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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling

In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state‐of‐the‐art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state‐of‐the‐art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human‐brain HARDI datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Soft matter and nanomaterials characterization by cryogenic transmission electron microscopy

Soft matter has historically been an unlikely candidate for investigation by electron microscopy techniques due to damage by the electron beam as well as inherent instability under a high vacuum environment. Characterization of soft matter has often relied on ensemble-scattering techniques. The recent development of cryogenic transmission electron microscopy (cryo-TEM) provides the soft matter community with an exciting opportunity to probe the structure of soft materials in real space. Cryo-TEM reduces beam damage and allows for characterization in a native, frozen-hydrated state, providing direct visual representation of soft structure. This article reviews cryo-TEM in soft materials characterization and illustrates how it has provided unique insights not possible by traditional ensemble techniques. Soft matter systems that have benefited from the use of cryo-TEM include biological-based “soft” nanoparticles (e.g., viruses and conjugates), synthetic polymers, supramolecular materials as well as the organic–inorganic interface of colloidal nanoparticles. We conclude that while many challenges remain, such as combining structural and chemical analyses; the opportunity for soft matter research to leverage newly developed cryo-TEM techniques continues to excite.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Unraveling the Dynamics of Nucleosome Arrays

The organization of genomic DNA into chromatin is a fundamental determinant of genome stability, regulation, and cellular function. Nucleosomes, the basic repeating units of chromatin, assemble into higher-order structures whose organization and heterogeneity remain difficult to characterize using conventional ensemble-averaged techniques. A key need in the field is the development of experimental approaches capable of directly visualizing nucleosome assemblies and their structural variability at the single-molecule level. This LDRD Lab-Wide project focused on establishing and evaluating atomic force microscopy (AFM)–based approaches for the characterization of nucleosome assemblies. The work emphasized experimental workflows for preparing, imaging, and assessing multi-nucleosome systems, rather than isolated single nucleosomes. Through method development and exploratory measurements, the project demonstrated the feasibility of applying scanning probe microscopy to investigate chromatin-relevant assemblies and provided preliminary insight into the strengths and limitations of this approach for future quantitative studies. Results and lessons learned from this effort were disseminated to the broader scientific community through multiple national conference presentations, helping to position LLNL for continued work in chromatin and genome organization research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

FunMC^2: A Filter for Uncertainty Visualization of Marching Cubes on Multi-Core Devices

Visualization is an important tool for scientists to extract understanding from complex scientific data. Scientists need to understand the uncertainty inherent in all scientific data in order to interpret the data correctly. Uncertainty visualization has been an active and growing area of research to address this challenge. Algorithms for uncertainty visualization can be expensive, and research efforts have been focused mainly on structured grid types. Further, support for uncertainty visualization in production tools is limited. In this paper, we adapt an algorithm for computing key metrics for visualizing uncertainty in Marching Cubes (MC) to multi-core devices and present the design, implementation, and evaluation for a Filter for uncertainty visualization of Marching Cubes on Multi-Core devices (FunMC2). FunMC2 accelerates the uncertainty visualization of MC significantly, and it is portable across multi-core CPUs and GPUs. Evaluation results show that FunMC2 based on OpenMP runs around 11× to 41× faster on multi-core CPUs than the corresponding serial version using one CPU core. FunMC2 based on a single GPU is around 5× to 9× faster than FunMC2 running by OpenMP. Moreover, FunMC2 is flexible enough to process ensemble data with both structured and unstructured mesh types. Furthermore, we demonstrate that FunMC2 can be seamlessly integrated as a plugin into ParaView, a production visualization tool for post-processing.

Wang, Jay↗
NASA NTRS2002

Image Dis-Integration for Improved Plasmasphere Visualization

Traditionally, study of the plasmasphere has involved terrestrial observation of local characteristics. Global modeling of the plasmasphere in such an observation regime made use of an ensemble of (sparse) local measurements. Recently, sensors aboard the IMAGE (Imager for Magnetopause-to-Aurora Global Exploration) satellite (in particular, the EUV (Extreme Ultra Violet) Imager) have created the potential for truly global study of the plasmasphere. IMAGE was launched in spring of 2000 in an orbit with apogee altitude 7.2 R E (Earth radii) and perigee altitude 1000 km. IMAGE's EUV sensor allows an external view of the distribution of cold plasma in the plasmasphere to be acquired. EUV is designed to image light emission at 30.4 nanometers, which is the emission wavelength of the He + ion in the presence of solar radiation. He + makes up approximately 15-20% of the plasma in the plasmasphere, thus imaging of He + enables determination of plasma distribution. The EUV instrument provides a 90° by 84° field of view which is imaged as an equally spaced 150x140 pixel array on a spherical imaging surface. The EUV produces an image approximately every 10 minutes when the sensor is operating. Since EUV images contain line-of-sight integrations of plasma distributions, they do not directly express equatorial plane density (which would enable comparison of observed plasma distributions with predictions from models). Furthermore, the plasma density at any point in three-space is not known. The goal of our work was development of a technique that can enable plasma density to be determined throughout three-space. Our approach to creation of a three-space representation of the plasma distribution involves disintegrating the EUV lines of sight to form a volumetric map of plasma densities.

Timothy S. Newman↗
DOE OSTI2023

Measurement of needle and armature dynamics in a gasoline direct injector by high-speed neutron imaging

In modern spark ignition engines, precise delivery of fuel with gasoline direct injection has become increasingly important in the effort to meet ever stricter efficiency and emissions regulations. Use of multiple small close-coupled injections has become more common in attempt to precisely control fuel distribution in the cylinder, but these strategies are hindered by nonlinear injection effects due to operation in the ballistic region of the solenoid-operated valve and due to armature bounce at the end of injection. Understanding the internal dynamics of the injector is crucial to minimizing and controlling non-linearity and shot-to-shot variation, but the experimental techniques available to date are capable only of tracking the position of either the top of the needle (via laser sensors or by monitoring current and voltage in the solenoid coil) or the bottom of the needle (via transparent nozzles or high-speed x-ray imaging). Furthermore, a complete picture of the axial and radial motion of valve needle has until now remained elusive. In this work, we present high-speed ensemble neutron transmission imaging of an entire 8-hole gasoline direct injector operating at 200 bar, allowing for both visualization and quantification of the actuation dynamics including lift and wobble of the valve ball, oscillation and bending of the valve needle, lift, rocking, and bounce of the armature, compression of the springs, and radial swelling of the solenoid during energization. Because neutrons offer high penetration through the metal injector while also being sensitive to the 1 H nuclei in fuel molecules, it is also possible to simultaneously see the fluid dynamics of the injection process, including filling of the sac volume, emanation of the spray through the nozzle holes and into the downstream gas, and the formation and evolution of fuel films on the tip of the injector and the walls of the spray container.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1992

Remote Sensing Data from CLARET: A Prototype Cart Data Set

A data set containing radiation, meteorological, and cloud sensor observations is documented. It was prepared for use by the Department of Energy's Atmospheric Radiation Measurement (ARM) program and other interested scientists. These data are a precursor of the types of data that ARM Cloud And Radiation Testbed (CART) sites will provide. The data are from the Cloud Lidar And Radar Exploratory Test (CLARET) conducted by the Wave Propagation Laboratory during autumn 1989 in the Denver-Boulder area of Colorado primarily for the purpose of developing new cloud-sensing techniques on cirrus. After becoming aware of this experiment, ARM scientists requested archival of subsets or the data to assist in the developing ARM program. Five CLARET cases were selected: two with cirrus, one with stratus, one with mixed-phase clouds, and one with clear skies. The cases range from 2 to 9.5 h in length. A pyranometer, pyrgeometer, pyrheliometer, and an infrared radiometer constituted the ensemble of instruments that provided surface radiation data. A lidar, radar, and ceilometer observed the cloud geometrical structure, and visual reports and all-sky camera observations were assimilated to provide cloud cover data. Radiosondes, wind profiler, RASS (profiling virtual temperature), microwave radiometers (observing column integrated liquid water and water vapor), and standard surface measurements provided meteorological data. Satellite data from the stratus case and one cirrus case were analyzed for statistics on cloud cover and top height. The main body of the selected data are available on diskette from the Wave Propagation Laboratory or Los Alamos National Laboratory. In addition to documenting the data set, this report describes CLARET and gives a bibliography of publications associated with the project. Some preliminary results of CLARET' research are also summarized. Simultaneous CO 2 lidar and radar backscatter measurements were shown to provide estimates of the effective radius of ice particles. Simultaneous radar and infrared radiometer data appear useful for estimating column-integrated numbers and average sizes of ice cloud particles. Ice water content obtained with this method compared favorably with values from another empirical technique using radar data alone. Depolarization of the CO 2 lidar signal from ice clouds was surprisingly small, suggesting that calculation of backscatter from nonspherical particles for this lidar is a tractable problem. Examples are also cited of CO 2 lidar measurements of the effective radius of water cloud drop size distributions and of inference of the size of pristine ice crystals that assume a particular orientation in the air. These parameters are all important to radiative transfer through clouds.

Clouds (Meteorology)↗
DOE OSTI2015

Three-dimensional spectrum mapping of bright emission centers: Investigating the brightness-limiting process in Eu-doped GaN red light emitting diodes

A pulse-driven emission-spectroscopy mapping technique is used to investigate the bright emission centers in Eu-doped GaN (GaN:Eu) red light emitting diodes (LED). The LEDs are operated in pulse-driven mode, and the emission spectra are acquired for a range of pulse frequencies. This ensemble of emission spectral data yields a three-dimensional mapping that allows the origin of emission lines to be identified by visual inspection. The identification was achieved even for a weak {sup 5}D{sub 0} → {sup 7}F{sub 3} transition in conventional photoluminescence measurements. A peculiar split is observed in the {sup 5}D{sub 0} → {sup 7}F{sub 3} transition for the bright emission center referred to as OMVPE 8. Despite the unique transition at this emission center, the emission efficiencies for the {sup 5}D{sub 0} → {sup 7}F{sub 3} and {sup 5}D{sub 0} → {sup 7}F{sub 2} transitions were identical. This finding indicates that the excitation of the emission centers, rather than the radiative transitions, is the limiting process that determines the GaN:Eu red LED brightness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Leveraging AI and Spatial Data to Unlock Pipeline Integrity Insights: NETL’s Advanced Infrastructure Integrity Model (AIIM)

Maintaining the integrity of natural gas infrastructure plays a critical role in ensuring energy security. Robust, data-driven foundational AI models for pipeline integrity can help address risk management and mitigation issues. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. The AIIM dashboard was released in 2022 and utilizes multi-ML models for ensemble-type insights. It was expanded to include analytics on reported incidents. It was developed as an ESRI Dashboard to support data visualization & interrogation and contains pipeline data and model results.

Advanced Infrastructure Integrity Model (AIIM)↗
NASA NTRS1980

A Scanning laser-velocimeter technique for measuring two-dimensional wake-vortex velocity distributions

A rapid scanning two dimensional laser velocimeter (LV) has been used to measure simultaneously the vortex vertical and axial velocity distributions in the Langley Vortex Research Facility. This system utilized a two dimensional Bragg cell for removing flow direction ambiguity by translating the optical frequency for each velocity component, which was separated by band-pass filters. A rotational scan mechanism provided an incremental rapid scan to compensate for the large displacement of the vortex with time. The data were processed with a digital counter and an on-line minicomputer. Vaporized kerosene (0.5 micron to 5 micron particle sizes) was used for flow visualization and LV scattering centers. The overall measured mean-velocity uncertainity is less than 2 percent. These measurements were obtained from ensemble averaging of individual realizations.

Gartrell, L. R.↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
DOE OSTI2019

Effects of α-tubulin acetylation on microtubule structure and stability

Acetylation of K40 in α-tubulin is the sole posttranslational modification to mark the luminal surface of microtubules. It is still controversial whether its relationship with microtubule stabilization is correlative or causative. We have obtained high-resolution cryo-electron microscopy (cryo-EM) reconstructions of pure samples of αTAT1-acetylated and SIRT2-deacetylated microtubules to visualize the structural consequences of this modification and reveal its potential for influencing the larger assembly properties of microtubules. We modeled the conformational ensembles of the unmodified and acetylated states by using the experimental cryo-EM density as a structural restraint in molecular dynamics simulations. We found that acetylation alters the conformational landscape of the flexible loop that contains αK40. Modification of αK40 reduces the disorder of the loop and restricts the states that it samples. We propose that the change in conformational sampling that we describe, at a location very close to the lateral contacts site, is likely to affect microtubule stability and function.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Infectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. To meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. We forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. While no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. As the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

Investigation of Neural Strategies of Visual Search

The goal of this project was to measure how neurons in the superior colliculus (SC) change their activity during a visual search task. Specifically, we proposed to measure how the activity of these neurons was altered by the discriminability of visual targets and to test how these changes might predict the changes in the subjects performance. The primary rationale for this study was that understanding how the information encoded by these neurons constrains overall search performance would foster the development of better models of human performance. Work performed during the period supported by this grant has achieved these aims. First, we have recorded from neurons in the superior colliculus (SC) during a visual search task in which the difficulty of the task and the performance of the subject was systematically varied. The results from these single-neuron physiology experiments shows that prior to eye movement onset, the difference in activity across the ensemble of neurons reaches a fixed threshold value, reflecting the operation of a winner-take-all mechanism. Second, we have developed a model of eye movement decisions based on the principle of winner-take-all . The model incorporates the idea that the overt saccade choice reflects only one of the multiple saccades prepared during visual discrimination, consistent with our physiological data. The value of the model is that, unlike previous models, it is able to account for both the latency and the percent correct of saccade choices.

Krauzlis, Richard J.↗
DOE OSTI2016

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Emerging Frameworks for Advancing Scientific Workflows Research, Development, and Education

Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2021 lightning talks, which cover four broad topics: (i) libEnsemble, a Python library to coordinate the concurrent evaluation of dynamic ensembles of calculations; (ii) Edu WRENCH, a set of online pedagogic modules that provides simulation-driven hands-on activity in the browser; (iii) VisDict, an envisioned visual dictionary framework that will translate terms, jargon, and concepts between research domains and workflow providers; and (iv) Pegasus Kickstart, a lightweight tool for capturing workflow tasks' performance, including performance metrics from Nvidia GPUs.

Casanova, Henri↗
DOE OSTI2025

Single-molecule reaction mapping uncovers diverse behaviours of electrocatalytic surface Pd–H intermediates

Many vital electrocatalytic transformations hinge on reactive surface metal–hydrogen intermediates (M–H*), yet the low concentration and transient nature of such intermediates present formidable challenges to in-depth investigation. Here we use single-molecule super-resolution reaction imaging to directly probe surface palladium–hydrogen (Pd–H*) intermediates on individual palladium nanocubes during electrocatalytic hydrogen evolution. Our approach visualizes hydrogen spillover from palladium to the surrounding substrate surface over hundreds of nanometres away and dissects substantial inter- and intraparticle heterogeneity. Through Gaussian-broadening kinetic analysis, we reveal that ensemble-averaged measurements systematically overestimate the stability of Pd–H*. Moreover, we resolve three subpopulations of palladium nanocubes with distinct reactivity features, uncovering critical correlations between intermediate stability, hydrogenation reactivity and transition-state properties. Finally, our findings highlight the necessity of single-particle resolution for capturing the intrinsic complexity of electrocatalysts; our approach is also broadly applicable to interrogate surface-reactive intermediates across a wide array of electrocatalytic pathways.

electrocatalysis↗