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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modelling CN Zeeman effect observations of the envelopes of a low-mass protostellar disc and a massive protostar

We use the POLARIS radiative transfer code to produce simulated circular polarization Zeeman emission maps of the cyanide (CN) J = 1–0 molecular line transition for two types of protostellar envelope magnetohydrodynamic simulations. Our first model is a low-mass disc envelope system (box length L = 200 au), and our second model is the envelope of a massive protostar (L = 104 au) with a protostellar wind and a CN-enhanced outflow shell. We compute the velocity-integrated Stokes I and V, as well as the implied V/I polarization percentage, for each detector pixel location in our simulated emission maps. Our results show that both types of protostellar environments are in principle accessible with current circular polarization instruments, with each containing swaths of envelope area that yield percentage polarizations that exceed the 1.8 percent nominal sensitivity limit for circular polarization experiments with the Atacama Large Millimeter/submillimeter Array. In both systems, high-polarization (≳1.8 percent) pixels tend to lie at an intermediate distance away from the central star and where the line-centre opacity of the CN emission is moderately optically thin (τLC ~ 0.1–1). Furthermore, our computed V/I values scale roughly with the density-weighted mean line-of-sight magnetic field strength, indicating that Zeeman observations can effectively diagnose the strength of envelope-scale magnetic fields. We also find that pixels with large V/I are preferentially co-located where the absolute value of the velocity-integrated V is also large, suggesting that locations with favourable percentage polarization are also favourable in terms of raw signal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Dissociative Recombination of Cold HeH + Ions

The HeH + cation is the simplest molecular prototype of the indirect dissociative recombination (DR) process that proceeds through electron capture into Rydberg states of the corresponding neutral molecule. This Letter develops the first application of our recently developed energy-dependent frame transformation theory to the indirect DR processes. The theoretical model is based on the multichannel quantum-defect theory with the vibrational basis states computed using exterior complex scaling of the nuclear Hamiltonian. The ab initio electronic R-matrix theory is adopted to compute quantum defects as functions of the collision energy and of the internuclear distance. The resulting DR rates are convolved over the beam energy distributions relevant to a recent experiment at the Cryogenic Storage Ring, giving good agreement between the experiment and the theory.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

via-wind (A Visual Impact Assessment Tool for Wind Turbines) [SWR-24-87]

Via-wind is an open-source tool for conducting visual impact assessments for wind turbines. It combines geographic information system (GIS) and 3D simulation methods to account for the key factors driving the visual impact of installed wind turbines, including distance, viewing angle, turbine orientation, visual exposure, and the cumulative effects of multiple turbines. This software is optimized for use in high-performance computing environments to enable large scale (e.g., country-wide) analysis, but can also be run on a single server or personal computer. For more information, please see the related journal article: https://www.sciencedirect.com/science/article/pii/S0306261924021846

Lopez, Anthony↗
DOE OSTI2020

Atomistic Studies of Individual Impurities and Impurity Complexes in III-V Semiconductors. Final report

The central hypothesis of our investigation is that reduced symmetries significantly influence the properties of individual impurities and impurity complexes in semiconductors. To disentangle the properties of the local environments of these impurities and impurity complexes from the behavior of an ensemble of impurities, at U. Iowa we performed theoretical calculations of specific local environments and developed the theoretical technology to calculate spin and charge transport through those local environments, in which the contact is a localized probe (scanning tunneling microscopy tip). These calculations were designed to be brought into comparison with experimental measurements made by Prof. Gupta’s group at Ohio State University. The principal environment that we focused on is the (110) surface of GaAs and other III-V semiconductors, because pristine surfaces can be revealed through a natural cleavage plane. We studied how the p-d hybridization of transition-metal dopants is influenced by the energetic location and the local environment’s symmetry-breaking of the d levels, and how the 4f-shell states of rare earth impurities couple to the surroundings. We calculated long-distance properties using tight-binding (linear combination of atomic orbitals, LCAO) Green’s function methods. The spin character of the dopants is often coherent on the time-scale of electronic transport through the region, and so correlations of the spin character of a dopant with that of a spinpolarized STM tip, or another dopant in the vicinity, can influence the measured conductivity from STM. We constructed a robust theory for these effects that can be used to describe quantitatively the measurements made by spin-polarized STM.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

The Great Inequality and the Dynamical Disintegration of the Outer Solar System

Using an ensemble of N-body simulations, this paper considers the fate of the outer gas giants (Jupiter, Saturn, Uranus, and Neptune) after the Sun leaves the main sequence and completes its stellar evolution. Due to solar mass loss—which is expected to remove roughly half of the star’s mass—the orbits of the giant planets expand. This adiabatic process maintains the orbital period ratios, but the mutual interactions between planets and the width of mean-motion resonances (MMR) increase, leading to the capture of Jupiter and Saturn into a stable 5:2 resonant configuration. The expanded orbits, coupled with the large-amplitude librations of the critical MMR angle, make the system more susceptible to perturbations from stellar flyby interactions. Accordingly, within about 30 Gyr, stellar encounters perturb the planets onto the chaotic subdomain of the 5:2 resonance, triggering a large-scale instability, which culminates in the ejections of all but one planet over the subsequent ∼10 Gyr. After an additional ∼50 Gyr, a close stellar encounter (with a perihelion distance less than ∼200 au) liberates the final planet. Through this sequence of events, the characteristic timescale over which the solar system will be completely dissolved is roughly 100 Gyr. Our analysis thus indicates that the expected dynamical lifetime of the solar system is much longer than the current age of the universe, but is significantly shorter than previous estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗
DOE OSTI2021

Scalable Programming Workflows for Validation of Quantum Computers

Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.

Nguyen, Thien↗
DOE OSTI2021

Nucleation and growth of blue phase liquid crystals on chemically-patterned surfaces: a surface anchoring assisted blue phase correlation length

In condensed matter, the correlation length is an essential parameter which describes the distance over which a material maintains its structural properties. In liquid crystals, for the orientational order parameter this characteristic length is of the order of nanometers, while in solid crystals it can extend to macroscopic length scales. Here, we report the measurement of the correlation length, or persistence length, of the crystallographic orientation of blue phases (BPs)—chiral liquid crystals with long-range 3D-crystalline structures and submicron-sized lattice-parameters. These materials exhibit phase transformations that have been identified as the liquid analog of crystal-crystal martensitic transformations. In this work, we use liquid epitaxial growth to achieve spontaneous BP-crystal nucleation and subsequent growth. Specifically, we design patterned substrates made of a binary array of regions with different liquid crystal anchoring, which facilitate a uniform nucleation and growth of BP-crystals with (100)-lattice orientation and a simple cubic symmetry. Furthermore, our results indicate that this simple cubic BP, the so-called blue phase II (BPII), forms first on the patterned surface, thereby propagating the growth of domains in directions parallel or perpendicular to the patterned regions. These results are used to understand the emergence of a surface anchoring assisted BPII-correlation length, taken as the distance over which the BP preserves its lattice orientation, as a function of time and pattern characteristics. We found that BPII single crystals can be achieved on patterned regions whose lateral dimensions are equal to or larger than 10 μm, consistent with our measurements of the BPII-correlation length. This newly acquired understanding of the role of surfaces on the formation of BPs is then used to design processes that permit formation of macroscopically large mono-domain, single-crystal BPs by relying on significantly reduced patterned areas (only part of the area is patterned), a feature that could benefit the applications of this intriguing class of materials.

correlation length↗
DOE OSTI2020

Stochastic renormalization group and gradient flow

A non-perturbative and continuous definition of RG transformations as stochastic processes is proposed, inspired by the observation that the functional RG equations for effective Boltzmann factors may be interpreted as Fokker-Planck equations. The result implies a new approach to Monte Carlo RG that is amenable to lattice simulation. Long-distance correlations of the effective theory are shown to approach gradient-flowed correlations, which are simpler to measure. The Markov property of the stochastic RG transformation implies an RG scaling formula which allows for the measurement of anomalous dimensions when transcribed into gradient flow expectation values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Scalable balanced training of conditional generative adversarial neural networks on image data

Here, we propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score, Fréchet inception distance, and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1000 processes and 2000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Real-time streamflow forecasting: AI vs. Hydrologic insights

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km 2 . The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training

Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train in the context of spiking networks with various ratios of excitatory to inhibitory neurons. The results indicate networks with biologically-realistic excitatory:inhibitory ratios can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.

bio-inspired computing↗
DOE OSTI2022

Fossil group origins

Aims: We analyse the large-scale structure out to 100 Mpc around a sample of 16 confirmed fossil systems using spectroscopic information from the Sloan Digital Sky Survey Data Release 16. Methods: We computed the distance between our fossil groups (FGs) and the centres of filaments and nodes from the literature. We also studied the density of bright galaxies, since this parameter is thought to be a good mass tracers, as well as the projected over-densities of galaxies. Finally, we applied a friends-of-friends (FoF) algorithm to detect virialised structures around our FGs and obtain an estimate of the mass available in their surroundings. Results: We find that FGs are mainly located close to filaments, with a mean distance of 3.7 ± 1.1 R 200 and a minimum distance of 0.05 R 200 . On the other hand, none of our FGs were found close to intersections, with a mean and minimum distance of 19.3 ± 3.6 and 6.1 R 200 , respectively. There is a correlation that indicates FGs at higher redshifts are found in denser regions, when we use bright galaxies as tracers of the mass. At the same time, FGs with the largest magnitude gaps (Δm 12 > 2.5) are found in less dense environments and tend to host (on average) smaller central galaxies. Conclusions: Our results suggest that FGs formed in a peculiar position within the cosmic web, close to filaments and far from nodes, whereby their interaction with the cosmic web itself may be limited. We deduce that FGs with brightest central galaxies (BCGs) that are relatively faint, high values of Δm 12 , and low redshifts could, in fact, be systems that are at the very last stage of their evolution. Moreover, we confirm theoretical predictions that systems with the largest magnitude gap are not massive.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

SPiRaL: a multiresolution global tomography model of seismic wave speeds and radial anisotropy variations in the crust and mantle

SUMMARY SPiRaL is a joint global-scale model of wave speeds (P and S) and anisotropy (vertical transverse isotropy, VTI) variations in the crust and mantle. The model is comprised of >2.1 million nodes with five parameters at each node that capture velocity variations for P- and S-waves travelling at arbitrary directions in transversely isotropic media with a vertical symmetry axis (VTI). The crust (including ice, water, sediments and crystalline layers) is directly incorporated into the model. The default node spacing is approximately 2° in the lower mantle and 1° in the crust and upper mantle. The grid is refined with ∼0.25° minimum node spacing in highly sampled regions of the crust and upper mantle throughout North America and Eurasia. The data considered in the construction of SPiRaL includes millions of body wave traveltimes (crustal, regional and teleseismic phases with multiples) and surface wave (Rayleigh and Love) dispersion. A multiresolution inversion approach is employed to capture long-wavelength heterogeneities commonly depicted in global-scale tomography images as well as more localized details that are typically resolved in more focused regional-scale studies. Our previous work has demonstrated that such global-scale models with regional-scale detail can accurately predict both teleseismic and regional body wave traveltimes, which is necessary for more accurate location of small seismic events that may have limited signal at teleseismic distances. SPiRaL was constructed to predict traveltimes for event location and long-period waveform dispersion for seismic source inversion applications in regions without sufficiently tuned models. SPiRaL may also serve as a starting model for full-waveform inversion (FWI) with the goal of fitting waves with periods 10–50 s over multiple broad regions (thousands of kilometres) and potentially the globe. To gain insight to this possibility, we simulated waveforms for a small set of events using SPiRaL and independent waveform-based models for comparison. For the events tested, the performance of the traveltime-based SPiRaL model is shown to be generally on par with regional 3-D waveform-based models in three regions (western United States, Middle East, Korean Peninsula) suggesting SPiRaL may serve as a starting model for FWI over broad regions.

58 GEOSCIENCES↗
DOE OSTI2024

Optical and electronic functionality arising from controlled defect formation in nanoscale complex oxide lateral epitaxy

Epitaxial crystallization of complex oxides provides the means to create materials with precisely selected composition, strain, and orientation, thereby controlling their functionalities. Extending this control to nanoscale three-dimensional geometries can be accomplished via a three-dimensional analog of oxide solid-phase epitaxy, lateral epitaxial crystallization. The orientation of crystals within laterally crystallized SrTiO 3 systematically changes from the orientation of the SrTiO 3 substrate. This evolution occurs as a function of lateral crystallization distance, with a rate of approximately 50° μm -1 . The mechanism of the rotation is consistent with a steady-state stress of tens of megapascal over a 100–nanometer scale region near the moving amorphous/crystalline interface arising from the amorphous-crystalline density difference. Second harmonic generation and piezoelectric force microscopy reveal that the laterally crystallized SrTiO 3 is noncentrosymmetric and develops a switchable piezoelectric response at room temperature, illustrating the potential to use lateral crystallization to control the functionality of complex oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Project Title: Development of a Pipe Crawler Inspection Tool for Fossil Energy Power Plants (Final Report)

This research effort focuses on the development of a pipe crawler inspection tool for fossil energy power plants. The pipe crawler is tethered and can navigate through 2 inch pipes that are often found in various systems within a power plant. The crawler can navigate through straight pipes and 180 degree elbows and includes instrumentation that can assess the integrity of the pipes. The sensors include a UT sensor for measuring the thickness of the pipe, a range finder and a camera. An additional module is used to prep the surface of the pipe prior to taking thickness measurements. Both bench scale and engineering scale testing was conducted to demonstrate the concept. Multiple crawlers were created and tethered together to distribute the load of the tether so that the crawler can navigate longer pipe distances.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Male mating season range expansion results from an increase in scale of daily movements for a polygynous–promiscuous bird

Abstract Males of species with promiscuous mating systems are commonly observed to use larger ranges during the mating season relative to non‐mating seasons, which is often attributed to a change in movements related to reproductive activities. However, few studies link seasonal range sizes to variation in daily space use patterns to provide insight into the behavioral mechanisms underlying mating season range expansion. We studied 20 GPS‐tagged male wild turkeys ( Meleagris gallopavo ), a large upland gamebird, during the mating and summer non‐mating seasons to test the hypothesis that larger mating season ranges resulted from male wild turkeys expanding the scale of daily movement activities to locate and court females. We delineated mating and non‐mating seasons based on intensity of gobbling, a vocalization tied to courtship behavior, recorded by autonomous recording units distributed across the study area. Mating season ranges were significantly larger than non‐mating season ranges. Daily ranges were larger in the mating season, as were distances between roost sites used on consecutive nights. Variance in daily range size was greater in the mating season, but low temporal autocorrelation suggested considerable daily variability in both seasons. We found no evidence that male wild turkeys changed how they distributed daily movements within seasonal ranges, or differences in habitat use, suggesting larger mating season ranges result from male wild turkeys increasing the scale of their daily movements, rather than a systematic shift to a nomadic movement strategy. Likely, the distribution of females is more dynamic and ephemeral compared to other resources, prompting males to traverse larger daily ranges during the mating season to locate and court females. Our work illustrates the utility of using daily movement to understand the behavioral process underlying larger space use patterns.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗