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Results for “Digitization”

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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

It begins - digitizing the Rocky Flats Collection

From 1952 to 1989, the Rocky Flats Plant near Golden, Colorado, manufactured plutonium cores - the nuclear weapon pits that still exist in the nation’s nuclear weapons stockpile. Not only did Rocky Flats personnel produce these plutonium cores, they also documented every facet of this highly technical process. Moreover, Rocky Flats was a premier center for actinide research and development, with these data as relevant today as they were yesterday

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Digitizer Evaluation Process [Slides]

The equipment we use for measurements are involved in multi-million dollar experiments. The experiments at the NNSS involve single shot experiments with high channel count.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Programmable Digital Devices used in Advanced Reactors

This paper introduces the concepts of common cause failure, diversity, and defense-in-depth used by the nuclear industry to analyze resilience in reactors. A survey of publicly traded and private companies building advanced reactors and their licensing status is presented. Safety and non-safety systems found in the NuScale Power design are summarized and the likely hardware and software categories used by those systems are enumerated. The importance of industry partners is highlighted. This paper also identifies an alternate path forward without industry partners to advance the knowledge needed to use artificial intelligence to analyze HBOMs and SBOMs to better understand reactor resiliency.

cybersecurity↗
DOE OSTI2025

GenAI-Based Digital Twins Aided Data Augmentation Increases Accuracy in Real-Time Cokurtosis-Based Anomaly Detection of Wearable Data

Early detection of potential infectious disease outbreaks is crucial for developing effective interventions. In this study, we introduce advanced anomaly detection methods tailored for health datasets collected from wearables, offering insights at both individual and population levels. Leveraging real-world physiological data from wearables, including heart rate and activity, we developed a framework for the early detection of infection in individuals. Despite the availability of data from recent pandemics, substantial gaps remain in data collection, hindering method development. To bridge this gap, we utilized Wasserstein Generative Adversarial Networks (WGANs) to generate realistic synthetic wearable data, augmenting our dataset for training. Subsequently, we use these augmented datasets to implement a cokurtosis-based technique for anomaly detection in multivariate time-series data. Our approach includes a comprehensive assessment of uncertainties in synthetic data compared to the actual data upon which it was modeled, as well as the uncertainty associated with fine-tuning anomaly detection thresholds in physiological measurements. Through our work, we present an enhanced method for early anomaly detection in multivariate datasets, with promising applications in healthcare and beyond. This framework could revolutionize early detection strategies and significantly impact public health response efforts in future pandemics.

Data-Driven Digital Twins↗
DOE OSTI2022

Challenges for Implementing FAIR Digital Objects with High Performance Workflows

New types of workflows are being used in science that couple traditional distributed and high-performance computing (HPC) with data-intensive approaches, and orchestrate ensembles of numerical simulations and artificial intelligence (AI) models. Such workflows may use AI models to supplement computation where numerical simulations may be too computationally expensive, to automate trivial yet time consuming operations, to perform preliminary selections among intractable numbers of combinations in domains as diverse as protein binding, fine-grid climate simulations, and drug discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Orthomosaic Images and Digital Elevation Models for the 2019 SALVO Campaign

The springtime, surface-albedo transition in the Alaskan Arctic and the forcings that determine the duration and nature of that transition are the focus of our Snow ALbedo eVOlution (SALVO I & II) campaign. The SALVO team are assessing the “whys” and “how longs” of the stages of the spring melt during which albedo values drop from 0.8 to 0.1, the largest and most significant change of the year. The near-shore location of the ARM NSA observatory makes this an ideal place to investigate these melt stages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

A digital map of the high center (HC) and low center (LC) polygon boundaries delineated from high-resolution LiDAR data for Utqiagvik (Barrow), Alaska

This dataset represents a map of the high center (HC) and low center (LC) polygon boundaries delineated from high resolution LiDAR data for the arctic coastal plain at Barrow, Alaska, presented as Figure 3a in the publication by Gangodagamage et al. (2014). The polygon troughs are considered as the surface expression of the ice-wedges. The troughs are in lower elevations than the interior polygon. The trough widths were initially identified from LiDAR data, and the boundary between two polygons assumed to be located along the lowest elevations on trough widths between them. Included are the components of a shapefile, PNG files, and the file-level metadata. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

TARGET: A multi-channel digitizer chip for very-high-energy gamma-ray telescopes

The next-generation very-high-energy (VHE) gamma-ray observatory, the Cherenkov Telescope Array, will feature dozens of imaging atmospheric Cherenkov telescopes (IACTs), each with thousands of pixels of photosensors. To be affordable and reliable, reading out such a mega-channel array requires event recording technology that is highly integrated and modular, with a low cost per channel. We present the design and performance of a chip targeted to this application: the TeV Array Readout with GSa/s sampling and Event Trigger (TARGET). This application-specific integrated circuit (ASIC) has 16 parallel input channels, a 4096-sample buffer for each channel, adjustable input termination, self-trigger functionality, and tight window-selected readout. We report the performance of TARGET in terms of sampling frequency, power consumption, dynamic range, current-mode gain, analog bandwidth, and cross talk. The large number of channels per chip allows a low cost per channel ($10 to $20 including front-end and back-end electronics but not including photosensors) to be achieved with a TARGET-based IACT readout system. In addition to basic performance parameters of the TARGET chip itself, we present a camera module prototype as well as a second-generation chip (TARGET 2), both of which have been produced.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Computational Methods for Analyzing Fluid Flow Dynamics from Digital Imagery

The main goal (long term) of this work is to perform computational dynamics analysis and quantify uncertainty from vector fields computed directly from measured data. Global analysis based on observed spatiotemporal evolution is performed by objective function based on expected physics and informed scientific priors, variational optimization to compute vector fields from measured data, and transport analysis proceeding with observations and priors. A mathematical formulation for computing flow fields is set up for computing the minimizer for the problem. An application to oceanic flow based on sea surface temperature is presented.

42 ENGINEERING↗