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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Spatiotemporal Sequence Forecasting Platform to Advance the Prediction of Changing Spatiotemporal Patterns of CO 2 Concentration by Incorporating Human Activity and Hydrological Extremes

Focal Areas (2): Predictive modeling using AI techniques and AI-derived model components. It describes the use of AI and other tools to design a prediction system comprising a hierarchy of models. Specifically, this white paper focuses on the development of a collaborative deep learning platform for evaluating and predicting spatiotemporal relationships between the hydrological and carbon cycles and includes capabilities for considering biogenic and anthropogenic inputs to these systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multiscale Patterning from Competing Interactions and Length Scales

We live in an era of complexity marked by impressive new tools powering the scientific method to accelerate discovery, prediction, and control of increasingly complex systems. In common with many disciplines and societal challenges and opportunities, materials and condensed matter sciences are beneficiaries. The volume and fidelity of experimental, computational, and visualization data available, and tools to rapidly interpret them, are remarkable. Conceptual frameworks, including multiscale, multiphysics modeling of this complexity, are fueled by the data and, in turn, guide directions for future experimental and computational strategies. In this spirit, we discuss the importance of competing interactions, length scales, and constraints as pervasive sources of spatio-temporal complexity. We use representative examples drawn from materials and condensed matter, including the important role of elasticity in some technologically important quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Installation Assignment Durations and Patterns of Army Personnel

The purpose of this document is to report on the development of personnel assignment duration statistical metrics and statistical descriptions for groups of Army personnel aggregated by rank, career management field, and other descriptors for 32 Continental United States (CONUS) Army installations. This information can be used for generating evidence-based measures of personnel exposure times associated with environmental health considerations at the installation and career management field levels. Assignment duration statistical descriptions were developed to provide temporal metrics for groups of personnel at Army installations as foundational data to support a variety of assessments. Statistical results of personnel assignment durations at the installation level are provided in several formats. The 50th percentile assignment duration represents the median number of days personnel in groups aggregated by rank and career management field spend at the 32 CONUS Army installations in this study. Likewise, other duration percentiles (90th) and quartiles of assignment durations for groups aggregated by rank and career management field spend at each installation were developed. Assignment path analysis provide statistical descriptions of assignment durations at Army installations throughout increments of time (e.g., 5 years, 10 years). Key groupings of assignment paths were achieved by analyzing the duration of assignments at installations in chronological order from entry throughout specified increments of military service to generate the most common assignment paths across the 32 CONUS Army installations in this study. The developed assignment duration analytics and duration statistical descriptions provide foundational temporal metrics for groups of personnel at 32 CONUS Army installations to support a variety of assessments including those by human resource planners and environmental health specialists.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Basic Research of Intrinsic, Tamper Indication Markings and Patterns Defined by Pulsed Laser Irradiation: 2015 Annual Report IACRO 13-5897I (DTRA Project Year End Report)

Information from 2015 annual report highlighting several tasks, including: Task 7: Research of microspectrophotometry for inspection and validation of laser color markings. Task 8: Investigate new laser fabrication techniques that produce color markings with improved corrosion resistance. Task 9: Research new methods for laser marking curved surfaces (and large areas). Task 10: Complete model simulations of laser-induced ripple formation-involves an ElectroMagnetic field solver.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Notes on the Variety of Multipactor Patterns

The experimental results on multipactor often are not in a good agreement with theoretical predictions and numerical simulations. The experimental results can noticeably vary from test to test even in the same geometry of electrodes, depending on the material they made of and condition of their surfaces, i.e., secondary emission properties. On the other hand, an actual secondary emission yield (SEY) of electrode material is never known a priory in the experiments. In practice, the SEY data for given material used for multipactor evaluation are obtained in specialized measurements. A difference between actual and tabulated SEY of material may result in a disagreement between pre-dictions and measurements. In this work the impact of the basic SEY characteristic variations on the multipactor dynamics in a variety of coaxial and rectangular waveguides is studied. The study was performed numerically with the use of CST Particle Studio.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Novel Patterned Surfaces for Improved Condenser Performance in Power Plants

In this project, we seek to improve the thermoelectric power plant performance through engi-neered nonwetting condenser tube surface designs that can enhance heat transfer performance by: (1) promoting dropwise condensation of the steam on the shell side and eliminating flooding of the surface structure by maintaining low droplet adhesion, thus increasing condensation heat transfer coefficient; (2) deterring fouling and corrosion, thereby reducing fouling resistance and improving heat transfer; (3) possibly reducing drag and increasing convective heat transfer inside coolant tubes; and (4) reducing the number of tubes, coolant water usage, and levelized cost of condenser.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Continual Learning for Pattern Recognizers using Neurogenesis Deep Learning

Deep neural networks have emerged as a leading set of algorithms to infer information from a variety of data sources such as images and time series data. In their most basic form, neural networks lack the ability to adapt to new classes of information. Continual learning is a field of study attempting to give previously trained deep learning models the ability to adapt to a changing environment. Previous work developed a CL method called Neurogenesis for Deep Learning (NDL). Here, we combine NDL with a specific neural network architecture (the Ladder Network) to produce a system capable of automatically adapting a classification neural network to new classes of data. The NDL Ladder Network was evaluated against other leading CL methods. While the NDL and Ladder Network system did not match the cutting edge performance achieved by other CL methods, in most cases it performed comparably and is the only system evaluated that can learn new classes of information with no human intervention.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data

The ongoing growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Through access to a large number of time series data sets from different power plants, we have found common issues that impede the modeling process. Namely, the time series data are hard to transfer between groups due to differences in variable nomenclature, and the quality of the data sets can vary. We address the issue of variable nomenclature by FAIRifying spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. We have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. With access to large time series data sets across many power plants, we can utilize spatiotemporal coherence between the sites in order to improve the quality of our data. To solve the issue of data missingness, we propose the use of Spatiotemporal-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.

14 SOLAR ENERGY↗
DOE OSTI2022

Timing based clustering in the Northern Finland Birth Cohorts 1966 and 1986 suggests two new patterns for childhood BMI curve [Poster]

Childhood body mass index (BMI) is a widely used measure of adiposity in children (<18 years of age). Children grow with individual tempo and individuals of the same age, or of the same BMI, might be in different phases in their individual growth curves. Variability between different childhood BMI curves can be separated in two components: phase variability (x-axis; time) and amplitude variability (y-axis; BMI). Phase variability can be thought of arising from differences in maturational age between individuals. This is related to the timing of peaks and valleys in a child’s BMI curve.

59 BASIC BIOLOGICAL SCIENCES↗