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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High temporal resolution estimates of Arctic snowfall rates emphasizing gauge and radar-based retrievals from the MOSAiC expedition

This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Projection-Based Constrained Fusion Performance with Link Loss and Measurement Bias

Sensor networks have been deployed in many real-world applications for tracking and monitoring of dynamic targets over large geographical areas. We consider a scenario where a number of ground and/or airborne sensors are used to jointly track a ground target with certain constraints on its motion dynamics, such as those defined by roadways. The state estimates from these sensors are sent over a lossy communications network to a remote fusion center for fusion. We propose both centralized and distributed projection-based methods to incorporate known constraints into the estimation and fusion process. In addition to presenting simple closed-form projected fusion solutions, we also show how to utilize knowledge of the constraints into the fusion process by two distance-based weighting methods. Wealso explore the effect of link loss and sensor measurement bias on tracking accuracy performance of constrained fusion using illustrative tracking examples. Performance evaluation results demonstrate the potential of adopting weighted average fusers in improving the overall tracking performance in the presenceof link loss and measurement bias.

Liu, Qiang↗
DOE OSTI2026

High-resolution in-beam video rate imaging with the ClearXCam for synchrotron beamlines

The ClearXCam detector is a new video rate imaging in-beam monitor based on a diamond sensor. Imaging with 2304 effective pixels is achieved by sequentially biasing one of 48 metal stripes on one side of a diamond sensor, while reading out the current from 48 stripes on the other side of the sensor. This system was characterized for real-time X-ray beam diagnostics at synchrotron beam­line 17-BM at the National Synchrotron Light Source II. Significant results include: detection of in-beam structure via the imaging mode, beam focusing in one minute with real-time imaging feedback during a focusing event, linearity over five orders of magnitude, validation of a fast mode operating at 100 Hz, and sub-micron beam-positioning resolution. The system is now available commercially.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Consequences of CCD imperfections for cosmology determined by weak lensing surveys: from laboratory measurements to cosmological parameter bias

Weak gravitational lensing causes subtle changes in the apparent shapes of galaxies due to the bending of light by the gravity of foreground masses. By measuring the shapes of large numbers of galaxies (millions in recent surveys, up to tens of billions in future surveys) we can infer the parameters that determine cosmology. Imperfections in the detectors used to record images of the sky can introduce changes in the apparent shapes of galaxies, which in turn can bias the inferred cosmological parameters. In this paper we consider the effect of two widely discussed sensor imperfections: tree rings, due to impurity gradients that cause transverse electric fields in the charge-coupled devices (CCDs), and pixel size variation, due to periodic CCD fabrication errors. These imperfections can be observed when the detectors are subject to uniform illumination (flat-field images). We develop methods to determine the spurious shear and convergence (due to the imperfections) from the flat-field images. We calculate how the spurious shear when added to the lensing shear will bias the determination of cosmological parameters. We apply our methods to candidate sensors of the Large Synoptic Survey Telescope (LSST) as a timely and important example, analyzing flat-field images recorded with LSST prototype CCDs in the laboratory. We find that tree rings and periodic pixel size variation present in the LSST CCDs will introduce negligible bias to cosmological parameters determined from the lensing power spectrum, specifically w, Ω{sub m}, and σ{sub 8}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Can We Rely on Satellite Visible/Infrared Microphysical Retrievals of Boundary Layer Clouds in Partially Cloudy Scenes? Implications for Climate Research

This study addresses the longstanding question of the reliability of gridded visible/infrared satellite cloud properties in partially cloudy scenes. By using in-situ cloud probes and airborne Research Scanning Polarimeter (RSP) observations, we analyze bias changes in satellite retrievals from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) geostationary sensor during the ORACLES campaign. Biases in cloud optical depth (τ) and droplet effective radius (r e ) modestly change for cloud area fraction greater than 35%. The agreement between SEVIRI and RSP r e substantially improves when the retrievals are averaged after removing pixels with τ < 3.0, yielding biases indistinguishable from overcast scenes. In addition, satellite and RSP show an excellent agreement for closed- and open-cell stratocumulus clouds, showing that the satellite retrievals capture spatial changes of r e , and confirming that satellites can faithfully reproduce real physical features for optically thick and partially cloudy scenes. We demonstrate that a simple methodology can minimize uncertainties in satellite-based climate studies.

Painemal, David [NASA Langley Research Center, Ham↗
DOE OSTI2016

Measurement of deposition rate and ion energy distribution in a pulsed dc magnetron sputtering system using a retarding field analyzer with embedded quartz crystal microbalance

A compact retarding field analyzer with embedded quartz crystal microbalance has been developed to measure deposition rate, ionized flux fraction, and ion energy distribution arriving at the substrate location. The sensor can be placed on grounded, electrically floating, or radio frequency (rf) biased electrodes. A calibration method is presented to compensate for temperature effects in the quartz crystal. The metal deposition rate, metal ionization fraction, and energy distribution of the ions arriving at the substrate location are investigated in an asymmetric bipolar pulsed dc magnetron sputtering reactor under grounded, floating, and rf biased conditions. The diagnostic presented in this research work does not suffer from complications caused by water cooling arrangements to maintain constant temperature and is an attractive technique for characterizing a thin film deposition system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

A multi-prong approach towards the development of high performance Temperature sensor using MWCNTs/Al2O3 composite film

Highlights: • MWCNTs/Al{sub 2}O{sub 3} composite based self-standing highly sensitive temperature sensor. • Optimization of film thickness to achieve best performance of the device. • Effect of carrier drift under strong electron-phonon coupling is examined. • Non-contact measurements reveal possibilities of ambient environment monitoring. • Highly stable and reproducible response bidding commercial scale production. - Abstract: We report fabrication of high-performance temperature sensor from free-standing MWCNTs/Al{sub 2}O{sub 3} composite film where a multi-prong approach was undertaken to enhance sensor performance. Film thickness was varied until a threshold value was reached by playing surface-area to volume-ratio factor leading to enormous improvements in crucial sensor parameters–temperature coefficient of resistance (TCR) and thermal hysteresis. Increased TCR resulted in high sensitivity, ultrafast response and negligible hysteresis loss. Besides, effect of carrier-drift transport under strong electron-phonon scattering is investigated by permutation of channel-length and external DC bias. Observed TCR value is −0.96%/°C for 16.53 μm thick film, at 2 V applied-bias voltage for 1 mm channel-length. Moreover, the sensor exhibited negligible hysteresis loss along-with response and recovery times of ∼40 s and ∼185 s respectively. Non-contact mode temperature measurements also demonstrated excellent performance. Fabricated sensors exhibited good stability and negligible drift for six-months. These studies are significant towards fabricating simple, highly-sensitive, economic heat sensor with high reproducibility.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Preliminary benchmarks and analysis of boundary conditions in a trenched microstructured silicon radiation detector

Microstructured neutron detectors have the benefit of enhanced neutron detection efficiency as compared to planar devices, achieved by etching 6 LiF-filled trenches on the top surface of a silicon PIN diode. This sensor geometry results in a complex electric field distribution and depletion characteristics within the diode under reverse bias. For the first time on record, the effects of a fixed oxide charge on the microstructured device depletion characteristics and mobile carrier transport is investigated. Prototype detectors were fabricated with non-conformal surface doping. Capacitance voltage and current voltage measurements were performed for these prototypes and compared with COMSOL Multiphysics simulations. A spectral response from an 241Am alpha particle source was acquired and analyzed. It was found that monoenergetic alpha particles produce three prominent peaks in the pulse height spectrum output by the device. The peaks were confirmed by simulations to correlate with dead layers and incident trajectories into the microstructure. It was also found that significant differences in pulse rise time result, corresponding with events arriving in a low-field region in the fins and a high-field region in the bulk. Geant4 was utilized for radiation transport, interaction modeling, and benchmarking the spectral data. The results of this simulation work provide confidence in the ability to attain and benchmark electrical characteristics and spectral data for semiconductor radiation detectors employing complex microstructures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Fundamental Piezotronic and Piezo-phototronic Effects in Nanowires

The piezo-phototronic effect is about the use of the piezoelectric potential created inside some materials for enhancing the charge carrier generation or separation at the metal-semiconductor contact or p-n junction. We demonstrate the impact of the piezo-phototronic effect on the photon sensitivity for a ZnO-CdS core-shell micro/nanowire based visible and UVsensor. CdS nanowire arrays were grown on the surface of a ZnO micro/nanowire to form a ZnO-CdS core-shell nanostructure by a facile hydrothermal method. With the two ends of a ZnO-CdS wire bonded on a polymer substrate, a flexible photodetector was fabricated, which is sensitive simultaneously to both green light (548 nm) and UV light (372 nm). The photocurrent and sensitivity of the ZnO_CdS wire photodetector is 103 times higher than that of CdS nanoribbon, and the responsivity (11 A W -1 , 548 nm, at 2 V bias) is nearly 100 times higher than that of ZnSe-nanobelt-based blue/UV-light sensor (0.12 AW -1 , 400 nm, at 30 V bias). Moreover, the performance of the ZnO-CdS wire photodetector upon illumination of visible and UV light can be both further enhanced for more than 10 times with the participation of the piezo-phototronic effect when the device is subjected to a -0.31% compressive strain. This investigation extends the application of the piezo-phototronic effect in a wide spectrum photon detector.

14 SOLAR ENERGY↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2017

Biases of the MET Temperature and Relative Humidity Sensor (HMP45) Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Quality (DQ) Office was alerted to a potential bias in the surface meteorological instrumentation (MET) temperature when compared with a nearby Mesonet station. This led to an investigation into this problem that was expanded to include many of the other extended facilities (EF) and both the temperature and relative humidity (RH) variables. For this study, the Mesonet was used as the standard reference due to results that showed an increased accuracy in high-humidity environments along with the fact that the Mesonet had previous documented a problem with the HMP45C sensors. Some differences between the sites were taken into account during the analysis: 1. ARM MET sensors were upgraded from an HMP35 to an HMP45 throughout 2007 2. Mesonet switched to aspirated shields in 2009 – To mitigate the differences between aspirated and non-aspirated measurements, data were only analyzed when the wind speed was higher than 3 m/s. This reduced the uncertainty for the non-aspirated measurements from 1.51 ºC to 0.4 ºC. 3. ARM MET is mounted 0.5m higher than the Mesonet station (2.0m versus 1.5m) – This is assumed to have a negligible effect on the differences. 4. Sites were not co-located – For some locations, the distances between sites were as much as 45 km. As part of the investigation into the differences, the Mesonet had reported that the HMP45 sensors had a low-temperature bias in high-humidity environments. This was verified at two different sites where the ARM measurements were compared with the Mesonet measurements. The Mesonet provided redundant temperature measurements from two different sensors at each site. These measurements compared fairly well, while the ARM sensor showed a bias overnight when the humidities were higher. After reviewing the yearly average differences in the data and analyzing the RH data during fog events when we assume it should be 100%, we determined that a majority of the sites have a bias in the RH compared to the Mesonet sites, but that only a few sites show a bias in the temperature measurements that are outside the range of instrument uncertainties. We note that there can be a lot of variability across some of the distances between the MET and Mesonet sites and these biases reported herein should not be used as offsets.

47 OTHER INSTRUMENTATION↗
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 OSTI2019

Fabrication and Magnetic Characterization of CFO/NiO and CFO/NiS Heterostructures

Magnetic analysis of different magnetic bilayers and multilayers is an important step for designing next-generation multiferroic heterostructures. In this study, we have investigated the magnetic interaction at the interface in CFO/NiO hetero-structures grown by pulsed laser deposition on sapphire substrates. The thin film heterostructure samples were characterized by means of X-ray diffraction, scanning electron microscopy (SEM), energy disruptive X-ray spectroscopy (EDS), and superconducting quantum interference device (SQUID) for magnetic measurements. The XRD analysis on different samples shows the crystalline growth of the thin films. The SEM-EDS analysis provides information about structural and elemental compositions of the heterostructure films. A SQUID magnetometer was used to investigate the temperature and field-dependent magnetic properties. To better understand the magnetic properties of the thin film heterostructure samples, zero field cool (ZFC) and field cool (FC) magnetization data were analyzed. At low temperature, ferromagnetic hysteresis loop was observed. A shift in the M-H hysteresis curve was observed for the FC data from the ZFC data, which indicates the presence of exchange bias effect. In addition, we have examined the effect of antiferromagnetic NiS thin films on ferromagnetism of CFO. This study on the magnetic interaction between a ferromagnetic layer and an antiferromagnetic layer can advance the frontier for applications in spin valves, magnetic sensors, and magnetic random access memory.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Staggered scheduling of sensor estimation and fusion for tracking over long-haul links

Networked sensing can be found in a multitude of real-world applications. Here, we focus on the communication-and computation-constrained long-haul sensor networks, where sensors are remotely deployed over a vast geographical area to perform certain tasks. Of special interest is a class of such networks where sensors take measurements of one or more dynamic targets and send their state estimates to a remote fusion center via long-haul satellite links. The severe loss and delay over such links can easily reduce the amount of sensor data received by the fusion center, thereby limiting the potential information fusion gain and resulting in suboptimal tracking performance. In this paper, starting with the temporal-domain staggered estimation for an individual sensor, we explore the impact of the so-called intra-state prediction and retrodiction on estimation errors. We then investigate the effect of such estimation scheduling across different sensors on the spatial-domain fusion performance, where the sensing time epochs across sensors are scheduled in an asynchronous and staggered manner. In particular, the impact of communication delay and loss as well as sensor bias on such scheduling is explored by means of numerical and simulation studies that demonstrate the validity of our analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Peak Prediction Using Multi Layer Perceptron (MLP) for Edge Computing ASICs Targeting Scientific Applications

High data rate detectors play an integral part in scientific research and their development is actively pursued at High Energy Physics (HEP) facilities around the world. Edge Machine Learning (ML) offers the ability to reduce data rates by integrating ML algorithms into Application Specific Integrated Circuits (ASICs) on the front end electronics. In this work, we explore a set of neural network architectures for predicting the peak amplitudes in the detector's sensor response. We have designed and synthesized several MLP based neural networks comparing their inference accuracy, power consumption, and area targeting for minimal latency. The neural networks are synthesized in a commercial 65nm process. The effect of quantizing the network's weights and biases on hardware performance and area is reported. We also conduct design space exploration to compare between design alternatives

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Phase Demodulation by Frequency Chirping in Coherence Microwave Photonic Interferometry

This paper presents a signal processing method to demodulate the optical interference phase of cascaded individual optical fiber intrinsic Fabry-Perot interferometric (IFPI) sensors in a coherent microwave-photonic interferometry (CMPI) distributed sensing system. The new method utilizes the chirp effect of electro-optic modulator (EOM) to create a quasi-quadrature optical interference phase shift between two adjacent pulses which correspond to two adjacent reflection points in the time domain. The phase shift can be controlled by adjusting the bias voltage that is applied to the EOM. The interference phase is calculated by elliptically fitting the phase shift. The interference phase change is proportional to the optical path difference (OPD) change of the interferometer, and the sign can be used to differentiate the increase or decrease of the OPD. The method is demonstrated for distributed strain sensing, showing good linearity, high resolution and large dynamic range.

42 ENGINEERING↗
DOE OSTI2018

Josephson Effects in Frequency-Domain Multiplexed TES Microcalorimeters and Bolometers

Frequency-division multiplexing is the baseline read-out system for large arrays of superconducting transition-edge sensors (TES’s) under development for the X-ray and infrared instruments like X-IFU (Athena) and SAFARI, respectively. In this multiplexing scheme, the sensors are ac-biased at different frequencies from 1 to 5 MHz and operate as amplitude modulators. Weak superconductivity is responsible for the complex TES resistive transition, experimentally explored in great detail so far, both with dc- and ac-biased read-out schemes. In this paper, we will review the current status of our understanding of the physics of the TES’s and their interaction with the ac bias circuit. In particular, we will compare the behaviour of the TES nonlinear impedance, across the superconducting transition, for several detector families, namely: high-normal-resistance TiAu TES bolometers, low-normal-resistance MoAu TES microcalorimeters and high-normal-resistance TiAu TES microcalorimeters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗