Additionally, we applied the HDBSCAN clustering algorithm in velocity space to each cluster to extract subgroups representing debris with different orbital phases. Results. Our procedure identifies 67 highly significant clusters (> 3σ), containing 12% of the sources in our halo set, and 232 subgroups or individual streams in velocity space. In total, 13.8% of the stars in our data set can be confidently associated with a significant cluster based on their Mahalanobis distance. Inspection of the hierarchical tree describing our data set reveals a complex web of relations between the significant clusters, suggesting that they can be tentatively grouped into at least six main large structures, many of which can be associated with previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in a companion paper, in which we also characterise the substructures in terms of their stellar populations. Conclusions. Our method allows us to systematically detect kinematic substructures in the Galactic stellar halo with a data-driven and interpretable algorithm. The list of the clusters and the associated star catalogue are provided in two tables available at the CDS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Surrogate modelling the Baryonic Universe – I. The colour of star formation

The spectral energy distribution of a galaxy emerges from the complex interplay of many physical ingredients, including its star formation history (SFH), metallicity evolution, and dust properties. Using GALAXPY , a new galaxy spectral prediction tool, and SFHs predicted by the empirical model UNIVERSEMACHINE and the cosmological hydrodynamical simulation IllustrisTNG, we isolate the influence of SFH on optical and near-infrared colours from 320 to 1080 Å at z = 0. By carrying out a principal component analysis, we demonstrate that physically motivated SFH variations modify galaxy colours along a single direction in colour space: the SFH-direction. We find that the projection of a galaxy’s present-day colours on to the SFH-direction is almost completely regulated by the fraction of stellar mass that the galaxy formed over the last billion years. Together with cosmic downsizing, this results in galaxies becoming redder as their host halo mass increases. We additionally study the change in galaxy colours due to variations in metallicity, dust attenuation, and nebular emission lines, finding that these properties vary broad-band colours along distinct directions in colour space relative to the SFH-direction. Finally, we show that the colours of low-redshift Sloan Digital Sky Survey galaxies span an ellipsoid with significant extent along two independent dimensions, and that the SFH-direction is well-aligned with the major axis of this ellipsoid. Our analysis supports the conclusion that variations in SFH are the dominant influence on present-day galaxy colours, and that the nature of this influence is strikingly simple.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Angular Momentum in Rotating Superfluid Droplets

The angular momentum of rotating superfluid droplets originates from quantized vortices and capillary waves, the interplay between which remains to be uncovered. Here, the rotation of isolated submicrometer superfluid 4 He droplets is studied by ultrafast x-ray diffraction using a free electron laser. The diffraction patterns provide simultaneous access to the morphology of the droplets and the vortex arrays they host. In capsule-shaped droplets, vortices form a distorted triangular lattice, whereas they arrange along elliptical contours in ellipsoidal droplets. The combined action of vortices and capillary waves results in droplet shapes close to those of classical droplets rotating with the same angular velocity. The findings are corroborated by density functional theory calculations describing the velocity fields and shape deformations of a rotating superfluid cylinder.

O'Connell, Sean M.O.↗
DOE OSTI2026

Framework for X-ray mirror surface shape fitting

For accurate characterization of grazing-incidence X-ray mirrors, we present a comprehensive framework to fit measured surface shapes (either slope or height) of X-ray mirrors used in synchrotron radiation and free-electron laser facilities. We summarize the closed-form expressions of some typical surface shapes of X-ray mirrors including elliptic cylinders, hyperbolic cylinders, ellipsoids, hyperboloids, and diaboloids. This framework is composed of four layers: definition of standard shapes with closed-form expressions, generation of theoretical surface with pose parameters (six degrees of freedom defining an object's position and orientation relative to a coordinate system), parameter optimization with the ability to select which parameters are fit and which are held constant, and the development of user-friendly fitting function wrappers for particular fitting tasks. A few practical fitting examples are demonstrated to verify the effectiveness of the proposed fitting framework. We discuss the physical meanings of the fitting parameters, and provide several examples using the elliptic cylinder and ellipsoid shapes to highlight some features of the framework. Moreover, we provide the presented framework as open-source codes (MATLAB and Python codes available at https://github.com/nsls2omf/xmf) to the community to encourage academic collaboration and further improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.

adaptive robust optimization↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2022

RUScal : Software for the analysis of resonant ultrasound spectroscopy measurements

Resonant ultrasound spectroscopy is used to nondestructively measure the elastic resonances of small solids to elucidate the material's elastic properties or other qualities like size, shape, or composition. In this work, we introduce the software RUScal for the purpose of determining elastic properties by analyzing the eigenfrequencies of solid specimens with common shapes, such as rectangular parallelepipeds, cylinders (solid and hollow tube), ellipsoids, and octahedrons, as well as irregularly shaped ellipsoids that can be described analytically. All symmetry classes are supported, from isotropic to triclinic, along with the option to add or remove up to three orthogonal mirror planes as well as the ability to reorient the crystal axes with respect the sample edges via Euler angles. Additional features include tools to help find initial sets of elastic constants, including grid exploration and Monte Carlo methods, a tool to analyze frequencies as a function of sample length or crystal orientation, an error analysis tool to assess fit quality, and formatting of the input and output files for batch fitting, e.g., as a function of temperature. This software was validated with published resonant ultrasound spectroscopy data for various materials, shapes, and symmetries with noted improvements in calculation time compared to finite element methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

High-speed particle analysis using forward and backward two-dimensional angular optical scattering

Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized high-speed flying particles. The elastic-scattering intensity pattern from a single particle as a function of spherical coordinate angles θ and ϕ provides detailed information on the particle’s morphology. By use of an ellipsoidal reflector and a high-speed CCD camera, scattering from a CW-laser was detected and resolved over a large angular range (θ from 12° to 168° and ϕ from 0° to 360°) from spherically shaped particles (e.g., amorphous silica and borosilicate glass) flowing through the reflector’s focal plane at speeds of 20 to 30 m/s. The scattering pattern from individual particles flying through the focal plane of the ellipsoid mirror provides insights about a particle’s size, clustering, roughness, and velocity.

Optics and optical instruments↗
DOE OSTI2024

Role of physics in physics-informed machine learning

Physical systems are characterized by inherent symmetries, one of which is encapsulated in the units of their parameters and system states. These symmetries enable a lossless order-reduction, e.g., via dimensional analysis based on the Buckingham theorem. Despite the latter's benefits, machine learning (ML) strategies for the discovery of constitutive laws seldom subject experimental and/or numerical data to dimensional analysis. We demonstrate the potential of dimensional analysis to significantly enhance the interpretability and generalizability of ML-discovered secondary laws. Our numerical experiments with creeping fluid flow past solid ellipsoids show how dimensional analysis enable both deep neural networks and sparse regression reproduce old results, e.g., Stokes law for a sphere, and generate new ones, e.g., an expression for an ellipsoid misaligned with the flow direction. Furthermore, our results suggest the need to incorporate other physics-based symmetries and invariances into ML-based techniques for equation discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DEM simulation of the compression of crushable sand: does the initial particle shape matter?

Advances in DEM modeling, combined with high-resolution X-ray tomography, opened the way for computer models based on virtual replicas of the particles which preserve nearly all facets of their geometry. This leads to simulation advantages, but also high computational costs. Here we tackle a question stemming from this trend: how accurate should particle models be to ensure accuracy? We address this question for the case of the compression of crushable sand. LS-DEM was used to generate three models of Ottawa sand (exact replicas, ellipsoids, and spheres) from digital images of its grains. Compression-induced crushing was simulated for all sets by tracking evolving size and shape distribution. The results confirm that exact replicas provide the closest match of the measurements. However, intermediate degrees of rendering (e.g. ellipsoids preserving volume and aspect ratio of the real grains) led to satisfactory results only marginally different from those of exact replicas. In conclusion, these findings provide an example of the protocols that may be followed to identify the optimal degree of particle approximation which should be regarded as mandatory to achieve a conscious, sustainable use of computational resources.

58 GEOSCIENCES↗