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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Neutron irradiation-induced microstructure damage in ultra-high temperature ceramic TiC

Titanium carbide (TiC) is an ultra-high temperature ceramic with potential as a structural material candidate for advanced reactor concepts. However, the irradiation tolerance of TiC is not well understood. Here, we reveal the key irradiation damage microstructure degradation processes in TiC using mixed spectrum neutron irradiations at dose of ~2 displacements per atom (dpa) at temperatures of ~220, 620, and 1115 °C, combined with state-of-art microstructure characterization using transmission electron microscopy (TEM) and high-resolution TEM (HRTEM). The helium injection (~65 atomic parts per million) produced by neutron transmutation also occurred in the sample. TiC was observed to form irradiation-induced interstitial-type dislocation loops and He-stabilized cavities. At 220 and 620 °C, the analysis of the electron diffraction patterns, rel-rod imaging and HRTEM revealed that the dislocation loops were faulted Frank loops with Burgers vector b Frank = 1/3<111> lying on {111} planes. A detailed Burgers vector identification performed by the g.b technique revealed that the dislocation loops forming at 1115 °C were unfaulted, edge-type, with Burgers vectors b = a<100> and a/2<110> with corresponding {100} and {110} habit planes. Using continuum mechanics, we estimated the critical radius at which a faulted dislocation loop transitions to a perfect loop to be 9 nm. Further, no amorphization occurred in TiC under-investigated irradiation conditions while macroscopic swelling under point defect swelling regime was observed. He-stabilized cavities were detected at 1115 °C, but not at lower temperature irradiation. These observations indicate the onset of long-range vacancy migration in TiC occurs between 620 and 1115 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A microbenchmark characterization of the Emu chick

The Emu Chick is a prototype system designed around the concept of migratory memory-side processing. Rather than transferring large amounts of data across power-hungry, high-latency interconnects, the Emu Chick moves lightweight thread contexts to near-memory cores before the beginning of each memory read. The current prototype hardware uses FPGAs to implement cache-less “Gossamer” cores for computational work and rely on a typical stationary core (PowerPC) to run basic operating system functions and migrate threads between nodes. In this multi-node characterization of the Emu Chick, we extend an earlier single-node investigation of the the memory bandwidth characteristics of the system through benchmarks like STREAM, pointer chasing, and sparse matrix-vector multiplication. Here, we compare the Emu Chick hardware to architectural simulation and an Intel Xeon-based platform. Our results demonstrate that for many basic operations the Emu Chick can use available memory bandwidth more efficiently than a more traditional, cache-based architecture although bandwidth usage suffers for computationally intensive workloads like SpMV. Moreover, the Emu Chick provides stable, predictable performance with up to 65% of the peak bandwidth utilization on a random-access pointer chasing benchmark with weak locality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy

Thin film deposition is a fundamental technology for the discovery, optimization, and manufacturing of functional materials. Deposition by molecular beam epitaxy (MBE) typically employs reflection high-energy electron diffraction (RHEED) as a real-time in situ probe of the growing film. However, the state-of-the-art for RHEED analysis during deposition requires human observation. Here, we present an approach using machine learning (ML) methods to monitor, analyze, and interpret RHEED images on-the-fly during thin film deposition. In the analysis workflow, RHEED pattern images are collected at one frame per second and featurized using a pretrained deep convolutional neural network. The feature vectors are then statistically analyzed to identify changepoints; these changepoints can be related to changes in the deposition mode from initial film nucleation to a transition regime, smooth film deposition, and in some cases, an additional transition to a rough, islanded deposition regime. The feature vectors are additionally analyzed via graph analysis and community classification. The graph is quantified as a stabilization plot, and we show that inflection points in the stabilization plot correspond to changes in the growth regime. The full RHEED analysis workflow is termed RHAAPsody and includes data transfer and output to a visual dashboard. We demonstrate the functionality of RHAAPsody by analyzing the precaptured RHEED images from epitaxial depositions of anatase TiO2 on SrTiO3(001) and show that the analysis workflow can be executed in less than 1 s. Our approach shows promise as one component of ML-enabled real-time feedback control of the MBE deposition process.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-informed neural networks for identification of material properties using standing waves

A metallic structure in its initial stage of failure involves plastic deformation or environmental degradation that changes the elastic modulus and density. This work presents the detection of change in wave velocity (a function of elastic modulus and density) as a system identification problem. A physics-informed neural network (PINN) is proposed to solve the system identification problem. The PINN takes the spatial coordinates of scanning locations and time as inputs and provides the displacement and wave velocity as outputs. The governing partial differential equation of standing waves in a rod is incorporated into the neural network as physics in the form of a loss function. The wave velocity vector is randomly initiated. During the training of the network, physics is used to determine and update the wave velocity target vector from the network’s displacement predictions. The measured data, comprising sparse displacement response on the rod structure, are used to train the PINN. The wave velocity at the sparse locations on the rod is learned from the predicted displacements during the training. Using the predictions of the trained network, the response of free vibration or material property variation can be reconstructed at unscanned locations on the structure to obtain high-resolution maps for full-field imaging to detect and localize the changes caused by plastic deformation. The PINN’s sparse scanning and simultaneous prediction capability during training can lead to high scanning and data-processing speeds. This capability yields a nondestructive evaluation system that can predict the presence of degraded material locations as the structural vibrations are scanned and processed in real time.

Rathod, Vivek↗
DOE OSTI2019

Alignment between Filaments and Galaxy Spins from the MaNGA Integral-field Survey

Halos and galaxies acquire their angular momentum during the collapse of the surrounding large-scale structure. This process imprints alignments between galaxy spins and nearby filaments and sheets. Low-mass halos grow by accretion onto filaments, aligning their spins with the filaments, whereas high-mass halos grow by mergers along filaments, generating spins perpendicular to the filament. Here, we search for this alignment signal using filaments identified with the "Cosmic Web Reconstruction" algorithm applied to the Sloan Digital Sky Survey Main Galaxy Sample and galaxy spins from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) integral-field unit survey. MaNGA produces a map of the galaxy's rotational velocity, allowing direct measurement of the galaxy's spin direction, or unit angular momentum vector projected onto the sky. We find no evidence for alignment between galaxy spins and filament directions. We do find hints of a mass-dependent alignment signal, which is in 2σ–3σ tension with the mass-dependent alignment signal in the MassiveBlack-II and Illustris hydrodynamical simulations. However, the tension vanishes when galaxy spin is measured using the Hα emission line velocity rather than stellar velocity. Lastly, in simulations we find that the mass-dependent transition from aligned to anti-aligned dark matter halo spins is not necessarily present in stellar spins: we find a stellar spin transition in Illustris but not in MassiveBlack-II, highlighting the sensitivity of spin-filament alignments to feedback prescriptions and subgrid physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Accelerating high-order continuum kinetic plasma simulations using multiple GPUs

Kinetic plasma simulations solve the Vlasov-Poisson or Vlasov-Maxwell equations to evolve scalar-variable distribution functions in position-velocity phase space and vector-variable electromagnetic fields in configuration space. The immense computational cost of evolving high-dimensional variables, and their large number of degrees of freedom, often limits the utility of continuum kinetic simulations and presents a challenge when it comes to accurately simulating real-world physical phenomena. To address this challenge, we present techniques that accelerate and minimize the computational work required for a scalable Vlasov-Poisson solver. We show theoretical hardware compute and communication bounds for solving a fourth-order finite-volume Vlasov-Poisson system. These bounds are then used to inform and evaluate the design of performance portable algorithms for a multiple graphics processing unit (GPU) accelerated version of the Vlasov-Poisson solver VCK-CPU [1]. We demonstrate that the multi-GPU Vlasov solver implementation, VCK-GPU, simultaneously minimizes required inter-process data transfer while also being bounded by the machine network performance limits. This results in an overall strong scaling speedup per timestep of up to 40x in three-dimensional phase space (one position, two velocity coordinates) and 54x in four dimensional phase space (two position, two velocity coordinates) and a 341x increase in simulation throughput of the GPU accelerated code over the existing CPU code. The GPU code is also able to weak scale up to 256 compute nodes and 1024 GPUs. In conclusion, we demonstrate that the improved compute performance enables exploring configurations which were previously computationally infeasible, including resolving fine-scale distribution function filamentation and multi-species dynamics with realistic electron-proton mass ratios.

Continuum kinetics↗
DOE OSTI2020

First Principles Nonadiabatic Excited-State Molecular Dynamics in NWChem

Computational simulation of non-adiabatic molecular dynamics is an indispensable tool for understanding complex photoinduced processes such as internal conversion, energy transfer, charge separation, and spatial localization of excitons, to name a few. Here, we report an implementation of the fewest-switches surface hopping algorithm in the NWChem computational chemistry program. Here the surface hopping method is combined with linear-response time-dependent density functional theory calculations of adiabatic excited state potential energy surfaces. To treat quantum transitions between arbitrary electronic Born{Oppenheimer states, we have implemented both numerical and analytical differentiation schemes for derivative non-adiabatic couplings. A numerical approach for the time-derivative non-adiabatic couplings together with an analytical method for calculating non-adiabatic coupling vectors is an efficient combination for surface hopping approaches. Additionally, electronic decoherence schemes and a state reassigned unavoided crossings algorithm are also implemented to improve the accuracy of the simulated dynamics and to handle trivial unavoided crossings. We apply our code to study the ultrafast decay of photoexcited benzene, including a detailed analysis of the potential energy surface, population decay time scales, and vibrational coordinates coupled to the excitation dynamics. The development presented in this work is a baseline for future implementations of more sophisticated frameworks for simulating of non-adiabatic molecular dynamics in NWChem.

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

Optimizing the hit finding algorithm for liquid argon TPC neutrino detectors using parallel architectures

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming even more difficult as the detectors increase in size to reach their physics goals. Liquid argon time projection chamber (LArTPC) neutrino experiments are expected to grow in the next decade to have 100 times more wires than in currently operating experiments, and modernization of LArTPC reconstruction code, including parallelization both at data- and instruction-level, will help to mitigate this challenge. The LArTPC hit finding algorithm is used across multiple experiments through a common software framework. In this paper we discuss a parallel implementation of this algorithm. Using a standalone setup we find speedup factors of two times from vectorization and 30–100 times from multi-threading on Intel architectures. The new version has been incorporated back into the framework so that it can be used by experiments. On a serial execution, the integrated version is about 10 times faster than the previous one and, once parallelization is enabled, more speedups comparable to the standalone program are achieved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Switching dynamics of the spin density wave in superconducting CeCoIn 5

The ordering wave vector Q of a spin density wave (SDW), stabilized within the superconducting state of CeCoIn 5 in a high magnetic field, has been shown to be hypersensitive to the direction of the field. Q can be switched from a nodal direction of the d-wave superconducting order parameter to a perpendicular node by rotating the in-plane magnetic field through the antinodal direction within a fraction of a degree. In this paper, we address the dynamics of the switching of Q. We use a free-energy functional based on the magnetization density, which describes the condensation of magnetic fluctuations of nodal quasiparticles, and show that the switching process includes closing of the SDW gap at one Q and then reopening the SDW gap at another Q perpendicular to the first one. The magnetic field couples to Q through the spin-orbit interaction. Our calculations show that the width of the hysteretic region of switching depends linearly on the deviation of magnetic field from the critical field associated with the SDW transition, consistent with our thermal conductivity measurements. Finally, the agreement between theory and experiment supports our scenario of the hypersensitivity of the Q phase on the direction of magnetic field, as well as the magnon condensation as the origin of the SDW phase in CeCoIn 5 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Integration of LIBS with Machine Learning for Real-Time Monitoring of Feedstock in H 2 Gasification Applications

This project, funded by the U.S. Department of Energy (DOE) – Office of Fossil Energy under Award Number DE-FE0032177, aimed to assess the feasibility of an integrated Laser-Induced Breakdown Spectroscopy (LIBS) system with advanced machine learning (ML) models for real-time characterization and potential control of hydrogen gasifiers running on waste materials as feedstocks. This was a multidisciplinary effort that encompassed the acquisition and standardized analysis of individual and blended feedstocks—comprising biomass, coal waste, and plastic waste, followed by the development of a dynamic LIBS bench system for material sample analysis and development of predictive ML models. Comprehensive laboratory testing enabled the creation of a robust elemental dataset that served as the foundation for ML model training. Techniques such as Random Forest, Gradient Boosting, Support Vector Regression, and Neural Networks were employed to predict key feedstock properties, including higher heating value (HHV), moisture content, thermal conductivity, and ash composition with high accuracy. The results were validated against experimental data and demonstrated strong potential for real-time application in gasifier control systems. The project concluded with a study on the integration of the LIBS+ML approach for gasifier control and a techno-economic analysis of the implementation of the approach into hydrogen (H 2 ) gasification systems. Dissemination of results was carried out at a DOE meeting. This work establishes a scalable framework for automated, in-line feedstock quality assessment, offering significant implications for process optimization and emissions reduction in hydrogen production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2017

THE EFFECT OF ROTATION ON OSCILLATORY DOUBLE-DIFFUSIVE CONVECTION (SEMICONVECTION)

Oscillatory double-diffusive convection (ODDC, more traditionally called semiconvection) is a form of linear double-diffusive instability that occurs in fluids that are unstably stratified in temperature (Schwarzschild unstable), but stably stratified in chemical composition (Ledoux stable). This scenario is thought to be quite common in the interiors of stars and giant planets, and understanding the transport of heat and chemical species by ODDC is of great importance to stellar and planetary evolution models. Fluids unstable to ODDC have a tendency to form convective thermocompositional layers that significantly enhance the fluxes of temperature and chemical composition compared with microscopic diffusion. Although a number of recent studies have focused on studying properties of both layered and nonlayered ODDC, few have addressed how additional physical processes such as global rotation affect its dynamics. In this work, we study first how rotation affects the linear stability properties of rotating ODDC. Using direct numerical simulations, we then analyze the effect of rotation on properties of layered and nonlayered ODDC, and we study how the angle of the rotation axis with respect to the direction of gravity affects layering. We find that rotating systems can be broadly grouped into two categories based on the strength of rotation. The qualitative behavior in the more weakly rotating group is similar to nonrotating ODDC, but strongly rotating systems become dominated by vortices that are invariant in the direction of the rotation vector and strongly influence transport. We find that whenever layers form, rotation always acts to reduce thermal and compositional transport.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Serial crystallography with multi-stage merging of thousands of images

KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Classification of dynamical Lie algebras generated by spin interactions on undirected graphs

Dynamical Lie algebras (DLAs) are a versatile tool for various topics that span from the expressibility-trainability of variational quantum algorithms (VQAs), to simulation of many body Hamiltonians. Quantum gates and most of the Hamiltonians of interest consist of local interactions; therefore, the analysis of all possible DLAs generated by 1- and 2-local operators is crucial for quantum simulation and VQAs on current hardware. Previously in [R. Wiersema et al ., npj Quantum Inf. 10 , 110 (2024)], we analyzed the DLAs on linear, circular and all-to-all topologies, and obtained results about their dimensions and algebraic structure. Here, in this work, we extend our analysis into any possible hardware topology and provide a classification of all DLAs generated by Pauli strings on any undirected interaction graph. Our results indicate that the DLAs depend solely on whether the connectivity or interaction graph is bipartite or not. In addition, we find that the non-trivial polynomially scaling DLAs appear only on 1D line or circle topologies, and all other DLAs have dimensions scaling exponentially with the system size. Together with the current VQA literature, our results imply that either the majority of VQAs are non-trainable, or we are yet to understand the role of DLAs on the trainability of VQAs.

Algebraic structures↗
DOE OSTI2026

Searches for CE ν NS and physics beyond the standard model using Skipper-CCDs at CONNIE

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) aims to detect the coherent scattering ( CE ν NS ) of reactor antineutrinos off silicon nuclei using thick fully depleted high-resistivity silicon CCDs. Two Skipper-CCD sensors with subelectron readout noise capability were installed at the experiment next to the Angra-2 reactor in 2021, making CONNIE the first experiment to employ Skipper-CCDs for reactor neutrino detection. We report on the performance of the Skipper-CCDs, the new data processing, data quality, and event selection for CE ν NS interactions, which enable CONNIE to reach a record low detection threshold of 15 eV. The data were collected over 300 days in 2021–2022 and correspond to exposures of 14.9 g-days with the reactor-on and 3.5 g-days with the reactor-off. The difference between the reactor-on and off event rates shows no excess and yields upper limits for the neutrino interaction rates, comparable with previous CONNIE limits from standard CCDs and higher exposures. Searches for new neutrino interactions beyond the Standard Model improve the previous CONNIE limit on a simplified model with light vector mediators. A first dark matter (DM) search by diurnal modulation by CONNIE obtains the best limits on the DM-electron scattering cross section by a surface-level experiment. These promising results, obtained using a very small-mass sensor, illustrate the potential of Skipper-CCDs to probe rare neutrino interactions and motivate the plans to increase the detector mass in the near future.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Search for single production of vector-like quarks decaying into Wb in pp collisions at $\sqrt{s} = 8$ TeV with the ATLAS detector

A search for singly produced vector-like Q quarks, where Q can be either a T quark with charge +2/3 or a Y quark with charge –4/3, is performed in proton–proton collisions recorded with the ATLAS detector at the LHC. The dataset corresponds to an integrated luminosity of 20.3 fb –1 and was produced with a centre-of-mass energy of √s = 8 TeV. This analysis targets Q → Wb decays where the W boson decays leptonically. A veto on massive large-radius jets is used to reject the dominant tt¯ background. The reconstructed Q-candidate mass, ranging from 0.4 to 1.2 TeV, is used in the search to discriminate signal from background processes. No significant deviation from the Standard Model expectation is observed, and limits are set on the Q → Wb cross-section times branching ratio. The results are also interpreted as limits on the QWb coupling and the mixing with the Standard Model sector for a singlet T quark or a Y quark from a doublet. T quarks with masses below 0.95 TeV are excluded at 95 % confidence level, assuming a unit coupling and a BR(T → Wb) = 0.5, whereas the expected limit is 1.10 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Magnetic Field Reconstruction for a Realistic Multi-Point, Multi-Scale Spacecraft Observatory

Future in situ space plasma investigations will likely involve spatially distributed observatories comprised of multiple spacecraft, beyond the four and five spacecraft configurations currently in operation. Inferring the magnetic field structure across the observatory, and not simply at the observation points, is a necessary step towards characterizing fundamental plasma processes using these unique multi-point, multi-scale data sets. We propose improvements upon the classic first-order reconstruction method, as well as a second-order method, utilizing magnetometer measurements from a realistic nine-spacecraft observatory. The improved first-order method, which averages over select ensembles of four spacecraft, reconstructs the magnetic field associated with simple current sheets and numerical simulations of turbulence accurately over larger volumes compared to second-order methods or first-order methods using a single regular tetrahedron. Using this averaging method on data sets with fewer than nine measurement points, the volume of accurate reconstruction compared to a known magnetic vector field improves approximately linearly with the number of measurement points.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Correlating results from high resolution EBSD with TEM- and ECCI-based dislocation microscopy: Approaching single dislocation sensitivity via noise reduction

High resolution electron backscatter diffraction (HREBSD), an SEM-based diffraction technique, may be used to measure the lattice distortion of a crystalline material and to infer the geometrically necessary dislocation content. Uncertainty in the image correlation process used to compare diffraction patterns leads to an uneven distribution of measurement noise in terms of the lattice distortion, which results in erroneous identification of dislocation type and density. This work presents a method of reducing noise in HREBSD dislocation measurements by removing the effect of the most problematic components of the measured distortion. The method is then validated by comparing with TEM analysis of dislocation pile-ups near a twin boundary in austenitic stainless steel and with ECCI analysis near a nano-indentation on a tantalum oligocrystal. Finally, the HREBSD dislocation microscopy technique is able to resolve individual dislocations visible in TEM and ECCI and correctly identify their Burgers vectors.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Guest Editorial

Energy Systems Integration (ESI) is the process of coordinating the operation and planning of energy systems across multiple pathways and/or geographical scales to deliver reliable, cost-effective energy services with minimal impact on the environment. Energy systems have evolved from individual systems with little or no dependencies into a complex set of integrated systems at scales that include customers, cities, and regions. This evolution has been driven by political, economic, and environmental objectives. As we try to meet the globally recognized imperative to reduce carbon emissions through the deployment of large renewable energy capacities while also maintaining reliability and competitiveness, flexible energy systems are required. This flexibility can be attained through integrating various systems: by physically linking energy vectors, namely electricity, heat, and fuels; by coordinating these vectors across other infrastructures, namely water, data, and transport; by institutionally coordinating energy markets; and, spatially, by increasing market footprint with granularity all the way down to the customer level. Smart grids and ESI are related, in particular, the coupling between electricity, consumers, data and transport. This special section focuses on the aspects of ESI where electricity is coupled to water, heat and fuels and where this coupling brings challenges and/or opportunities.

24 POWER TRANSMISSION AND DISTRIBUTION↗