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At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Profiling at Oliktok Point to Enhance Year of Polar Prediction (YOPP) Experiments (POPEYE) Field Campaign Report

The arctic region is rapidly evolving, and enhanced predictive capabilities, for both weather and climate, are urgently required. Therefore, the international community is executing an extended period of focused observations and modeling of the arctic environment, dubbed the Year of Polar Prediction or YOPP. The YOPP featured two special observing periods (SOPs) (and will feature a third in 2020). The first of the completed SOPs occurred in the spring of 2018, and the second during the late summer and early fall of 2018. Here, we summarize the deployment of additional U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility resources to the third ARM Mobile Facility (AMF3) at Oliktok Point, Alaska during the second three-month SOP (1 July 2018–30 September 2018). This deployment, named POPEYE (Profiling at Oliktok Point to Enhance YOPP Experiments), included the launching of extra radiosondes (one more per day) and operation of ARM-operated unmanned aircraft (DataHawk 2s) and ARM-operated tethered balloons. These instruments conducted routine profiling activities over the course of the special observing period to obtain measurements of atmospheric thermodynamic structure, cloud and precipitation properties, and aerosol properties. These measurements are expected to be used for a variety of purposes, including: 1) to conduct detailed studies of arctic cloud and aerosol processes; 2) to inform YOPP modeling efforts through real-time availability for assimilation into operational and research analysis products; 3) to evaluate and improve retrieval algorithms involving ARM remote sensors; 4) to evaluate and improve a variety of modeling tools being used to forecast arctic weather and climate; and 5) to initialize and evaluate simulations associated with a potential arctic large-eddy (LES) simulation framework similar to the ongoing LES ARM Symbiotic Simulation and Observation (LASSO) project.

Oliktok Point, Arctic processes, unmanned aerial v↗
NASA NTRS2022

Probing Dark Current Random Telegraph Signal in a Small Pitch Vertically Pinned Photodiode CMOS Image Sensor after Proton Irradiation

Dark Current degradation and Dark Current Ran- dom Telegraph Signal after proton irradiation are studied in new scale silicon microvolumes by using a commercial CMOS Image Sensor. Results show that previously reported empirical models describing the Displacement Damage induced degradations are still valid despite the 10 to 100 times smaller depletion volume used. In addition, no evidence of significant Total Ionizing Dose effects is observed. Finally the reduction of the fraction of Random Telegraph Signal (RTS) pixels detected and of the fraction of multi-level RTS pixels is directly linked to the reduction in pixel volume.

CMOS Image Sensor (CIS)↗
DOE OSTI2023

Characterizing Current THD’s Dependency on Solar Irradiance and Supraharmonics Profiling for a Grid-Tied Photovoltaic Power Plant

The rapidly increasing distributed energy resources (DERs) in power systems are now getting interconnected to set community grid structures, where power quality will be a major concern. The grid-to-grid (G2G) bidirectional power transfer among the distribution microgrid will not be considered commercially feasible unless the upstream harmonics are under the limits. The aggregation of such harmonics, measured as total harmonic distortion (THD), is feared to be beyond tolerable limits with the progression of rooftop grid-tied PV-like installations. Hence, this THD needs to be characterized with DER generation end variables. In this work, the photovoltaic (PV) DERs’ dependency on environment variables such as irradiance was profiled in the context of generating and injecting harmonics into the grid. A mathematical model of a grid-tied three-phase PV DER was developed as part of this correlation characterization, matching the fundamental unit structure of a 1.4 MW solar canopy located on the Florida International University (FIU) Miami campus. To determine the qualitative association with produced THD patterns, the model was evaluated with various irradiance settings. A real-time digital simulation (RTDS) platform was used to verify it. Following this confirmation, sets of data from power quality meters at the point of common coupling and FIU field sensors were utilized to validate further the correlation model. The results showed that the grid current’s THD exhibited a high correlation with the irradiance profile and its variation over time. The early morning and late afternoon periods of the day, associated with a low irradiance, constantly had higher harmonics generated from the PV DER. The midday THD was rather rational with partial shadings, hence a geolocation-dependent factor. These findings were verified by an RTDS and validated by real field data. In quantifying the THD injected by a single DER at a high-frequency (2–150 kHz) supraharmonics (SH) level, a 3% peak increment in magnitude was observed from the high-fixed to the low-fixed irradiance profile. The correlation characteristics depicted that the hybrid microgrid suffered from a daytime-dependent harmonic insertion from the grid-tied DER. This is a global problem unless specific measures are taken to mitigate the harmonics. The electrically notorious higher-frequency SH was found to increase proportionally. The G2G power transfer can be limited because of the higher THD in the early morning and late afternoon, which will also worsen because the numbers of grid-tied PV DERs (i.e., rooftop solar and industrial solar) are likely to increase rapidly soon. The community grid structure can thus have a controlled harmonics filtration setup purposefully designed to address the findings of this work, which also fall within the scope of our future research.

14 SOLAR ENERGY↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
NASA NTRS1991

Current status and future developments in radar remote sensing

Some of the major initiatives and directions of remote sensing using SAR (Synthetic Aperture Radar) data alone and in conjunction with other sensors for Earth science investigations are outlined. Specific emphasis is on areas key to global monitoring using SAR data from spaceborne platforms: calibration, geophysical processing, and generation of digital elevation models. Calibration as used here encompasses end to end system characterization over the life of a sensor and characterization of data products relative to past and future sensors. Geophysical processing is defined here to include any processing which results in derived geophysical units. An additional data type, topography, which is required to complete the three dimensional view of surface properties and correct for distortions inherent in SAR is discussed. Future challenges in radar remote sensing include development of strategies to extrapolate from regional to global scale models and development of new sensor technology.

Evans, Diane L.↗
NASA NTRS1992

The direct effects of gravity on the control and output matrices of controlled structure models

The effects of gravity on the dynamic performance of structural control actuators and sensors are dual forms of an additive perturbation that can attenuate or amplify the device response (input or output). The modal modeling of these perturbations is derived for the general case of arbitrarily oriented devices and arbitrarily oriented planes of deformation. A nondimensional sensitivity analysis to identify the circumstances under which the effects of gravity are important is presented. Results show that gravity effects become important when the product of the ratio of the normalized modal slope and the modal displacement is comparable to the ratio of the gravitational acceleration and the product of the beam length and the squared eigenfrequency for a given mode.

Rey, Daniel A.↗
DOE OSTI2022

Quantification of gas concentrations in NO/NO 2 /C 3 H 8 /NH 3 mixtures using machine learning

We employ machine learning to decode the composition of unknown gas mixtures from the output of an array of four electrochemical sensors. The sensors use metal oxide electrodes paired with a ceramic electrolyte, yttria-stabilized zirconia (YSZ), to produce voltage responses to the presence of gases in complex mixtures. The voltages from the sensor array serve as inputs to a machine learning pipeline which first carries out multi-class classification of mixtures into types based on which gases are present at non-zero concentrations, and subsequently predicts gas concentrations given the mixture type. Thus, our model is able to take a single reading from the sensor array in response to gas mixtures involving NO, NO 2 , C 3 H 8 , and NH 3 , and output a highly accurate prediction of which gases are present in the mixture, along with the concentrations of each constituent gas. Of note, our computational framework can be easily expanded to include additional gases and additional mixture types, allowing it to be used in numerous automotive, industrial and environmental monitoring settings.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Global communication and control

A process detects media dispersion. The process detects the dispersion of contaminants through distributed remote sensor platforms that connect one or more sensors on a remote device. The process transmits detection data from the distributed remote sensor platform to a radiation tolerant satellite router. A gateway connects the radiation tolerant satellite router to a hardware server and converts the detection data to a compatible form with a protocol used by a hardware server. The process generates a plume model in response to the detection data and meteorological data that models dispersion plumes and activates and deactivates selected sensors in response to a forecasted to dispersion area.

Harter, Andrew↗
DOE OSTI2014

Robustness analysis of an air heating plant and control law by using polynomial chaos

This paper presents a robustness analysis of an air heating plant with a multivariable closed-loop control law by using the polynomial chaos methodology (MPC). The plant consists of a PVC tube with a fan in the air input (that forces the air through the tube) and a mass flux sensor in the output. A heating resistance warms the air as it flows inside the tube, and a thermo-couple sensor measures the air temperature. The plant has thus two inputs (the fan's rotation intensity and heat generated by the resistance, both measured in percent of the maximum value) and two outputs (air temperature and air mass flux, also in percent of the maximal value). The mathematical model is obtained by System Identification techniques. The mass flux sensor, which is nonlinear, is linearized and the delays in the transfer functions are properly approximated by non-minimum phase transfer functions. The resulting model is transformed to a state-space model, which is used for control design purposes. The multivariable robust control design techniques used is the LQG/LTR, and the controllers are validated in simulation software and in the real plant. Finally, the MPC is applied by considering some of the system's parameters as random variables (one at a time, and the system's stochastic differential equations are solved by expanding the solution (a stochastic process) in an orthogonal basis of polynomial functions of the basic random variables. This method transforms the stochastic equations in a set of deterministic differential equations, which can be solved by traditional numerical methods (That is the MPC). Statistical data for the system (like expected values and variances) are then calculated. The effects of randomness in the parameters are evaluated in the open-loop and closed-loop pole's positions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Localized real-time information on outdoor air quality at kindergartens in Oslo, Norway using low-cost sensor nodes

Highlights: • We show that low-cost sensors can provide an indication of outdoor air quality. • We employed a data fusion method to provide real-time air quality maps. • Localized real-time air quality information can help parents and kindergarten staff. In Norway, children in kindergartens spend significant time outdoors under all weather conditions, and there is thus a natural concern about the quality of outdoor air. It is well known that air pollution is associated with a wide variety of adverse health impacts for children, with greater impact on children with asthma. Especially during winter and spring, kindergartens in Oslo that are situated close to streets with busy traffic, or in areas where wood burning is used for house heating, can experience many days with bad air quality. During these periods, updated information on air quality levels can help the kindergarten teachers to plan appropriate outdoor activities and thus protect children's health. We have installed 17 low-cost air quality nodes in kindergartens in Oslo. These nodes are smaller, cheaper and less complex to use than traditional equipment. Performance evaluation shows that while they are less accurate and suffer from higher uncertainty than reference equipment, they still can provide reliable coarse information about local pollution. The main challenge when using this technology is that calibration parameters might change with time depending on the atmospheric conditions. Thus, even if the sensors are calibrated a priori, once deployed, and especially if they are deployed for a long time, it is not possible to determine if a node is over- or under-estimating the concentration levels. To enhance the data from the sensors, we employed a data fusion technique that allows generating a detailed air quality map merging the data from the sensors and the data from an urban model, thus being able to offer air quality information to any location within Oslo. We arranged a focus group with the participation of local administration, kindergarten staff and parents to understand their opinion and needs related to the air quality information that was provided to the participant kindergartens. They expressed concern about the data quality but agree that having updated information on the air quality in the surroundings of kindergartens can help them to reduce children's exposure to air pollution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Transient multivariable sensor evaluation

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Temperature dependent dual hydrogen sensor response of Pd nanoparticle decorated Al doped ZnO surfaces

Sputter deposited Al doped ZnO (AZO) thin films exhibit a dual hydrogen sensing response in the temperature range 40 °C–150 °C after surface modifications with palladium nanoparticles. The unmodified AZO films showed no response in hydrogen in the temperature range 40 °C–150 °C. The operational temperature windows on the low and high temperature sides have been estimated by isolating the semiconductor-to-metal transition temperature zone of the sensor device. The gas response pattern was modeled by considering various adsorption isotherms, which revealed the dominance of heterogeneous adsorption characteristics. The Arrhenius adsorption barrier showed dual variation with change in hydrogen gas concentration on either side of the semiconductor-to-metal transition. A detailed analysis of the hydrogen gas response pattern by considering the changes in nano palladium due to hydrogen adsorption, and semiconductor-to-metal transition of nanocrystalline Al doped ZnO layer due to temperature, along with material characterization studies by glancing incidence X-ray diffraction, atomic force microscopy, and transmission electron microscopy, are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Overview of North American Hydrogen Sensor Standards

An overview of the main North American codes and standards associated with hydrogen safety sensors is provided. The distinction between a code and a standard is defined, and the relationship between standards and codes is clarified, especially for those circumstances where a standard or a certification requirement is explicitly referenced within a code. The report identifies three main types of standards commonly applied to hydrogen sensors (interface and controls standards, shock and hazard standards, and performance-based standards). The certification process and a list and description of the main standards and model codes associated with the use of hydrogen safety sensors in hydrogen infrastructure are presented.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS2017

Hyperspectral Radiative Transfer Modeling to Explore the Combined Retrieval of Biophysical Parameters and Canopy Fluorescence from FLEX - Sentinel-3 Tandem Mission Multi-Sensor Data

The FLuorescence EXplorer (FLEX) satellite mission, selected as ESA's 8th Earth Explorer, has been designed forthe measurement of sun-induced fluorescence (F) spectra emitted by plants. This will be accomplished through amulti-sensor approach by placing it in a common orbit in tandem with the Sentinel-3 (S3) mission, which willhave two optical sensors on board, OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land SurfaceTemperature Radiometer) to complement FLEX. These S3 instruments will be used in combination with theimaging spectrometers on board FLEX to provide data useful for atmospheric correction of FLEX data. However,a fully synergetic approach, i.e. by exploiting the spectral and directional information from all tandem missioninstruments together, is an attractive alternative which is explored in this paper. By employing all combined topof-atmosphere (TOA) spectral radiance data, one can (i) characterize the relevant optical properties of the atmosphere,(ii) retrieve biophysical canopy properties including the associated reflectance anisotropy, and (iii)retrieve a more accurate and consistent canopy F.Regarding retrieval methods, Fraunhofer Line Depth (FLD) and Spectral Fitting (SF) are well-known techniquesapplied to hyperspectral data. Both methods depend on a high spectral resolution and assume aLambertian (isotropic) canopy reflectance. However, most vegetation canopies are non-Lambertian. This impliesthat, in particular when ignoring the anisotropic surface reflection, substantial retrieval errors can occur due tothe interaction between atmospheric absorption bands and surface reflectance anisotropy. In this paper, a novelmethod based on spectral radiative transfer (RT) modeling is proposed, in which coupled RT models are used tosimulate TOA radiance spectra. These are then matched with ‘measured' spectra in order to retrieve surfacefluorescence, along with a suite of biophysical parameters, by model inversion through optimization. By applyingcoupled RT models of the soil-leaf-canopy and the surface-atmosphere systems, TOA radiance spectra canbe simulated for all optical sensors of this tandem mission. In this way, complex effects due to surface reflectanceanisotropy and the spectral sampling by the various instruments, which are difficult to compensate for in the endproducts, are properly taken into account by their incorporation in the forward modeling. Next, by model inversionof TOA radiance data via optimization, the most accurate F retrievals can be achieved in a consistentmanner, along with important canopy level biophysical parameters that may help interpret the F spectrum, suchas chlorophyll content and leaf area index (LAI). The potential of this approach has been explored in a numericalexperiment, and the results are presented in this paper. We find that, with the assumed well-characterized andplausible FLEX/S3 instrument performances, the simultaneous retrieval of biophysical canopy parameters and Fspectra would be possible with a remarkable accuracy, provided the correct atmospheric characterization isavailable.

Verhoef, Wouter↗
DOE OSTI2020

Global evidence for the acclimation of ecosystem photosynthesis to light

Photosynthesis responds quickly to changes in light, increasing with incoming photosynthetic photon flux density (PPFD) until the leaves become light saturated. This instantaneous response to PPFD, which is widely studied and incorporated into models of photosynthesis, is overlaid on non-instantaneous photosynthetic changes resulting from the acclimation of plants to average PPFD over intermediate timescales of a week to months ($\overline{PPFD}$). Such photosynthetic light acclimation is not typically incorporated into models, due to the lack of observational constraints. In this work, we use eddy covariance observations from globally distributed and automated sensor networks, along with photosynthesis estimates from nine terrestrial biosphere models (TBMs), to quantify and assess photosynthetic acclimation to light in natural environments. We also use recent theoretical developments to incorporate light acclimation in a TBM. Our results show widespread light acclimation of ecosystem photosynthesis. On average, a 1 μmol m -2 s -1 increase in $\overline{PPFD}_{10}$ (ten-day average PPFD) leads to a 0.031 ± 0.013 μmol C m -2 s -1 increase in the maximum photosynthetic assimilation rate (A max ), with croplands having stronger acclimation rates than grasslands and forests. Our analysis shows that the TBMs examined either neglect or substantially underestimate light acclimation. By updating a TBM to include photosynthetic acclimation, successfully reproducing the $\overline{PPFD}_{10}$-A max relationship, we provide a robust method for the incorporation of photosynthetic light acclimation in future models.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Comprehensive Understanding for Vegetated Scene Radiance Relationships

Directional reflectance distributions spanning the entire existent hemisphere were measured in two field studies; one using a Mark III 3-band radiometer and one using the rapid scanning bidirectional field instrument called PARABOLA. Surfaces measured included corn, soybeans, bare soils, grass lawn, orchard grass, alfalfa, cotton row crops, plowed field, annual grassland, stipa grass, hard wheat, salt plain shrubland, and irrigated wheat. Analysis of field data showed unique reflectance distributions ranging from bare soil to complete vegetation canopies. Physical mechanisms causing these trends were proposed. A 3-D model was developed and is unique in that it predicts: (1) the directional spectral reflectance factors as a function of the sensor's azimuth and zenith angles and the sensor's position above the canopy; (2) the spectral absorption as a function of location within the scene; and (3) the directional spectral radiance as a function of the sensor's location within the scene. Initial verification of the model as applied to a soybean row crop showed that the simulated directional data corresponded relatively well in gross trends to the measured data. The model was expanded to include the anisotropic scattering properties of leaves as a function of the leaf orientation distribution in both the zenith and azimuth angle modes.

Kimes, D. S.↗