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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Parts Per Million CH4 Chemoresistor Sensors Based on multi wall Carbon Nanotubes/Metal-oxide Nanoparticles

This paper presents a novel atomic layer deposition (ALD) based ZnO functionalization of surface pretreated multiwalled carbon nanotubes (MWCNTs) for highly sensitive methane chemoresistive sensors. The temperature optimization of the ALD process leads to enhanced ZnO nanoparticle functionalization and improvement in their crystalline quality as shown by transmission electron microscopy. Here we propose a facile and effective top-down method for the high-yield fabrication of chemoresistor sensors making use of MWCNTs/ZnO composite. The behavior of MWCNTs/ZnO sensors in presence of methane concentrations down to 10 ppm level has been investigated. The sensor is currently being tested under a range of conditions that include potentially interfering gases and changes to relative humidity.

chemoresistor, carbon nanotubes, metal-oxide- nano↗
DOE OSTI2023

The Distributed Grid Sensing Services (DGSS) platform: A scalable and decentralized sensor dissemination network

In this paper, a scalable, decentralized sensor-oriented, communication blueprint will be introduced. The proposed architecture has been specifically designed to support the mass deployment of sensors while enabling data consumers to access the underlying data streams using a distributed systems approach. With the proposed approach, sensors and data clients can be decoupled from the physical communication infrastructure and migrated into a modern, software-defined infrastructure ecosystem that can be configured to suit the end application’s demands. In its current iteration, the blueprint defines the interactions, processes, and expected outcomes that each individual component within the architecture must fulfill in order to support the overall ecosystem’s needs. Within this work, such assemble of services is referred the databus, and represents the heart of the Distributed Grid Sensing Services (DGSS) architecture, a multi-year project that aims to design, implement and test a dedicated sensor dissemination network that can support the needs of the extended grid state. It is our expectation, that the proposed blueprint presented in this paper will serve as a future guide to our implementation efforts

Sebastian Cardenas, David J.↗
DOE OSTI2015

A Fiber Bragg Grating Temperature Sensor for 2-400 K

We demonstrate fiber optic, multiplexible temperature sensing using a fiber Bragg grating (FBG) with an operational range of 2-400 K, and a temperature resolution better than 10 mK for temperatures < 12 K. This represents a significant reduction in the lowest usable temperature as well as a significant increase in sensitivity at cryogenic temperatures compared with previously reported multiplexible solutions. This is accomplished by mounting the section of the fiber with a FBG on a polytetrafluoroethylene coupon, which has a non-negligible coefficient of thermal expansion down to < 4 K. The sensors exhibit a good stability over multiple temperature cycles and acceptable sensor-to-sensor repeatability. Possible applications for this sensor include distributed temperature sensing across superconducting elements and cryogenic temperature measurements in environments where electrical measurements are impractical or unsafe.

Zaynetdinov, Madrakhim↗
DOE OSTI2021

High-Voltage CMOS Active Pixel Sensor

The high-voltage CMOS (HVCMOS) sensors are a novel type of CMOS active pixel sensors for ionizing particles that can be implemented in CMOS processes with deep n-well option. The pixel contains one sensor electrode formed with a deep n-well implanted in a p-type substrate. CMOS pixel electronics, embedded in shallow wells, are placed inside the deep n-well. By biasing the substrate with a high negative voltage and by the use of a lowly doped substrate, a depleted region depth of at least 30 µm can be achieved. The electrons generated by a particle are collected by drift, which induces fast detectable signals. This publication presents a 4.2-cm 2 large HVCMOS pixel sensor implemented in a commercial 180-nm process on a lowly doped substrate and its characterization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗
DOE OSTI2023

EmSense: A High-Resolution Emulated Sensor for Experiments with the Smart Grid and Distributed Ledger Technology

This work involves the development of a device - EmSense (“Emulated Sensor”) - that emulates a high-resolution sensor for a power grid. The device collects raw current and voltage sensor data which derive from ORNL's signature library. This library is a dataset that ORNL curates from many different sources that include power systems from various utilities. The EmSense packages the data from the library in the form of IEC 61850 Sampled Value (SV) packets and then broadcasts these SV packets on the network. In another mode, EmSense can generate artificial sinusoidal data that appears as waveforms for voltage and current signals. EmSense has an internal algorithm for determining the period of a signal based on the data so that the period can be specified as a variable in the IEC 61850 packets. The purpose of EmSense is to allow for experimentation with the Dark Net Infrastructure where a variety of power line sensors must be represented along with their typical communication traffic. The EmSense device was developed in coordination with the software for receiving and processing the packets in the Distributed Ledger Technology (DLT) framework of the DarkNet Project. This receiving software must have a methodology for dealing with information of high velocity, variety, and volume. Experimenting with EmSense facilitates the development of such software. The results showed that the DLT framework and the trust-anchoring approach managed to process a large flow of traffic even with up to six instances of EmSense device broadcasting data. This was achieved without overfilling packet queues in the memory of the actual hardware of the DLT devices or causing the Central Processing Unit (CPU) of the hardware to be overwhelmed. The DLTs were also able to store the data in a compact and useful form for later analysis and archival purposes.

Werth, Aaron↗
DOE OSTI2022

Comparison of Power System Current Sensors via Playback of Electrical Disturbances

The need to accurately measure high-frequency content in power system voltage and current phenomena is increasingly becoming more of a priority. As the amount of distributed energy resources (DER) and nonlinear loads penetrating the grid increases, so do challenges associated with traditional measurement and metering applications. In this paper, three commercially-available medium-voltage-level current sensors are characterized in terms of their harmonic amplitude and phase performance against reference signals that are “played back” through the sensors through the use of an arbitrary waveform generator. It is shown that none of the three sensors studied are able to faithfully replicate all of the input signals completely, though there are advantages and disadvantages to each in terms of noise, resonance, and induced phase drift. Additionally, the Goodness-of-Fit metric, typically used for PMU model validation, is used to generate side-by-side comparisons of sensor accuracy over a small window around the events under study.

Wilson, Aaron↗
DOE OSTI2016

Effect of retransmission and retrodiction on estimation and fusion in long-haul sensor networks

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this work, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can also degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Besides, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Lastly, simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Asymmetric Dual Sensor Latency in Distributed Control

Considerable geographical separation between various phasor measurement unit sensor suites used in the wide-area damping control architecture results in time delays with varying steady-state and stochastic components. The consequences of asymmetric latent sensors used in a standard feedback configuration are presented, indicating that asymmetry of the delay provides not only a previously suspected modification to the argument of the loop transmission function, but also a potentially substantial impact on its modulus. Under particular delay configurations, the controller may experience the introduction of varying crossovers in the loop transmission, resulting in a shifting of control bandwidth and previously unconsidered (in)stability characteristics (e.g., alteration of relative stability margins). With consideration to the theoretical results provided, the optimal sensor location based on delay characteristics is provided. Finally, results of the study indicate that it is most frequently best practice to act on the most current sample in the feedback channel, rather than wait for precise time alignment of the delayed sensor.

42 ENGINEERING↗
DOE OSTI2019

Electrochemical Sensors for Air Quality Monitoring

We report the continuous growth of modern industries has left the world with poor air quality especially in urban areas and work place environments, posing a major challenge to quality of life. The major contributors to traditional air pollution are NOx, SOx, and H2S while NH3 and Volatile organic compounds are of increasing concern recently. Traditional air quality monitors based on mass spec, infra-red spectroscopy and gas chromatography are expensive and not suitable for large scale deployment. Electrochemical gas sensors provide a cheap alternative option for widespread air quality monitoring. While many electrochemical sensors detect toxic gases in the range of 1 – 10,000 ppm, recent advances in chemiresistive and mixed potential electrochemical sensors have brought down the detection range to parts per billion, required range for environmental monitoring. In this Interface article, we discuss recent advancements in sensors for the efficient detection of key air pollutant gases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Sensor Data Analytics and Data Quality Assessment Software

The proposed framework derives a set of quality metrics to provide critical insights into and tracking of grid operations, sensor performance, sensor longevity, and event statistics. Power grid engineers can utilize this information to identify problems with existing sensor locations and problematic power grid assets including generators, transmission lines, load centers, and substations. This information can also be used to identify unexpected/abnormal behavior of power grid components, improve power grid observability, and operational monitoring, and thus enhance real-time decision-making support system. Power grid planners can utilize this information to augment existing sensing architecture with new sensors and improve the observability of the network.

Mahapatra, Kaveri↗
DOE OSTI2022

Plasmonic sensors based on graphene and graphene hybrid materials

Abstract The past decade has witnessed a rapid growth of graphene plasmonics and their applications in different fields. Compared with conventional plasmonic materials, graphene enables highly confined plasmons with much longer lifetimes. Moreover, graphene plasmons work in an extended wavelength range, i.e., mid-infrared and terahertz regime, overlapping with the fingerprints of most organic and biomolecules, and have broadened their applications towards plasmonic biological and chemical sensors. In this review, we discuss intrinsic plasmonic properties of graphene and strategies both for tuning graphene plasmons as well as achieving higher performance by integrating graphene with plasmonic nanostructures. Next, we survey applications of graphene and graphene-hybrid materials in biosensors, chemical sensors, optical sensors, and sensors in other fields. Lastly, we conclude this review by providing a brief outlook and challenges of the field. Through this review, we aim to provide an overall picture of graphene plasmonic sensing and to suggest future trends of development of graphene plasmonics.

Zhang, Zhichao↗
DOE OSTI2025

Designing an Optimal Sensor Network via Minimizing Information Loss

Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Technical Challenges and Gaps in Integration of Advanced Sensors, Instrumentation, and Communication Technologies with Digital Twins for Nuclear Application

This paper explores integrating advanced sensor, instrumentation, and communication technologies with digital twin technologies for nuclear energy application. Digital twins and digital-twin-enabling technologies are expected to integrate with future nuclear reactor designs and have the potential to impact currently operating nuclear power plants. Greater digital integration, advanced instrumentation and control systems, and advanced operations and maintenance practices are all associated with digital-twin-enabling technologies. Advanced sensors, instrumentation, and communication technology are expected to comprise important elements of the infrastructure required to develop and operate a nuclear digital twin system. This paper identifies and discusses challenges and gaps in developing and implementing advanced sensors and instrumentation and communication technology to be integrated with a digital twin in current and advanced reactor applications. It is important to address some challenge and gap to enable a successful near-term deploying advanced sensors, instrumentation, and communication technologies integrated with digital twins.

Yadav, Vaibhav↗
DOE OSTI2020

Multiplexable high-temperature stable and low-loss intrinsic Fabry-Perot in-fiber sensors through nanograting engineering

This paper presents a method of using femtosecond laser inscribed nanograting as low-loss– and high-temperature–stable in-fiber reflectors. By introducing a pair of nanograting inside the core of a single-mode optical fiber, an intrinsic Fabry-Perot interferometer can be created for high-temperature sensing applications. The morphology of the nanograting inscribed in fiber cores was engineered by tuning the fabrication conditions to achieve a high fringe visibility of 0.49 and low insertion loss of 0.002 dB per sensor. Using a white light interferometry demodulation algorithm, we demonstrate the temperature sensitivity, cross-talk, and spatial multiplexability of sensor arrays. Both the sensor performance and stability were studied from room temperature to 1000°C with cyclic heating and cooling. Our results demonstrate a femtosecond direct laser writing technique capable of producing highly multiplexable in-fiber intrinsic Fabry-Perot interferometer sensor devices with high fringe contrast, high sensitivity, and low-loss for application in harsh environmental conditions.

Wang, Mohan (ORCID:0000000166784993)↗
DOE OSTI2022

Ultra-compact hybrid silicon:chalcogenide waveguide temperature sensor

We demonstrate a real-time, reusable, and reversible integrated optical sensor for temperature monitoring within harsh environments. The sensor architecture combines the phase change property of chalcogenide glasses (ChG) with the high-density integration advantages of high index silicon waveguides. To demonstrate sensor feasibility, ChG composition Ge 40 S 60 , which is characterized by a sharp phase transition from amorphous to crystalline phase around 415 °C, is deposited over a 50 µ m section of a single mode optical waveguide. The phase transition changes the behavior of Ge 40 S 60 from a low loss to high loss material, thus significantly affecting the hybrid waveguide loss around the phase transition temperature. A transmission power drop of over 40dB in the crystalline phase compared to the amorphous phase is experimentally measured. Moreover, we recover the amorphous phase through the application of an electrical pulse, thus showing the reversible nature of our compact temperature sensor. Through integrating multiple compositions of ChG with well-defined phases transition temperatures over a silicon waveguide array, it is possible to determine, in real-time, the temperature evolution within a harsh environment, such as within a nuclear reactor cladding.

Badamchi, Bahareh (ORCID:0000000193105300)↗
DOE OSTI2023

Spatial Study 2022: Surface Water Samples, Cotton Strip Degradation, and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides data and photos generated from sample collection during the same one-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. The contents include surface water geochemistry data; river substrate grain size photos; stream depth data; manual chamber open channel respiration data; and field metadata (including qualitative information on instream and river corridor characteristics). Grain size photos can be used to improve estimates of channel substrate D50 data. The dataset also includes tensile strength and photos from cotton strip field degradation experiments; five-week sensor time series temperature, dissolved oxygen, pressure, pH, specific conductance, chlorophyll A, and turbidity data; plots of the sensor data; and R scripts used to generate the plots. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. SSS data published in the CM data package were not included in this data package. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Additionally, sensor data from a similar 2021 spatial study can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052 and 2021 sample data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1898914. The 2021 spatial study had some sites in common with this 2022 spatial study. This dataset is comprised of three photo folders and one main data folder with six subfolders. The photo folders contain photographs and videos of cotton strip retrieval and sediment quadrats. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) total suspended solids (TSS) data and cotton strip tensile strength data and averages; (5) field protocol; (6) readme; (7) methods codes; (8) international generic sample number (IGSN) mapping file; (9) sensor installation methods summary; (10) stream depth and averages; and (11) Ultrameter data and averages. The Sonar subfolder consists of Sonar time-series depth data and a processing script. The BarotrollAtm, DepthHOBO, MantaRiver, miniDOT, and miniDOTManualChamber subfolders contain time-series data, plots, and summary files. All files are .csv, .pdf, .txt, .R, .Rmd, .jpg, .jpeg, .AVI, .mp4, or .mov. The data package was originally published in April 2023. It was updated in August 2023 (v2; modified files) and September 2024 (v3; modified files). See the change history section in the readme for details. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗