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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 307 records · Page 17

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Room temperature multiplexed gas sensing using chemical-sensitive 3.5-nm-thin silicon transistors

There is great interest in developing a low-power gas sensing technology that can sensitively and selectively quantify the chemical composition of a target atmosphere. Nanomaterials have emerged as extremely promising candidates for this technology due to their inherent low-dimensional nature and high surface-to-volume ratio. Among these, nanoscale silicon is of great interest because pristine silicon is largely inert on its own in the context of gas sensing, unless functionalized with an appropriate gas-sensitive material. We report a chemical-sensitive field-effect transistor (CS-FET) platform based on 3.5-nm-thin silicon channel transistors. Using industry-compatible processing techniques, the conventional electrically active gate stack is replaced by an ultrathin chemical-sensitive layer that is electrically nonconducting and coupled to the 3.5-nm-thin silicon channel. We demonstrate a low-power, sensitive, and selective multiplexed gas sensing technology using this platform by detecting H 2 S, H 2 , and NO 2 at room temperature for environment, health, and safety in the oil and gas industry, offering significant advantages over existing technology. Moreover, the system described here can be readily integrated with mobile electronics for distributed sensor networks in environmental pollution mapping and personal air-quality monitors.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Energy storage: The future enabled by nanomaterials

Lithium-ion batteries, which power portable electronics, electric vehicles, and stationary storage, have been recognized with the 2019 Nobel Prize in chemistry. The development of nanomaterials and their related processing into electrodes and devices can improve the performance and/or development of the existing energy storage systems. We provide a perspective on recent progress in the application of nanomaterials in energy storage devices, such as supercapacitors and batteries. The versatility of nanomaterials can lead to power sources for portable, flexible, foldable, and distributable electronics; electric transportation; and grid-scale storage, as well as integration in living environments and biomedical systems. To overcome limitations of nanomaterials related to high reactivity and chemical instability caused by their high surface area, nanoparticles with different functionalities should be combined in smart architectures on nano- and microscales. The integration of nanomaterials into functional architectures and devices requires the development of advanced manufacturing approaches. We discuss successful strategies and outline a roadmap for the exploitation of nanomaterials for enabling future energy storage applications, such as powering distributed sensor networks and flexible and wearable electronics.

catalysis (heterogeneous)↗

DOE OSTI2013

Lightweight Distributed Metric Service (LDMS) v. 4.0

LDMS is a framework for collection and transport of system monitoring data from computational clusters. It utilizes existing data sources on the cluster (e.g., temperature sensors, network traffic counters). The collection and transport mechanisms of LDMS are lightweight and do not significantly adversely impact the performance of concurrently running applications.

Gentile, Ann↗

DOE OSTI2019

Urban Deployment Model (UDM) v1.0

The software, called the Urban Deployment Model (UDM), is an integrated set of tools for the simulation and analysis of static and mobile radiation detection systems in complex urban environments. The UDM includes a graphical user interface through which the workflow associated with setting up and executing a simulation may be established. The modular toolsets combine aspects of radiation transport, navigation, graph theory, optimization, and visualization to allow the complete end-to-end simulation of large scale, highly-distributed sensor networks.

Abgrall, Nicolas↗

DOE OSTI2016

General Line Ampacity State Solver

INL’s General Line Ampacity State Solver (GLASS) is a high performance software platform that enables advanced grid system operations and planning efforts to be computed in real time. It enables the combination of advanced sensors and sensor networks, communication and data transfer channels, secure and reliable data management, and real-time processing through a flexible system architecture, with the ability to support and integrate with any utility enterprise system. GLASS is designed to help resolve the problems associated with thermally limited transmission and distribution networks, namely bare overhead power transmission lines. GLASS enables the methodology of Concurrent Cooling (Dynamic Line Rating) by reducing the need of using a cost-prohibited number of weather stations (reduce the number of weather stations per mile of overhead power line) to achieve the resolution necessary for individual electric transmission line span capacity calculations. GLASS achieves this by coupling real-time grid operations data and infrastructure architecture information with real-time field deployed weather station measurements and pre-computed Computational Fluid Dynamics (CFD)-based wind modeling interpolation parameters (look-up tables). GLASS uses mathematical models based on the IEEE 738 standard and builds off of the provided iterative solution using modern software paradigms and programming styles to provide a more robust and modular software system. GLASS also interfaces in real-time with utility companies using known transmission line spans and structures. GLASS provides real-time ampacity calculations based on recent weather station data as well as transmission line specifications. GLASS also allows the user to interact graphically with internal databases, which include information such as transmission line conductor types and thermal rating limits, weather station names and locations, specific transmission line span and structure locations, as well as recent ampacity calculations.

Gentle, Jake↗

DOE OSTI2024

An Accurate and Automated Convective Vortex Detection Method for Long-Duration Infrasound Microbarometer Data

Heating of the surficial layer of the atmosphere often generates convective vortices, known as “dust devils” when they entrain visible debris. Convective vortices are common on both Earth and Mars, where they affect the climate via dust loading, contribute to wind erosion, impact the efficiency of photovoltaic systems, and potentially result in injury and property damage. However, long-duration terrestrial convective vortex activity records are rare. We have developed a high-precision and high-recall method to extract convective vortex signatures from infrasound microbarometer data streams. The techniques utilizes a wavelet-based detector to capture potential events and then a template matching system to extract the duration of the vortex. Since permanent and temporary infrasound sensors networks are present throughout the globe (many with open data), our method unlocks a vast new convective vortex dataset without requiring the deployment of specialized instrumentation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

A vibro-haptic human-machine interface for structural health monitoring

The structural health monitoring (SHM) community’s goal has been to endow physical systems with a nervous system not unlike those commonly found in living organisms. Typically the SHM community has attempted to do this by instrumenting structures with a variety of sensors, and then applying various signal processing and classification procedures to the data in order to detect the presence of damage, the location of damage, the severity of damage, and to estimate the remaining useful life of the structure. This procedure has had some success, but we are still a long way from achieving the performance of nervous systems found in biology. This is primarily because contemporary classification algorithms do not have the performance required. In many cases expert judgment is superior to automated classification. This work introduces a new paradigm. We propose interfacing the human nervous system to the distributed sensor network located on the structure and developing new techniques to enable human-machine cooperation. Results from the field of sensory substitution suggest this should be possible. This study investigates a vibro-haptic human-machine interface for SHM. The investigation was performed using a surrogate three-story structure. The structure features three nonlinearity-inducing bumpers to simulate damage. Accelerometers are placed on each floor to measure the response of the structure to a harmonic base excitation. The accelerometer measurements are preprocessed. As a result, the preprocessed data is then encoded encoded as a vibro-tactile stimulus. Human subjects were then subjected to the vibro-tactile stimulus and asked to characterize the damage in the structure.

42 ENGINEERING↗

DOE OSTI2022

Directional Cooling-Induced Fracturing: Directional Cooling Data Under True-Triaxial Stress State

Data includes Directional Cooling-Induced Fracturing (DCIF) testing data using westerly granite blocks. This submission includes data from two samples of westerly granite, lab sample 7 and 8. Files contain stress, temperature and acoustic emission data acquired during polyaxial, laboratory testing of westerly granite blocks for each sample. FILES: .tradb -- files containing acoustic emission waveforms; sqlite3 database .pridb -- files containing basic acoustic emission information (no waveforms); sqlite3 database .geom -- geometry of the AE sensor network (ASCII)

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Utah FORGE: GES Well 16A(78)-32 and Well 16B(78)-32 Stimulation Seismic Event Catalogs

This dataset contains seismic event catalogs from the hydraulic stimulation of wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site in April 2024. The data was collected by Geo Energy Suisse (GES) using a variety of seismic monitoring technologies, including 3-component (3C) geophones and distributed acoustic sensing (DAS) systems. These technologies were deployed across several locations, including wells 16A, 16B, and Delano-1, with sensor arrays at multiple depths to capture microseismic activity during the stimulations. The catalogs provide both real-time and manually checked seismic event locations, with detailed parameters such as trigger conditions, velocity models, and data acquisition settings. The dataset includes information on the stimulation stages, event rates, and hydraulic injection conditions for each well, with a report detailing the data acquisition configuration and seismic event location methodologies. Users will need to reference the included report for a complete understanding of the sensor network, data processing techniques, and accuracy considerations.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

A Comparison of Machine Learning Methods of Association Tested on Dense Nodal Arrays

The association of phase picks to form events is one of the fundamental components of seismology. Large and dense sensor networks, such as >1000 geophone arrays (and distributed acoustic sensing), offer unique challenges in association due to the vast numbers of observations and high likelihood of errant picks. In addition, the large number of stations can greatly increase the time it takes to perform the association. For this reason, machine learning (ML) methods might provide a more optimal method of association for such networks. In this work, we examine how well ML methods (e.g., Gaussian mixture model association, PhaseLink, and Graph Earthquake Neural Interpretation Engine) can incorporate dense seismic arrays into regional networks and how well they handle the increasing numbers of stations. Here, we test their capabilities on two dense seismic deployments, one within Rock Valley Nevada (52 nodes and a 9-station sparse local network), and the LArge-n Seismic Survey in Oklahoma dense nodal array (>1800 vertical-component geophones). Processing data from these two different styles of dense seismic deployments allows testing of how the ML algorithms can merge array data with a broader regional network, how they deal with poorly picked phases, and how they handle anthropogenic noise. We compare the ML-associated bulletins to those obtained using the Rapid Earthquake Association and Location algorithm, a more traditional method of association. We find that there are very small differences in results between the methods for small networks (<100 stations) with low pick rates. For large networks (>1000), there are enough errant picks that some of the ML methods start to create false events out of noise. We also find that the ML methods vary in computation time significantly but are all faster than the traditional method tested here.

58 GEOSCIENCES↗

DOE OSTI2013

Karst characterization in a semi-arid region using gravity, seismic, and resistivity geophysical techniques.

We proposed to customize emerging in situ geophysical monitoring technology to generate time-series data during sporadic rain events in a semi-arid region. Electrodes were to be connected to wireless \nodes" which can be left in the eld for many months. Embedded software would then increase sampling frequency during periods of rainfall. We hypothesized that this contrast between no-volume ow in karst passageways dur- ing dry periods and partial- or saturated-volume ow during a rain event is detectable by these Wireless Sensor Network (WSN) geophysical nodes, we call this a Wireless Resistivity Network (WRN). The development of new methodologies to characterize semi-arid karst hydrology is intended to augment Sandia National Laboratorys mission to lead e orts in energy technologies, waste disposal and climate security by helping to identify safe and secure regions and those that are at risk. Development and initial eld testing identi ed technological barriers to using WRNs for identifying semi-arid karst, exposing R&D which can be targeted in the future. Gravity, seismic, and resis- tivity surveys elucidated how each technique might e ectively be used to characterize semi-arid karst. This research brings to light the importance and challenges with char- acterizing semi-arid karst through a multi-method geophysical study. As there have been very few studies with this emphasis, this study has expanded the body of practical experience needed to protect the nations water and energy security interests.

Barnhart, Kevin Scott↗

DOE OSTI2013

Compressive sensing for nuclear security

Special nuclear material (SNM) detection has applications in nuclear material control, treaty verification, and national security. The neutron and gamma-ray radiation signature of SNMs can be indirectly observed in scintillator materials, which fluoresce when exposed to this radiation. A photomultiplier tube (PMT) coupled to the scintillator material is often used to convert this weak fluorescence to an electrical output signal. The fluorescence produced by a neutron interaction event differs from that of a gamma-ray interaction event, leading to a slightly different pulse in the PMT output signal. The ability to distinguish between these pulse types, i.e., pulse shape discrimination (PSD), has enabled applications such as neutron spectroscopy, neutron scatter cameras, and dual-mode neutron/gamma-ray imagers. In this research, we explore the use of compressive sensing to guide the development of novel mixed-signal hardware for PMT output signal acquisition. Effectively, we explore smart digitizers that extract sufficient information for PSD while requiring a considerably lower sample rate than conventional digitizers. Given that we determine the feasibility of realizing these designs in custom low-power analog integrated circuits, this research enables the incorporation of SNM detection into wireless sensor networks.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Ionospheric acoustic and gravity wave activity above low-latitude thunderstorms

In this report, we study the correlation between thunderstorm activity and ionospheric gravity and acoustic waves in the low-latitude ionosphere. We use ionospheric total electron content (TEC) measurements from the Low Latitude Ionospheric Sensor Network (LISN) and lightning measurements from the World- Wide Lightning Location Network (WWLLN). We find that ionospheric acoustic waves show a strong diurnal pattern in summer, peaking in the pre-midnight time period. However, the peak magnitude does not correspond to thunderstorm area, and the peak time is significantly after the peak in thunderstorm activity. Wintertime acoustic wave activity has no discernable pattern in these data. The coverage area of ionospheric gravity waves in the summer was found to increase with increasing thunderstorm activity. Wintertime gravity wave activity has an observable diurnal pattern unrelated to thunderstorm activity. These findings show that while thunderstorms are not the only, or dominant source of ionospheric perturbations at low-latitudes, they do have an observable effect on gravity wave activity and could be influential in acoustic wave activity.

58 GEOSCIENCES↗

DOE OSTI2017

Geoacoustics Takes to the Sky

Low frequency sound waves transmit information on both natural and anthropogenic phenomena, transfer energy between the lower and upper atmosphere, and often propagate for thousands of kilometers. These sound waves are typically in the "infrasound" (below human hearing) range. Although the vast majority of geoacoustic sensor networks have existed on the Earth's surface, microphones drifting in the atmosphere may have much greater sensitivity as well as sample regions inaccessible from the ground.

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Sensor Placement Optimization using Chama

Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama is currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2019

Ubiquitous Nuclear Threat Detection Final Report, Harvey Mudd College Engineering Clinic

The Lawrence Livermore National Labs clinic team was asked to design a repeatable, portable procedure that would incentivize participants to carry detectors that form a radiation sensor network along repeated paths, to precisely known locations and past known radioactive sources. To achieve these goals, the team designed day-long and week-long data collection procedures in the form of puzzle hunt events. The team also created an Android app in order to support this measurement campaign and foster engagement. A ground truth collection method was constructed and validated through repeated use. Information concerning effective deployment of the data-collection procedure was gathered for future iterations. Over the course of procedural testing, 6 de 17 HMC students participated and collected 250 hours of data. 3 deployments of the procedure were conducted in each semester. A total of 63 students participated and collected 300 hours of data over the year. Deployment 1: 16 students, Deployment 2: 17 students, Deployment 3: 8 users, ~18 active hours, Deployment 4: 4 students, ~10 active hours, and Deployment 5: 18 students, ~40 active hours.

42 ENGINEERING↗

DOE OSTI2018

Basic Research Needs for Microelectronics

Since the invention of the integrated circuit in 1958, advances in microelectronics have followed Moore’s law and other scaling laws. These have led to over a billion-fold increase in the number of transistors on a chip. The Department of Energy’s Office of Science (DOE-SC) programs have always been at the cutting edge of microelectronics, making major contributions to the scientific understanding, materials, and advanced instrumentation that enabled innovations to promote scaling. This has driven transformative advances in microelectronics for the challenging demands of DOE’s high performance computing and science facilities. Now, strong evidence exists that scaling is approaching its physical and economic limits, and yet, the growth of data-centric computing and sensor networks is redefining computing workloads and microelectronics needs. In addition, greatly improved microelectronics are needed for the nation’s electricity grid if it is to be energy-efficient, resilient to natural phenomena and intentional attack, and agile in adapting to fluctuations in demand and power generation. Sustained and rapid progress in microelectronics science and technology from millivolt to megavolt scales is thus essential if we are to continue pushing the boundaries of science within DOE, and more significantly, to continue to lead the global information and power technology revolution. In October 2018, DOE-SC convened a workshop to address Basic Research Needs for Microelectronics by a “co-design” approach. Participants focused on scientific issues associated with advanced microelectronics technologies for applications relevant to the DOE mission, including computing, power grid management, and science facility workloads. (Topics of direct relevance to quantum information science and quantum computing were outside the scope of this workshop.) The workshop was organized around microelectronics needs for three areas: 1) future DOE-SC facilities, 2) high performance computing beyond exascale (a billion billion calculations per second), and 3) power control, conversion, and detection for a modernized electrical grid and related high power applications. Panels were formed around each of these areas, with a fourth panel formed to address Crosscutting Research. Workshop participants were asked to focus on a co-design innovation ecosystem in which materials, chemistries, devices, systems, architectures, algorithms, and software are researched and developed in a closely integrated fashion, with feedback and interdisciplinary collaboration at each interface in this microelectronics technology hierarchy. The panels identified the five Priority Research Directions on the following page.

36 MATERIALS SCIENCE↗