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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including "canopy greenness", processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the "greenness rising" and end of the "greenness falling" stages. Lastly, the database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗
DOE OSTI2015

SU-E-I-88: The Effect of System Dead Time On Real-Time Plastic and GOS Based Fiber-Optic Dosimetry Systems

Purpose: A methodology is presented to correct for measurement inaccuracies at high detector count rates using a plastic and GOS scintillation fibers coupled to a photomultiplier tube with digital readout. This system allows temporal acquisition and manipulation of measured data. Methods: The detection system used was a plastic scintillator and a separate gadolinium scintillator, both (0.5 diameter) coupled to an optical fiber with a Hamamatsu photon counter with a built-in microcontroller and digital interface. Count rate performance of the system was evaluated using the nonparalzable detector model. Detector response was investigated across multiple radiation sources including: orthovoltage x-ray system, colbat-60 gamma rays, proton therapy beam, and a diagnostic radiography x-ray tube. The dead time parameter was calculated by measuring the count rate of the system at different exposure rates using a reference detector. Results: The system dead time was evaluated for the following sources of radiation used clinically: diagnostic energy x-rays, cobalt-60 gamma rays, orthovoltage xrays, particle proton accelerator, and megavoltage x-rays. It was found that dead time increased significantly when exposing the detector to sources capable of generating Cerenkov radiation, all of the sources sans the diagnostic x-rays, with increasing prominence at higher photon energies. Percent depth dose curves generated by a dedicated ionization chamber and compared to the detection system demonstrated that correcting for dead time improves accuracy. On most sources, nonparalzable model fit provided an improved system response. Conclusion: Overall, the system dead time was variable across the investigated radiation particles and energies. It was demonstrated that the system response accuracy was greatly improved by correcting for dead time effects. Cerenkov radiation plays a significant role in the increase in the system dead time through transient absorption effects attributed to electron hole-pair creations within the optical waveguide.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

A Large-Scale, High-Resolution Hydrological Model Parameter Data Set for Climate Change Impact Assessment for the Conterminous US

To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic dataset with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation—including meteorologic forcings, soil, land class, vegetation, and elevation—were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous United States at refined 1/24° (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter dataset was prepared for the macro-scale Variable Infiltration Capacity (VIC) hydrologic model. The VIC simulation was driven by DAYMET daily meteorological forcing and was calibrated against USGS WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter dataset may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous United States. We anticipate that through this hydrologic parameter dataset, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter dataset will be provided to interested parties to support further hydro-climate impact assessment.

Oubeidillah, Abdoul A.↗
DOE OSTI2016

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this article, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Ionizing radiation effects in SONOS-based neuromorphic inference accelerators

Here, we evaluate the sensitivity of neuromorphic inference accelerators based on Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) charge trap memory arrays to total ionizing dose (TID) effects. Data retention statistics were collected for 16 Mbit of 40 nm SONOS digital memory exposed to ionizing radiation from a Co-60 source, showing good retention of the bits up to the maximum dose of 500 krad(Si). Using this data, we formulate a rate-equation-based model for the TID response of trapped charge carriers in the ONO stack, and predict the effect of TID on intermediate device states between ‘program’ and ‘erase’. This model is then used to simulate arrays of low-power, analog SONOS devices that store 8-bit neural network weights and support in situ matrix-vector multiplication. We evaluate the accuracy of the irradiated SONOS-based inference accelerator on two image recognition tasks – CIFAR-10 and the challenging ImageNet dataset – using state-of-the-art convolutional neural networks, such as ResNet-50. We find that across the datasets and neural networks evaluated, the accelerator tolerates a maximum TID between 10 krad(Si) and 100 krad(Si), with deeper networks being more susceptible to accuracy losses due to TID.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

CrossSim

CrossSim is a simulator for modeling neural-inspired machine learning algorithms on analog hardware, such as resistive memory crossbars. It includes noise models for reading and updating the resistances, which can be based on idealized equations or experimental data. It can also introduce noise and finite precision effects when converting values from digital to analog and vice versa. All of these effects can be turned on or off as an algorithm processes a data set and attempts to learn its salient attributes so that it can be categorized in the machine learning training/classification context. CrossSim thus allows the robustness, accuracy, and energy usage of a machine learning algorithm to be tested on simulated hardware.

Plimpton, StevenJ.↗
DOE OSTI2012

Review of the Digital Inject Book

The DIB greatly simplifies some aspects of generating inject data and also enables one to rapidly make changes to the scenario and generate new data. The underlying code for generating gamma ray inject data is GADRAS, while the underlying code for generating most of the neutron inject data is SrcSim. Since GADRAS has been around for a significant period of time and has a relatively large user base, reviewing GADRAS itself was considered out of scope for this TI project. However, how the DIB utilizes and interfaces with GADRAS to generate the inject data was considered to be in scope. SrcSim on the other hand does not have the same kind of pedigree as GADRAS and has not been as widely evaluated. Therefore, this review will cover not only how the DIB interfaces with SrcSim, but also the SrcSim code itself.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Non-invasive measurement of proppant pack deformation

In this study, we describe a method to non-invasively study the movement of proppant packs at the sub-fracture scale by applying three-dimensional digital image correlation techniques to X-ray tomography data. Proppant movement is tracked in a fractured core of Marcellus shale placed under a series of increasing confining pressures up to 10,000 psi. The analysis reveals the sudden failure of a region of the proppant pack, accompanied by the large-scale rearrangement of grains across the entire fracture surface. The failure of the pack coincides with the appearance of vortex-like grain motions similar to features observed in biaxial compression of two dimensional granular assemblies.

02 PETROLEUM↗
DOE OSTI2023

The entropy of galaxy spectra: how much information is encoded?

Abstract The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.

Ferreras, Ignacio (ORCID:0000000345843127)↗
DOE OSTI2020

Industry 4.0 and Intelligent Manufacturing Processes: A Review of Modern Sensing Technologies

Digital Manufacturing technologies have quickly become ubiquitous in the manufacturing industry. The transformation commonly referred to as the fourth industrial revolution, or Industry 4.0, has ushered in a wide range of communication technologies, connection mechanisms, and data analysis capabilities. These technologies provide powerful tools to create a more lean, profitable, and data-driven manufacturing processes. In this paper, we review modern communication technologies and connection architectures for Digital Manufacturing and Industry 4.0 applications. An introduction to Cyber-Physical Systems and a review of digital manufacturing trends is followed by an overview of data acquisition methods for manufacturing processes. Numerous communication protocols are presented and discussed for connecting disparate machines and processes. Flexible data architectures are discussed and examples of machine monitoring implementations are provided. Finally, select implementations of these communication protocols and architectures are surveyed with recommendations for future architecture implementations.

42 ENGINEERING↗
DOE OSTI2026

Data Center Cybersecurity, Supply Chain Risk Management, and Emerging Regulation Cohort Summary: Takeaways and Action Plans

This report summarizes the outcomes of the Data Center Cohort under the Department of Energy’s Technical Assistance for Digital Assurance (TADA) initiative, aimed at enhancing grid resilience through cybersecurity, supply chain risk management (SCRM), and Cyber-Informed Engineering (CIE). The cohort engaged 17 organizations across utilities, data center operators, vendors, and technology providers in three sessions combining presentations, discussions, and exercises. Key topics included AI-driven load behavior, cybersecurity vulnerabilities in UPS/BESS and cooling systems, governance gaps at utility–data center boundaries, and supply chain integrity. Five cross-cutting themes emerged: interconnection architecture vulnerabilities, fragmented governance, AI-driven stability risks, lack of regulatory frameworks, and long-term supply chain concerns. Actionable recommendations were developed, including implementing DMZ segmentation, formalizing vendor access agreements, designing AI workload limits, and advancing standards through NERC and state-level programs. These strategies aim to strengthen resilience, clarify responsibilities, and ensure secure integration of data centers into the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Data Unfolding with Wiener-SVD Method

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit

Architectural and hyper-parameter design choices can influence deep-learner (DL) model fidelity but can also be affected by malformed training and validation data. However, practitioners may spend significant time refining layers and hyper-parameters before discovering that distorted training data was impeding training progress. We found that an evolutionary algorithm (EA) can be used to diagnose this kind of DL problem. An EA evaluated thousands of DL configurations on Summit that yielded no overall improvement in DL performance, which suggested problems with the training and validation data. We suspected that Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement that was applied to previously generated digital surface models (DSMs), for which we were training DLs to find errors, had damaged the training data. Subsequent runs with an alternative global normalization yielded significantly improved DL performance. However, the DL Intersection Over Union (IOU) still exhibited consistent sub-par performance, which suggested further problems with the training data and DL approach. Nonetheless, we were able to diagnose this problem within a 12-hour span via Summit runs, which prevented several weeks of unproductive trial-and-error DL configuration refinement, and allowed for a more timely convergence on an ultimately viable solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗
DOE OSTI2015

GRAPhEME: a setup to measure (n, xn γ) reaction cross sections

Most of nuclear reactor developments are using evaluated data base for numerical simulations. However, the considered databases present still large uncertainties and disagreements. To improve their level of precision, new measurements are needed, in particular for (n, xn) reactions, which are of great importance as they modify the neutron spectrum, the neutron population, and produce radioactive species. The IPHC group started an experimental program to measure (n, xn gamma) reaction cross sections using prompt gamma spectroscopy and neutron energy determination by time of flight. Measurements of (n, xn gamma) cross section have been performed for {sup 235,238}U, {sup 232}Th, {sup nat,182,183,184,186}W, {sup nat}Zr. The experimental setup is installed at the neutron beam at GELINA (Geel, Belgium). The setup has recently been upgraded with the addition of a highly segmented 36 pixels planar HPGe detector. Significant efforts have been made to reduce radiation background and electromagnetic perturbations. The setup is equipped with a high rate digital acquisition system. The analysis of the segmented detector data requires a specific procedure to account for cross signals between pixels. An overall attention is paid to the precision of the measurement. The setup characteristic and the analysis procedure will be presented along with the acquisition and analysis challenges. Examples of results and their impact on models will be discussed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Geophysical estimation of shallow permafrost distribution and properties in an ice-wedge polygon-dominated Arctic tundra region

Shallow permafrost distribution and characteristics are important for predicting ecosystem feedbacks to a changing climate over decadal to century timescales because they can drive active layer deepening and land surface deformation, which in turn can significantly affect hydrologic and biogeochemical responses, including greenhouse gas dynamics. As part of the U.S. Department of Energy Next-Generation Ecosystem Experiments- Arctic, we have investigated shallow Arctic permafrost characteristics at a site in Barrow, Alaska, with the objective of improving our understanding of the spatial distribution of shallow permafrost, its associated properties, and its links with landscape microtopography. To meet this objective, we have acquired and integrated a variety of information, including electric resistance tomography data, frequency-domain electromagnetic induction data, laboratory core analysis, petrophysical studies, high-resolution digital surface models, and color mosaics inferred from kite-based landscape imaging. The results of our study provide a comprehensive and high-resolution examination of the distribution and nature of shallow permafrost in the Arctic tundra, including the estimation of ice content, porosity, and salinity. Among other results, porosity in the top 2 m varied between 85% (besides ice wedges) and 40%, and was negatively correlated with fluid salinity. Salinity directly influenced ice and unfrozen water content and indirectly influenced the soil organic matter content. A relatively continuous but depth-variable increase in salinity led to a partially unfrozen saline layer (cryopeg) located below the top of the permafrost. The cryopeg environment could lead to year-round microbial production of greenhouse gases. Results also indicated a covariability between topography and permafrost characteristics including icewedge and salinity distribution. In addition to providing insight about the Arctic ecosystem, through integration of lab-based petrophysical results with field data, this study also quantified the key controls on electric resistivity at this Arctic permafrost site, including salinity, porosity, water content, ice content, soil organic matter content, and lithologic properties.

58 GEOSCIENCES↗