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At least 469 records · Page 26

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DOE OSTI2020

High-Resolution Measurement of Soil Thermal Properties and Moisture Content Using a Novel Heated Fiber Optics Approach

Hydrological parameters are scale dependent. Efficient monitoring techniques capable of measuring hydrological parameters, such as soil moisture content (θ), over a wide range of spatial scales are essential for understanding the complexity of water and energy movement across the landscape. Techniques to measure θ over spatial scales in the range from centimeters to thousands of meters, however, are sorely lacking. Recent improvements in the distributed temperature sensing (DTS) technology supported the development of novel techniques to fill that gap. However, improvements in the accuracy and applicability of DTS techniques are still needed. This study investigates the possibility of improving the accuracy of the fiber optics dual-probe heat-pulse (FO-DPHP) DTS technique by using a new design to maintain the spacing between the FO-DPHP probes and by introducing a novel data interpretation approach. The accuracy of the novel FO-DPHP design was tested at different θ in a sand column experiment. The FO-DPHP measurements obtained using traditional and novel data interpretation approaches were compared against independent measurements from several calibrated soil water content (EC5) sensors. Monte-Carlo analyses were also performed to assess the impact of DTS measurement errors on the accuracy achieved using the data interpretation approaches. The novel design and data interpretation approach allowed for accurate measurements of soil thermal properties and θ without the need to perform a hard-to-achieve soil-specific calibration. Measured θ had mean errors and standard deviations <0.03 and <0.01 m 3 m -3 , respectively, for moisture conditions ranging from dry to near saturation. The standard deviation in the measured heat capacity was <0.01 MJ m -3 K -1 .

42 ENGINEERING↗

DOE OSTI2023

Development of Fast Decay-Energy Spectroscopy With Magnetic Microcalorimeters

Decay energy spectroscopy (DES) is an increasingly important radiometric technique arising from the unique thermal detection physics and the precision of low-temperature microcalorimetry. DES can enable high-precision analysis of very small amounts of radioactive material with simple and rapid sample preparation, making it a key new tool for nuclear safeguards, nuclear forensics, the improvement of nuclear data, measurements of absolute activity, and other applications. For problems in nuclear safeguards and nuclear forensics it is highly desirable to increase the count rates achievable in DES, while maintaining simple and rapid sample preparation. Here, in this report, we describe progress in a new project to achieve high count rates in DES by taking advantage of the physics of magnetic microcalorimetry. We describe the results of initial modeling and experiment to validate the idea, a sample fabrication technique suitable for nuclear materials laboratories, the preparation and initial tests of paramagnetic Au-Er alloy in thin foil form, and the development of a sensing coil geometry that will perform well with the increased-thickness sensor used in the new technique.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Skipper-in-CMOS: Nondestructive Readout With Subelectron Noise Performance for Pixel Detectors

The Skipper-in-CMOS image sensor integrates the nondestructive readout capability of skipper charge coupled devices (Skipper-CCDs) with the high conversion gain of a pinned photodiode (PPD) in a CMOS imaging process while taking advantage of in-pixel signal processing. This allows both single photon counting as well as high frame rate readout through highly parallel processing. The first results obtained from a ${15} \times {15}~\mu $ m2 pixel cell of a Skipper-in-CMOS sensor fabricated in Tower Semiconductor’s commercial 180-nm CMOS image sensor process are presented. Measurements confirm the expected reduction of the readout noise with the number of samples down to deep subelectron noise of $0.15\text {e}^ - $ , demonstrating the charge transfer operation from the PPD and the single photon counting operation when the sensor is exposed to light. This article also discusses new testing strategies employed for its operation and characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Dark Energy Studies with LSST using the Photon Simulator (Final Report)

This grant funded the development and dissemination of the Photon Simulator (PhoSim) for the purpose of studying dark energy at high precision with the upcoming Large Synoptic Survey Telescope (LSST) astronomical survey. The work was in collaboration with the Dark Energy Science Collaboration (DESC). Several detailed physics improvements were made in the optics, atmosphere and sensor, a number of validation studies were performed, and a significant number of usability features were implemented. Future work in DESC will use PhoSim as the image simulation tool for data challenges used by the analysis groups.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2020

Gaussian process regression for radiological contamination mapping- Applied to optimal motion planning for mobile sensor platforms [Slides]

Want to achieve best representative characterization of the entire area efficiently and accurately-Unmanned aerial/ground vehicles (UAV/UGVs) for contamination mapping. Some major challenges include: Limited battery life (move smart), Human operated (fully autonomous controls) and Many measurements (predictive mapping capabilities). The objective: Develop fully autonomous controls for mobile sensor platforms to improve efficiency and maintain performance.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

Autonomous measurements of carbon dioxide uptake in a blue carbon habitat

A better understanding of carbon cycling dynamics in coastal ecosystems is critical to quantify “blue carbon” storage. However, obtaining the necessary measurements is complicated due to the dynamic conditions that characterize these ecosystems, and requires both spatially and temporally resolved measurements of carbon dioxide concentrations and complementary water quality parameters. In this study, we evaluate the use of a utility-class autonomous surface vehicle as a platform for measuring carbon dioxide dynamics in a shallow, intertidal, blue carbon ecosystem. We integrated a carbon dioxide sensor, a water quality sonde, and hydroacoustic sensors with an autonomous surface vehicle and performed measurements in and around a meadow of Zostera marina eelgrass. Results demonstrate the utility of this system to provide insight into complex carbon cycling dynamics in and around a shallow, intertidal blue carbon habitat.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

An Automated Disruption-Tolerant Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. This paper presents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Here, experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

Operational Focused Data Analytics for Optimizing Radiation Portal Monitor-Based Nuclear Smuggling Detection Systems at Global Ports of Entry

The National Nuclear Security Administration’s Office of Nuclear Smuggling Detection and Deterrence has deployed a fleet of radiation portal monitors (RPMs) across the world at global ports of entry including seaports, airports, and land border crossings. These RPMs are integrated into radiation detection systems (RDS) that also include fixed cameras, optical character recognition (OCR) systems, primary scanning systems (e.g., X-ray or gamma-ray), and secondary scanning systems (e.g., spectroscopic radiation portal monitors, portable radiation detection systems). The data from these sensing technologies is collected at the Central Alarm Station (CAS) where servers and computers reside to control and operate the system. Operators utilize the data collected by the CAS and declared cargo information to make decisions on how to respond to an alarm.This work explores the use of CAS-located data, looking at both the sensor data streams and operator inputs, to perform analysis which supports customs and border protection agencies to improve training capability and operational effectiveness. We focus on analyzing site level effectiveness and behavior by rolling up CAS-located data collected from individual occurrences. To-date, more than 15 sites (e.g., seaports, airports, border crossings) have been analyzed in this manner with the goal of understanding system operations to verify effectiveness and recommend potential improvements. This work first aims to provide background information on relevant CAS-located data sources and our current operational system analytics process including example results. After summarizing our current analytic techniques, we discuss how the future data analytics systems can provide key benefits to improving operational performance while minimizing the burden these detection systems place on operators.

Kuhn, Michael↗

DOE OSTI2024

Silicon Carbide Passive Temperature Sensors at INL

Discuss the SiC passive thermometry methods currently performed at INL and ORNL, decide on best path forward for establishing an ASTM Standard for processing SiC temperature monitors, and expand this ASTM benchmarking efforts to international collaborators (Netherlands, Canada, and Poland).

36 MATERIALS SCIENCE↗

DOE OSTI2021

All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control

This is the final report for the project “All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control”, funded by DOE, and performed by Clemson University, Phase IV Engineering and Harvard University from October 1, 2016 to December 31, 2020. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, plug & play, battery-free, wireless sensor technology to enable low-cost (<$10 per node) indoor and outdoor temperature and humidity measurement for energy efficient building controls and operations. The proposed technology is based on the novel concept of all-digital sensing and its seamless integration with the passive RFID technology. This project focuses on the design, fabrication, material optimization, interrogation electronics, validation, and demonstration of the novel sensor nodes for building applications. The specific objectives of this research program include: (1) Design, fabricate and optimize a compact, robust, and high-resolution digitizer, which could convert rotation angle into digital numbers. (2) Develop the multi-physics-based modeling and simulation of the temperature/humidity transducer to derive a rational design of the architecture, dimension, structure, materials (i.e., mechanical, electrical, thermal and hygroscopic) properties and functions of the sensor node. (3) Design, fabricate and optimize the bi-material based humidity sensitive coil which could linearly transduce the environmental relative humidity variations to rotation angles. (4) Design, fabricate and optimize an UHF RFID platform which could support long-range passive wireless communications of 8-bit digital numbers. (5) Design, fabricate and optimize the miniaturized all-digital sensor using MEMS technology. (6) Validate the integrated all-digital sensing system in a real building environment to test the system’s performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Heat-induced drift reduction of time of flight in fused quartz acoustic waveguides via annealing process

High temperature structural acoustic sensors play an important role in many applications. Fused quartz waveguide is a popular choice due to its resistance to harsh environments and its convenience of modification. However, time of flight between pulse and echo, which is widely used in these sensors, tends to encounter drifts in fast temperature changing process even after temperature returns to initial value. In this article, different annealing process are performed for a special modified fused quartz waveguide with a sensor node. Annealing treatment is found able to reduce the drift when the waveguide undergoes a sudden temperature spike to 1000 °C at 500 kHz operating acoustic frequency, and the best annealing condition could make the drift one magnitude smaller. A following temperature test up to 1000 °C shows consistent measurement readings.

Hileman, Zachary (ORCID:0000000226529274)↗

DOE OSTI2020

Study of CMOS strip sensor for future silicon tracker

Monolithic silicon sensors developed with High-Voltage CMOS (HV-CMOS) processes have become highly attractive for charged particle tracking. Compared with the standard CMOS sensors, HV-CMOS sensors can provide larger and deeper depletion regions that lead to larger signals and faster charge collection. They can provide high position resolution, low material budget, high radiation hardness and low cost that are desirable for high performance tracking in harsh collision environment. Various studies have been conducted to explore the technology feasibility for the large-area tracking systems at future collider experiments. CHESS (CMOS HV/HR Evaluation for Strip Sensor) sensor series have been developed as an alternative solution to the conventional silicon micro-strip detectors for the ATLAS inner tracker upgrade. The first prototype (named CHESS1) was to evaluate the diode geometry and the in-pixel analog electronics. Obtained test results were used to optimize the second prototype (named CHESS2). CHESS2 was implemented with a full digital readout architecture and realized as a full reticle sized monolithic sensor. Here, the basic characteristics of the CHESS2 prototype sensors and their performance in response to different input signals are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Optimization and Performance Characterization of the Second Generation Fermilab Constant Fraction Discriminator Readout ASIC

We present the optimization and performance characterization of the second-generation Fermilab Constant Fraction Discriminator ASIC (FCFD), designed for the readout of AC-coupled low-gain avalanche detector (LGAD) strip-sensors. The FCFD is explicitly engineered to be insensitive to signal-amplitude variations, thereby removing the need for time-walk correction that is required in other LGAD time-stamping readout ASICs. This updated version, referred to as FCFD1.1, incorporates several enhancements over the first iteration to address key challenges in AC-coupled LGAD front-end design. We outline the primary readout-ASIC design considerations for these applications, describe the methodology used to evaluate critical sensor and system parameters, and summarize the additionally implemented features. Performance measurements using injected charge signals and minimum-ionizing particles in test-beams demonstrate a time resolution of approximately 40 ps and a position resolution of roughly \SI{15}{\micro\meter} when tested with beam particles uniformly over the active area of the sensor.

Apresyan, Artur [Fermilab] (ORCID:0000000261860130↗

DOE OSTI2020

Identification and characterization of the dominant thermal resistance in lithium-ion batteries using operando 3-omega sensors

Poor thermal transport within lithium-ion batteries fundamentally limits their performance, safety, and lifetime, in spite of external thermal management systems. All prior efforts to understand the origin of batteries' mysteriously high thermal resistance have been confined to ex situ measurements without understanding the impact of battery operation. Here, we develop a frequency-domain technique that employs sensors capable of measuring spatially resolved intrinsic thermal transport properties within a live battery while it is undergoing cycling. Our results reveal that the poor battery thermal transport is due to high thermal contact resistance between the separator and both electrode layers and worsens as a result of formation cycling, degrading total battery thermal transport by up to 70%. We develop a thermal model of these contact resistances to explain their origin. These contacts account for up to 65% of the total thermal resistance inside the battery, leading to far-reaching consequences for the thermal design of batteries. Finally, our technique unlocks new thermal measurement capabilities for future battery research.

25 ENERGY STORAGE↗

DOE OSTI2024

Evaluation and Intercomparison of Small Uncrewed Aircraft Systems Used for Atmospheric Research

Abstract Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Monolayer Sc 2 CF 2 as a Potential Selective and Sensitive NO 2 Sensor: Insight from First-Principles Calculations

Two-dimensional materials with excellent surface–volume ratios and massive reaction sites recently have been receiving attention for gas sensing. With first-principles calculations, we explored the performance of monolayer Sc 2 CF 2 as a gas sensor. We investigated how molecule adsorption affects its electronic structure and optical properties. It is found that a large charge transfer quantity happens between Sc 2 CF 2 and NO 2 , which results from the fact that the lowest unoccupied molecular orbital (LUMO) of NO 2 is below the valence band maximum (VBM) of Sc 2 CF 2 . Moreover, the MD simulation shows that NO 2 can adsorb on the Sc 2 CF 2 surface stably at room temperature. We explored the effect of biaxial strain on the adsorption energy and charge transfer quantity of each system, and the results show that the biaxial strain can enhance both the adsorption energy and charge transfer quantity of the NO 2 system and thus can improve the sensitivity of Sc 2 CF 2 in detecting the NO 2 molecule. Furthermore, we investigated the adsorption behavior and charge transfer of polar polyatomic molecules at the Sc 2 CF 2 surface with h-BN as a substrate, and the results demonstrate that the h-BN substrate can hardly modify the main results. Our result predicts that Sc 2 CF 2 can be a promising selective and sensitive sensor to detect the NO 2 molecule, and could also give a theoretical guide for other terminated MXenes used for gas sensors or detectors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.

58 GEOSCIENCES↗