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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Cell fate regulation governed by a repurposed bacterial histidine kinase

One of the simplest organisms to divide asymmetrically is the bacterium Caulobacter crescentus . The DivL pseudo-histidine kinase, positioned at one cell pole, regulates cell-fate by controlling the activation of the global transcription factor CtrA via an interaction with the response regulator (RR) DivK. DivL uniquely contains a tyrosine at the histidine phosphorylation site, and can achieve these regulatory functions in vivo without kinase activity. Determination of the DivL crystal structure and biochemical analysis of wild-type and site-specific DivL mutants revealed that the DivL PAS domains regulate binding specificity for DivK~P over DivK, which is modulated by an allosteric intramolecular interaction between adjacent domains. We discovered that DivL's catalytic domains have been repurposed as a phosphospecific RR input sensor, thereby reversing the flow of information observed in conventional histidine kinase (HK)-RR systems and coupling a complex network of signaling proteins for cell-fate regulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Graphene Electric Field Sensor Enables Single Shot Label-Free Imaging of Bioelectric Potentials

The measurement of electrical activity across systems of excitable cells underlies current progress in neuroscience, cardiac pharmacology, and neurotechnology. However, bioelectricity spans orders of magnitude in intensity, space, and time, posing substantial technological challenges. The development of methods permitting network-scale recordings with high spatial resolution remains key to studies of electrogenic cells, emergent networks, and bioelectric computation. Here, for this work, we demonstrate single-shot and label-free imaging of extracellular potentials with high resolution across a wide field-of-view. The critically coupled waveguide-amplified graphene electric field (CAGE) sensor leverages the field-sensitive optical transitions in graphene to convert electric potentials into the optical regime. As a proof-of-concept, we use the CAGE sensor to detect native electrical activity from cardiac action potentials with tens-of-microns resolution, simultaneously map the propagation of these potentials at tissue-scale, and monitor their modification by pharmacological agents. This platform is robust, scalable, and compatible with existing microscopy techniques for multimodal correlative imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Development of FELIX based readout system for HV-CMOS sensor testbeam

The High Voltage CMOS (HV-CMOS) sensors are extensively investigated by the ATLAS collaboration for the High-Luminosity LHC (HL-LHC) upgrade of the Inner Tracker (ITk) detector. The HV-CMOS technology is a commercial integrated circuit process with the advantages of monolithic readout and fast charge collection. A front-end test platform of CaRIBOu (Control and Readout Itk BOard) and a testbeam Telescope based on the ATLAS IBL (Insertable B-Layer) silicon pixel modules, have been developed for the HV-CMOS sensor study and characterization in testbeam experiments. The Front-End LInk eXchange (FELIX) system is a newapproach to function as the gateway between detector front-end electronics and the commodity switched network in the ATLAS upgrade. A FELIX based readout system has been developed for both the CaRIBOu and the testbeam Telescope. It has been deployed successfully in the testbeam at theCERNSPS H8 beamline in August 2017. The test results show that the readout system is capable of sensor calibration and readout of a high-density pixel detector with high trigger rate in testbeam experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Cognition at the Point of Sensing

Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Near-field passive sensor for the monitoring of high-temperature oxidative corrosion of metals

This study reports on the development and testing of a passive wireless device designed to track temperature and corrosion behavior in SS304H stainless steel under elevated temperatures. The ANSYS HFSS software was utilized to model and optimize the design of an inductor-capacitor (LC) resonator passive wireless sensors fabricated using platinum designs printed onto an aluminum oxide support operating at frequencies between (50–190 MHz). The optimal LC wireless sensor designs were then fabricated using screen-printing and sintering methods. Here, the sensors were tested by placing the sensor onto polished SS304H flat substrates and heated to 900–1050 °C in air. Wireless acquisition of sensor data during heating, cooling, and isothermal stages was achieved through a Pt loop antenna connected to an RF signal generator and a network analyzer.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Unrolled Video Super-Resolution Network with Autoregressive Prior for the Case of Known Motion

Real-time detection and classification of distant objects is necessary for many national security applications. However, when objects are far from the sensor, they occupy only a small number of pixels in the captured video, limiting the amount of visual detail available for recognition. State-of-the-art classification methods typically rely on high-resolution (HR) video streams to capture characteristic object features, but obtaining such detail is challenging for distant objects that occupy only a few pixels. This motivates the development of video super-resolution (VSR) methods that enhance object classification by recovering fine details from low-pixel representations. Current VSR methods rely either on model-based optimization, which is interpretable but computationally expensive, or on learning-based approaches, which are efficient and high-performing but often lack flexibility and interpretability. In this report, we propose an end-to-end trainable unrolled VSR network, UVSRNet, which super-resolves each frame in a video by exploiting sub-pixel motion between neighboring low-resolution (LR) frames as well as incorporating high-frequency detail from previously super-resolved frames. In particular, by unrolling a plug-and-play (PnP) half-quadratic splitting (HQS) algorithm, we leverage a model-based data-fitting module alongside a learning-based autoregressive prior module. This combination yields a method that maintains the flexibility and interpretability of model-based methods while achieving the performance advantages of learning-based methods.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

Radiation measurements at ICOS ecosystem stations

Solar radiation is a prime driver of energy and carbon fluxes in natural ecosystems. Radiation measurements are essential for interpreting ecosystem scale greenhouse gases and energy fluxes as well as many other observations performed at ecosystem stations of the Integrated Carbon Observation System (ICOS). Here, we describe and explain the relevance of the radiation variables that are monitored continuously at ICOS ecosystem stations and define recommendations to perform these measurements with consistent and comparable accuracy. The measurement methodology and instruments are described including detailed technical specifications. Guidelines for instrumental set up as well as for operation, maintenance and data collection are defined considering both ICOS scientific objectives and practical operational constraints. For measurements of short-wave (solar) and long wave (infrared) radiation components, requirements for the ICOS network are based on available well-defined state-of-the art standards (World Meteorological Organization, International Organization for Standardization). For photosynthetically active radiation measurements, some basic instrumental requirements are based on the performance of commercially available sensors. Since site specific conditions and practical constraints at individual ICOS ecosystem stations may hamper the applicability of standard requirements, we recommend that ICOS develops mid-term coordinated actions to assess the effective level of uncertainties in radiation measurements at the network scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Light-powered end-to-end neutron detection and imaging with an edge-deployed optical AI chip

Neutron detection is widely used in many applications including nuclear physics, nuclear energy, nuclear technologies and nuclear safeguards. Developing an end-to-end neutron detection and imaging workflow paves way towards fully automated processes for many applications. We implemented an automated workflow for neutron detection experiments which use a solid state image sensor to capture neutron hits as a digital image. We deploy the workflow to an edge-based optical neural network (ONN) to increase the radiation-hardness and lifetime of neutron detection instruments. We present a two-stage neural network framework for detection of neutrons at sub-pixel resolution. The first stage uses a region proposal network to efficiently detect and extract neutron hits from the input camera image. The second stage feeds the extracted hits into a fully connected neural network to predict the sub-pixel hit position. The performance of the two-stage framework is evaluated using the edge-based ONN. The results show that we can achieve above 96% neutron detection accuracy as well as sub-pixel and sub-micron position resolution, while enjoying the advantages of the ONN hardware including radiation-hardness, low energy consumption and high computing speed for integrated edge camera and hardware deployment, when compared with electronic counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Evaluation of a catalytically aided thermal regeneration method for quartz filter-based black carbon sensing

Black carbon (BC)–a strong indicator of diesel particulate matter and other sources of incomplete carbonaceous fuel combustion–is an important air pollutant that affects public health, yet low-cost sensors capable of long-term, autonomous BC monitoring remain underdeveloped. We report on the development and evaluation of a novel BC prototype sensor that integrates soot collection on a quartz filter, in-situ optical transmission measurement, and thermal filter regeneration. To enable regeneration at lower temperatures, we evaluated the catalytic effects of various alkali metal salts pre-applied to the filter. Among these, cesium carbonate (Cs 2 CO 3 ) exhibited the strongest catalytic activity, lowering the temperature required for complete BC removal by up to 190 °C and reducing energy consumption by more than 75% compared to that required for untreated filters. The catalytic effect persisted through 10 BC collection–regeneration cycles. These findings demonstrate the potential of catalytically aided thermal regeneration in BC sensors and suggest a pathway toward energy-efficient and reduced maintenance air quality monitoring suitable for distributed BC monitoring networks.

Tang, Xiaochen [Lawrence Berkeley National Laborat↗
DOE OSTI2016

Influential Aspects of the Smart City

Using millions of sensors in everyday objects, smart cities will generate petabytes of data, and it will be delivered to multiple users via networks. Multi-disciplinary inter-operability is essential. We propose system engineering management, with multidisciplinary teams as an effective way to deliver real change. Their goal is to develop intelligent and integrated services through the use of digital technologies and open collaboration. We also caution that the process cannot be entirely planned ahead of time, it must be allowed to evolve. New technology will change the game (where does a 3-D printer fit into a smart city?). Municipal planning means central planning – not known for its sensitivity to reality. A successful smart city will include lots of feedback mechanisms for the citizenry.

collaboration↗
DOE OSTI2021

IoT Devices and Applications for Wire-Based Hybrid Manufacturing Machine Tools

Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.

Thien, Austen↗
DOE OSTI2019

Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the remote sensing techniques, a large number of RS scene images are available. Since manually labeling largescale RS scene images is both labor and time consuming, when a new unlabeled dataset is obtained, how to use the existing labeled datasets to classify the new unlabeled images is an important research direction. Different RS scene image datasets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions and image scales, so the distribution discrepancy exists among different image datasets. As a result, simply applying Convolutional Neural Networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain Adaptation (DA) can be helpful to solve this problem. In this paper, we design a Subspace Alignment (SA) and CNN based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Finetuning the modified CNN model with the added SA layer is able to make the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public datasets show that adding the SA layer into CNN can improve the scene classification on target domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Identification of NPP transients using artificial intelligence - 335

If the accidents happen in nuclear power plants (NPPs), the NPP operators identify the transients through observing the behaviors of the major parameters. However, the integrity of the instrumentation signals is not ensured under the accident circumstances and the major parameters essential in identifying the transients can rapidly change although the instruments generate accurate signals. Thus, to successfully control the accidents by taking necessary measures relatively faster, it is very important to provide the operators with the information that can identify the transients and accidents accurately. In this study, the transients of severe accidents in NPPs are identified and classified using artificial intelligence methodologies. Several transients are identified using support vector classification (SVC) and probabilistic neural network (PNN) as artificial intelligence methodologies. The used data for the transients are the simulation data using the modular accident analysis program (MAAP) code for the optimized power reactors (OPR1000s). The time-integrated values of chosen simulated sensor signals are used as the input variables for identifying the transients. As a result of this study, the proposed models identified the transients quite precisely. Therefore, the quite accurate identification of the transients indicates the excellence of these artificial intelligence methods and they will produce useful supporting information for the operators even under extreme transient circumstances in NPPs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

A robust and multifunctional calcium coordination polymer as a selective fluorescent sensor for acetone and iron (+3) and as a tunable proton conductor

A multifunctional calcium coordination polymer (Ca-CP) formulated as [(CH 3 ) 2 NH 2 ][Ca(Me 2 tcpbH)(H 2 O)] (1) has been synthesized and characterized. Compound 1 features a robust and novel three-dimensional (3D) network with cage-like cavities. The high thermal and moisture stability of the title compound makes it a good candidate for possible environment or energy related applications. Fluorescence (FL) studies demonstrate that 1 shows emission with a maximum at 350 nm under 270 nm excitation. The title compound acts as a selective and sensitive fluorescent sensor for the detection of acetone and Fe 3+ ions at low concentrations. The FL intensity quenching percentage and the concentration ratio of Fe 3+ /Fe 2+ showed a linear relation with a Fe 3+ concentration as low as 50 μM in Fe 2+ aqueous solutions, offering an easy way to probe the extent of Fe 2+ oxidation. Moreover, due to the relatively high hydration character of Ca 2+ , the terminal water molecules and undeprotonated carboxylate groups form a strong hydrogen-bonded network within the structure, facilitating its tunable proton conduction under various humidity and temperature conditions. The proton conducting mechanism has been investigated using PXRD, IR, and EA methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

COMPASS-FME Synoptic Sites Level 2 Sensor Data v2-1

This is the version 2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. Please see v2-1 L2 Sensor Package QStart.pdf for detailed information on data package structure, temporal coverage, and versioning.

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2018

Brick : Metadata schema for portable smart building applications

Buildings account for 32% of worldwide energy usage. A new regime of exciting new “applications” that span a distributed fabric of sensors, actuators and humans has emerged to improve building energy efficiency and operations management. These applications leverage the technological advances in embedded sensing, processing, networking and methods by which they can be coupled with supervisory control and data acquisition systems deployed in modern buildings and with users on mobile wireless platforms. There are, however, several technical challenges to confront before such a vision of smart building applications and cyber-physical systems can be realized. The sensory data produced by these systems need significant curation before it can be used meaningfully. This is largely a manual, cost-prohibitive task and hence such solutions rarely experience widespread adoption due to the lack of a common descriptive schema. Recent attempts have sought to address this through data standards and metadata schemata but fall short in capturing the richness of relationships required by applications. Here, this paper describes Brick, a uniform metadata schema for representing buildings that builds upon recent advances in the area. Our schema defines a concrete ontology for sensors, subsystems and the relationships between them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight unmodified energy efficiency applications on these buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Validation of Power Distribution Models using Load Flow Analysis in an ADMS Environment

Electric utilities are facing the need for better monitoring, analysis, and control of their distribution systems. An accurate mathematical model is a key to both the development of cutting-edge, scalable model-based algorithms and the assessment of emerging technologies such as distributed energy resources (DER) for grid planning and operation. However, the constantly evolving nature of power distribution systems poses challenges to maintaining accurate models. In this paper, we propose a novel load flow based approach to validate power distribution models. Networked equipment models described according to the Common Information Model (CIM) standard and a measurement model are used to formulate the distribution load flow problem. First, a system admittance matrix (Ybus) is derived from device-level CIM parameters. Next, the operational parameters (dynamic Ybus and nodal injections) are extracted from the measurement model using sensor configuration and equipment state. An iterative power flow method is then used to compute nodal voltages and branch flows that are compared against the measurement data to find any inconsistencies in the networked equipment model. This approach is implemented within GridAPPS-D, an open-source standards-based platform for advanced distribution management system (ADMS) application development, and demonstrated on the IEEE 13-bus, 123-bus, and 8500-node test feeders.

Common information model, model validation, power ↗