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DOE OSTI2021

Mutual Inductance Level Sensor for Use in Liquid Metals

Electromagnetic instrumentation capable of measuring the level of liquid metal has been developed at Argonne National Laboratory’s (ANL) Mechanisms Engineering Test Loop (METL). The mutual inductance level sensor (MILS) utilizes the electromagnetic coupling between two coil conductors and a surrounding liquid metal to determine the level of the liquid metal. Mineral insulated cables wrapped on a stainless steel core provide a durable sensor construction. The use of a sealed stainless steel thimble isolates the sensor from the high-temperature liquid metal, allowing for easy sensor maintenance. Modern digital electronics allow for reliable and accurate operation of the sensor. Two sensor variations have been designed and fabricated, and the first variation has seen extensive testing in a non-sodium testing stand as well as in the high-temperature sodium environment of METL. Electromagnetic finite-element analysis studies have been performed using the COMSOL Magnetic Fields Solver. Regular operation of the MILS will commence following in-situ calibration of the sensors in the METL environment.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Review of Existing Test Methods for Occupancy Sensors

One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict their performance in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies, have come on the market over the years, with claims of improved performance compared to their predecessors; yet in practice, their performance has led to neither high deployment nor high user satisfaction in any significant application, nor has it differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them have typically been loosely described and incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a well-described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, also additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recent published, well-described test methods for characterizing occupancy sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates the best techniques for characterizing sensor performance in indoor spaces and identifies test method gaps that need to be filled in order to evaluate emerging technologies and products.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Novel 3D Force Sensors for a Cost-Effective 3D Force Plate for Biomechanical Analysis

Three-dimensional force plates are important tools for biomechanics discovery and sports performance practice. However, currently, available 3D force plates lack portability and are often cost-prohibitive. To address this, a recently discovered 3D force sensor technology was used in the fabrication of a prototype force plate. Thirteen participants performed bodyweight and weighted lunges and squats on the prototype force plate and a standard 3D force plate positioned in series to compare forces measured by both force plates and validate the technology. For the lunges, there was excellent agreement between the experimental force plate and the standard force plate in the X-, Y-, and Z-axes (r = 0.950–0.999, p < 0.001). For the squats, there was excellent agreement between the force plates in the Z-axis (r = 0.996, p < 0.001). Across axes and movements, root mean square error (RMSE) ranged from 1.17% to 5.36% between force plates. Although the current prototype force plate is limited in sampling rate, the low RMSEs and extremely high agreement in peak forces provide confidence the novel force sensors have utility in constructing cost-effective and versatile use-case 3D force plates.

42 ENGINEERING↗

DOE OSTI2025

A Synthetic Pathway for Producing Carbon Dots for Detecting Iron Ions Using a Fiber Optic Spectrometer

Iron detection is of growing importance in the critical minerals sector, where unwanted iron ions are typically removed during the processing of target critical metals. The ideal sensor should utilize inexpensive, scalable materials along with a low-cost, robust, and easy-to-use analysis platform. Here, we demonstrate a simple acid–base synthesis of luminescent iron-responsive carbon dots by reacting ethanolamine, phosphoric acid, and m-phenylenediamine. The carbon dots exhibit selective, iron-specific emission quenching, with the ability to detect part-per-billion levels of iron ions even in 0.1 M HCl. After benchmarking the purified materials using a commercial spectrometer, a “low-cost” process is demonstrated in which carbon dots with minimal purification are coupled with a portable fiber-optic spectrometer for analyzing iron content. Carbon dot-coated paper strips are also evaluated as another convenient platform for iron analysis. Taken together, the sensing material and platforms demonstrated here are well-suited for detecting trace quantities of iron in environmentally relevant conditions, with potential applications in tracking iron removal processes during critical mineral production as one exciting area of interest.

acid mine draining↗

DOE OSTI2025

State-dependent motion of a genetically encoded fluorescent biosensor

Genetically encoded biosensors can measure biochemical properties such as small-molecule concentrations with single-cell resolution, even in vivo. Despite their utility, these sensors are “black boxes”: Very little is known about the structures of their low- and high-fluorescence states or what features are required to transition between them. We used LiLac, a lactate biosensor with a quantitative fluorescence-lifetime readout, as a model system to address these questions. X-ray crystal structures and engineered high-affinity metal bridges demonstrate that LiLac exhibits a large interdomain twist motion that pulls the fluorescent protein away from a “sealed,” high-lifetime state in the absence of lactate to a “cracked,” low-lifetime state in its presence. Understanding the structures and dynamics of LiLac will help to think about and engineer other fluorescent biosensors.

Rosen, Paul C. (ORCID:000000017414454X)↗

DOE OSTI2024

Assembly of CMS Endcap MIP Timing Detector Module at FNAL

The High-Luminosity LHC (HL-LHC) will enable a more detailed exploration of new phenomena thanks to an anticipated increase in collisions where pileup is expected to reach approximately 200 simultaneous interactions. Many CMS systems will be significantly upgraded to prepare for this new era, including the MIP Timing Detector (MTD) project. The MTD is designed to mitigate the effect of pileup and is set to provide a timestamp accurate to 30 ~ 40 picoseconds for every event, ensuring sustained detector performance at HL-LHC. The MTD is divided into two sections, Barrel Timing Layer (BTL) and Endcap Timing Layer (ETL) which utilize different sensor and ASIC technologies due to the difference in active surfaces, irradiation conditions, and installation schedules. The ETL, composed of two double-sided disks, employs the Low Gain Avalanche Detector (LGAD) sensor and the Endcap Timing Readout Chip (ETROC). More than 8,000 modules, each consisting of four LGAD sensors and ETROCs are required for the ETL detector. These modules will be assembled using an automated robotic gantry that guarantees precise placement at a level of 10 micrometers. In addition, the full assembly of ETL modules includes film application with the jig, wire-bonding, encapsulation with the automated dispensing robot for protecting the wire-bonding, and film curing with a vacuum oven. This talk reports on the successfully completed throughput test with mockup components using the gantry and the successful assembly of real functional modules for beam tests at CERN and FNAL, including the first official ETL module.

Apresyan, Artur↗

DOE OSTI2020

Estimating vehicle fuel economy from overhead camera imagery and application for traffic control

In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with minimal impact to mobility. We introduce the ORNL Overhead Vehicle Dataset (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for vehicle detection. We show the dataset utility through three applications: the estimate of fuel consumption based on segmentation bounding boxes, vehicle discrimination for those vehicles with largest bounding boxes, and a fine-grained classification on a limited number of vehicle makes and models using a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set based on web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation of Urban Mobility (SUMO) package. Our results show the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras.

Karnowski, Thomas↗

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 OSTI2020

Sampling Rate Comparison for Distribution System Voltage Measurements

Measurement of the voltage delivered by an electric distribution is a relatively straight forward task. When determining the harmonic distortion in the voltage, matters associated with what orders of the 50/60 Hz harmonics to be analyzed dictate the sampling rate. A study addressing this matter is presented.

42 ENGINEERING↗

DOE OSTI2020

FNET/GridEye: A Tool for Situational Awareness of Large Power Interconnetion Grids

This paper gives an overview of a wide-area measurement system deployed at the distribution level: FNET/GridEye, which consists of hundreds of sensors, communication and a data center. The sensors are utilized to take frequency, voltage phase angle and magnitude measurements from the ordinary 110 or 220V outlets in offices or residential houses. These measurements are continuously transmitted to the data center via Internet. Many applications have been implemented to monitor large-scale interconnected power grids, and interpret grid operation status to improve system operators' situational awareness capability. Some representative online and offline applications are presented in this paper, as well as several recently developed new applications.

Zhu, Lin↗

DOE OSTI2024

Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions

In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.

LiDAR, machine learning, neural network, object re↗

DOE OSTI2025

QUPITER -- Space Quantum Sensors for Jovian-Bound Dark Matter

We propose utilizing space quantum sensors to detect ultralight dark matter (ULDM) bound to planetary bodies, focusing on Jupiter as the heaviest planet in the solar system. Leveraging Jupiter's deep gravitational potential and the wealth of experience from numerous successful missions, we present strong sensitivity projections on the mass and couplings of scalar ULDM. Future space missions offer unique opportunities to probe the ULDM interactions using quantum sensors, including superconducting quantum interference device (SQUID) magnetometers. By measuring dark matter-induced magnetic field oscillations, we expect to achieve sensitivity orders of magnitude beyond the terrestrial probes and significantly improve detection prospects of theoretically motivated ULDM candidates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗

DOE OSTI2022

High resolution atomic force microscopy with an active piezoelectric microcantilever

Active microcantilevers with on-chip sensing and actuation provide significant advantages in tapping mode Atomic Force Microscopy (AFM). Collocated transduction allows for effective manipulation of cantilever dynamics through feedback control, enabling higher scan rates. However, the adjacency of the sensing and actuation electrodes is known to result in a high level of feedthrough, leading to a low imaging resolution. Readout circuit noise further deteriorates the imaging precision. Here, we investigate the noise sources that affect AFM microcantilevers with collocated aluminum nitride (AlN) actuator–sensor pairs. We reported these cantilevers in earlier work and demonstrated that they display a very low level of feedthrough between the actuation and sensing electrodes. We present a high signal-to-noise ratio (SNR) sensing method that enables us to demonstrate high-resolution AFM on a calibration grating with nm-step silicon carbide (SiC) terraces. In conclusion, measuring the Lorentzian response of the cantilever’s Brownian motion with the on-chip active sensor at resonance enables us to calibrate the dynamic stiffness at the first fundamental resonance mode, without utilizing an optical sensor.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Editorial: Deep-sea observation equipment and exploration technology

The increasing interest in deep-sea exploration is a crucial catalyst for driving advancements in deep-sea observation technology research. Numerous sophisticated sensors have been developed to facilitate comprehensive studies in deep-sea physics, chemistry, and geology. For example, cutting-edge small and compact seabed samplers are now employed on various platforms, such as Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and Human-Operated Vehicles (HOVs), enabling observation of the hadal zone. Additionally, Micro-electromechanical systems (MEMS) sensors are routinely utilized for ocean wave observation. This Research Topic aims to foster new insights into ocean exploration and observation technology, including ocean sensor technology, ocean observation platforms, and ocean observation reliability technology.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Practical procedures for sensor quality assessment

Sensors are increasingly deployed for process monitoring and control. These produce on-line measurements at a high frequency, in parallel with low-frequency laboratory measurements. Compared to laboratory practices, sensor data quality assessment and control practices are far less structured at most utilities. This leads to inaccurate sensor data with unknown uncertainty factors.This chapter shows how to establish standard operating procedures (SOPs) to support sensor data quality assessment and control and subsequent maintenance actions by producing relevant sensor metadata. Furthermore, SOPs are provided for the most commonly used wastewater quality sensors, inspired by utility and academic best practices. This chapter builds on definitions provided in Chapter 3 and provides additional definitions specifically related to sensors maintenance. Chapter 6 complements the methods in this chapter, which are based on reference measurements, with data-analytical techniques.

Alferes, Janelcy↗

DOE OSTI2023

Stretchable and wearable polymeric heaters and strain sensors fabricated using liquid metals

Wearable electronic devices (WEDs) are receiving significant attention because of the increasing interest in soft robotics, electronic skin, and wearable sensors. Liquid metals (LMs) are compelling for WEDs owing to their metallic conductivity, fluidic nature, and low toxicity. For this study, we fabricated stretchable and soft WEDs using LM (eutectic gallium-indium alloy) wires (LMWs) patterned via force wetting through custom-made stencils on an elastic substrate. LMWs can generate thermal energy via Joule heating upon current application and deliver it to the substrate, resulting in wearable polymeric heaters. The LM mixed with carbonyl iron particles (CIPs) can also be patterned while preserving fluidic behavior. The degree of thermal energy generated through the LMWs can be manipulated as a function of the CIP concentration in the LM and geometrical factors of the electrode patterns, i.e., width and length. An elastic film patterned with LMWs attached to the human body exhibits changes in the effective electrical resistance depending on applied strain, demonstrating potential as a wearable strain sensor. This LM utilized WED that can generate thermal energy upon current application through the LMWs and detect the bodily motion has significant potential for application in wearable thermotherapy, electronic skin, and soft sensors.

47 OTHER INSTRUMENTATION↗