DEVELOPMENT OF BIG-DATA EDGE-COMPUTING ANALYTIC FRAMEWORK FOR DISTRIBUTED FIBER OPTIC SENSORS
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02 PETROLEUM↗
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Low-cost Retrofit Kit for Integral Reciprocating Compressors (IRCs) to Reduce Emissions and Enhance Efficiency
Methane emissions from natural gas engines within the oil and gas industry pose a significant environmental challenge, contributing approximately 34.1 MMTCO2 eq to the total of 239 MMTCO2 eq of methane emissions in 2021, according to the EPA report. In response to this pressing issue, a collaborative effort involving the University of Oklahoma and key industry partners—WAGO Automation, Mid Continental Rental, Elipsa, and Perscient—has resulted in the development of a retrofit kit designed to reduce emissions from integral reciprocating compressors (IRCs), which are integrated compressors and engines. The retrofit kit developed comprises an Air Management System (AMS), Integrated Sensors, and a Cloud-Connected Control Unit with Graphical User Interface (GUI)/Human-Machine Interface (HMI). This solution enhances operational efficiency, reduces emissions, and expands the operational envelope of IRCs in the natural gas industry. The project successfully completed all tasks, including the installation of a full-size IRC at a designated site in Oklahoma, the development of an optimized AMS, integration of sensors, and implementation of a data acquisition system. Significant achievements include a notable reduction in CH 4 emissions, up to 84% at specific loads, and the successful deployment of the retrofit kit in diverse field conditions. The system's capabilities were enhanced through the creation of a feedback control algorithm for the AMS using a correlation matrix illustrating relationships between engine parameters, and the design of a predictive and preventive maintenance platform. The project concluded with the deployment of the entire retrofit kit to another location, confirming its effectiveness in reducing emissions and enhancing IRC performance. The comprehensive solution offers valuable benefits for IRCs, making them invaluable assets in the natural gas industry.
03 NATURAL GAS↗
Achieving Cyber-Resilience for Power Systems using a Learning, Model-Assisted Blockchain Framework
The secure integration and management of distributed energy resources (DER) and power aggregators in the electric grid requires secure communications and a physics-aware Command and Control (C2) strategy. A Blockchain (BC)-based overlay network was developed to provide a security layer for the existing power grid network that mitigates risks in current and legacy network and C2 protocols. By integrating a Model-Assisted Machine Learning (MAML) framework with a Secure Blockchain Overlay Network (SBON) a defense-in-depth strategy was achieved. In our approach, the MAML framework leveraged a smart contract framework to gather network data and learn the dynamics of DER to develop detection strategies for attacks targeting sensors and actuators used by DER. The MAML framework learned dynamical systems models for individual DERs to detect sensor attacks. For DER we utilized a Digital Twin (DT) to accelerate the learning process for a model resistant to stealthy attacks. The project created DT for PV inverters and BESS. The DTs were coupled with a model-assisted, data-driven learning of DER behavior. Specifically, we evaluated architectures for model-based learning with model-free fine-tuning. Additionally, differential privacy techniques were used to obfuscate data, while still allowing the computation of attack detection results based on obfuscated data. The SBON developed leverages a private permissioned blockchain network orchestrated with the Hyperledger Fabric framework. To connect the cyber world, which orchestrates the blockchain fabric, and the physical world where the power network resides, we developed a system implementation to enable the secure interaction of the physical world and the abstracted blockchain.
97 MATHEMATICS AND COMPUTING↗
Evaluating Laser-Induced Breakdown Spectroscopy Sensor Technology for Rapid Source Characterization of Rare Earth Elements
A prototype Laser Induced Breakdown Spectroscopy (LIBS) sensor is tested for the determination of rare earth elements (Eu and Yb) in liquid and solid samples. The sensor head, built using a monolithic passively Q-switched (PQSW) Nd:YAG laser, produced a 1064 nm laser beam with 3.3 ns pulses and an energy of 4.2 mJ. The measurements show good calibration linearity for both Eu and Yb with R 2 values above 0.99 for all analyzed spectral lines in liquid and solid samples. Limits of Detection (LODs) obtained were as low as 1 ppm, which are comparable to or better than those reported previously by using table top actively Q-switched systems. This study aims to develop a high sensitivity, field deployable sensor for characterizing existing and new sources of rare earth elements
47 OTHER INSTRUMENTATION↗
Prospects of using rare-earth hexaborides in thermoelectric single-photon detectors
The results of the numerical simulation of heat propagation processes occurring after the absorption of single photons with energies of 1 eV–1 keV in a three-layer sensor of a thermoelectric detector are analyzed. Different configurations of the sensor with a tungsten absorber, a thermoelectric layer of cerium hexaboride, and a tungsten heat sink are considered. It is shown that sensors for detecting photons of a specific spectral range with the required energy resolution and counting rate can be developed by varying the geometric sizes of the sensor layers. It is concluded that a three-layer sensor has a number of advantages in comparison with a single-layer sensor and has characteristics allowing consideration of the thermoelectric detector as a realistic alternative to superconducting single-photon detectors.
36 MATERIALS SCIENCE↗
An introduction to neuromorphic computing and its potential impact for unattended ground sensors
Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.
97 MATHEMATICS AND COMPUTING↗
Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs
The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.
England, Troy [Fermilab] (ORCID:0000000154405255)↗
Two 28-nm front-end ASICs for ultra-fine spatial resolution and precision timing to be 3D integrated with 12 LGADs
The 3DIntSenS Collaboration—a joint effort between SLAC, Fermilab, and LLNL—is developing enabling technologies for next-generation radiation imaging detectors that combine ultra-fine spatial resolution (about 10 µm) with precision timing (<20 ps), while maintaining low power <1 W/cm2 and high data throughput. The approach leverages 3D integration between advanced CMOS readout ASICs and finely pixelated LGAD sensors to achieve the performance and scalability required for large-area, high-rate applications. High-granularity, precision-timing detectors are essential for scientific advances in HEP, NP, BES, and FES, but widespread adoption is limited by the cost and complexity of 3D integration. To close this gap, the collaboration is developing LGAD sensors compatible with 12-inch commercial CMOS processes, enabling cost-effective integration with high-performance ASICs under development. We present two 28 nm CMOS ASIC prototypes, including a low-jitter front end, and in-pixel TDC demonstrating sub-10 ps timing resolution. These advances represent a critical step toward scalable, high-resolution radiation imaging systems for future scientific instrumentation.
England, Troy [Fermilab] (ORCID:0000000154405255)↗
Harmonic Signals Dataset
The Harmonic Signals Dataset (HSD) consists of one-dimensional time series data collected from current sensors with a goal of providing data for research and development of Non-Intrusive Load Monitoring (NILM) with high sample rate (800kHz) sensors. NILM seeks to detect and characterize electrical equipment operating within a facility by sensing changes in electrical current on the building power system associated with the equipment. To provide data with a known ground truth, but with the realism of a signal introduced within a building facility, a series of known ground truth signals were injected as voltage into the power system of a building and measurements of the signals at multiple locations across the building power system were made with electrical current sensors.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Eigendecomposition model of resistance temperature detector with applications to S-CO 2 cycle sensing
Super-critical carbon dioxide (S-CO2) is a promising thermodynamic cycle for advanced nuclear reactors and solar energy conversion applications. Dynamic control of the proposed recompression S-CO2 cycle is accomplished with input from resistance temperature detector (RTD) measurements of the process fluid. One of the challenges in practical implementation of S-CO2 cycle is high corrosion rate of component and sensor materials. In this paper, we develop a mathematical model of RTD sensing using eigendecomposition model of radial heat transfer in a layered long cylinder. We show that the value of RTD time constant primarily depends on the rate of heat transfer from the fluid to the outer wall of RTD. We also show that for typical material properties, RTD time constant can be calculated as the sum of reciprocal eigen-values of the heat transfer matrix. Using the computational model and a set of RTD and CO2 fluid thermophysical parameter values, we calculate the value of time constant of thermowell-mounted RTD sensor at the hot side of the precooler in the S-CO2 cycle. The eigendecomposition model of RTD will be used in future studies to model sensor degradation and its impact on control of S-CO2. (C) 2016 Elsevier B.V. All rights reserved.
finite element↗
PERFORMANCE CHARACTERISTICS OF A VECTOR SENSOR ARRAY IN AN ENERGETIC TIDAL CHANNEL
A compact three-dimensional vector sensor array (termed the NoiseSpotter) was recently developed with the goal of characterizing and localizing sources of sound in energetic environments such as those where marine renewable energy devices are likely to be located. The three vector sensors on the NoiseSpotter are mounted on a bottom platform with each sensor suspended inside an optional flow noise removal shield. The multiple pressure and particle velocity channels on the NoiseSpotter are simultaneously sampled and stored on-board to enable coherent array processing of vector sensor array data. A series of field trials were conducted where the NoiseSpotter was deployed on a bottom platform in an 8 m deep tidal channel in Sequim Bay, Washington. Pure tones were transmitted from a drifting vessel whose distance from the bottom platform varied between 50 m and 500 m. The effect of flow noise on vector sensor array performance is quantified along with the detectability of low intensity sound in an energetic environment. Flow noise removal efficiency for each channel of every vector sensor is demonstrated using multiple metrics derived from the active and reactive intensities computed using the pressure-velocity cross-spectral density matrix. A flow noise removal shield is shown to improve the detectability of low intensity sound such as those that are expected to be emitted by marine renewable energy devices.
Raghukumar, Kaustubha↗
Methodology to Account for Radiation and Temperature Influences on Optical Fibers compaction and Index of Refraction
The effects of radiation induced compaction and index of refraction change can cause significant drift in the measurements from intrinsic fiber optic temperature sensors. The work presented here aims to develop and test a methodology to measure these macroscopic properties that are responsible for the drift in order to correct the measurement. This report discusses the measurement methodology, theory, and sensor construction. Preliminary results from the first irradiation test using this sensor are provided here.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
“Virtual IED sensor” at an rf-biased electrode in low-pressure plasma
Energy distribution and the flux of the ions coming on a surface are considered as the key-parameters in anisotropic plasma etching. Since direct ion energy distribution (IED) measurements at the treated surface during plasma processing are often hardly possible, there is an opportunity for virtual ones. This work is devoted to the possibility of such indirect IED and ion flux measurements at an rf-biased electrode in low-pressure rf plasma by using a “virtual IED sensor” which represents “in-situ” IED calculations on the absolute scale in accordance with a plasma sheath model containing a set of measurable external parameters. The “virtual IED sensor” should also involve some external calibration procedure. Applicability and accuracy of the “virtual IED sensor” are validated for a dual-frequency reactive ion etching (RIE) inductively coupled plasma (ICP) reactor with a capacitively coupled rf-biased electrode. The validation is carried out for heavy (Ar) and light (H{sub 2}) gases under different discharge conditions (different ICP powers, rf-bias frequencies, and voltages). An EQP mass-spectrometer and an rf-compensated Langmuir probe (LP) are used to characterize plasma, while an rf-compensated retarded field energy analyzer (RFEA) is applied to measure IED and ion flux at the rf-biased electrode. Besides, the pulsed selfbias method is used as an external calibration procedure for ion flux estimating at the rf-biased electrode. It is shown that pulsed selfbias method allows calibrating the IED absolute scale quite accurately. It is also shown that the “virtual IED sensor” based on the simplest collisionless sheath model allows reproducing well enough the experimental IEDs at the pressures when the sheath thickness s is less than the ion mean free path λ{sub i} (s < λ{sub i}). At higher pressure (when s > λ{sub i}), the difference between calculated and experimental IEDs due to ion collisions in the sheath is observed in the low energy range. The effect of electron impact ionization in the sheath on the origin and intensity of low-energy peaks in IED is discussed compared to ion charge-exchange collisions. Obviously, the extrapolation of the “virtual IED sensor” approach to higher pressures requires developing some other sheath models, taking into account both ion and electron collisions and probably including even a model of the whole plasma volume instead of plasma sheath one.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Evaluation of the Reliability of Passive Infrared (PIR) Occupancy Sensors for Residential Indoor Lighting
Solid-state lighting (SSL) technologies have penetrated the general illumination market in recent years, largely replacing conventional technologies such as incandescent and fluorescent lighting. Most of the initial excitement about light-emitting diode (LED) sources for SSL devices focused on their energy savings potential resulting from vast improvements in source efficiency and luminous efficacy compared with conventional illumination products. More recently, the focus has shifted toward other aspects of lighting application efficiency, namely intensity effectiveness and spectral efficiency, because of the ease of controlling both the light intensity of LEDs with drive voltage and the color properties of the LED-based illuminators. To capitalize on the energy savings of increased intensity effectiveness and spectral efficiency, a lighting control system (LCS) is often used. While the current penetration of LCSs is relatively modest, it is anticipated that lighting controls (i.e., connected lighting, controls and LED and conventional lighting) could have an installed penetration as higher as 46% by the year 2035, saving an additional 1.3 quads of energy. Despite the large energy savings that can be gained from using LCSs, standard test methods for evaluating sensors employed in LCSs and reliability data of the LCSs and their components are generally lacking. The National Electrical Manufacturers Association (NEMA) developed the only standard, NEMA WD 7-2011 (R2016), to test occupancy and motion sensor performance (herein referred to as “the NEMA protocol”). In a previous U.S. Department of Energy (DOE) effort, some concerns of the NEMA protocol were identified (e.g., strict height and weight limits on test subjects, large amounts of manual effort, sometimes inconsistent repeatability). During the current work, a consistent detection length test (DLT) and a Robotic Sensor Evaluation System (RoboSES) were developed to alleviate some of these concerns. RoboSES acts as a human surrogate by using thermal pads on a mannequin and a remote-controlled mobile base to test a sensor’s field of view (FOV). This report builds on the earlier DOE efforts to understand sensor technologies used in LCSs for general illumination. Specifically, this report describes the optimization of RoboSES, characterizes and establishes test methods to assess the reliability of multiple passive infrared (PIR) sensors, and reports the findings of robustness and reliability testing on two commercial PIR sensors intended for residential applications. The information presented in this report is gained from up to 4,000 hours (hrs) of accelerated stress tests (ASTs).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Spatiotemporal flame propagations, combustion and solid particle emissions from lean and stoichiometric gasoline direct injection engine operation
Increased particle number and mass emissions in gasoline direct injection (GDI) engines should require to investigate in-cylinder flame and combustion characteristics associated with primary source of particle emissions. In this article, in-cylinder spatiotemporal flame luminosity is quantitatively characterized to features combustion process and solid particle emissions from a GDI engine operating in two lean and one stoichiometric modes. Low- and high-steady state operating points were used to compare combustion strategies on flame development and emission characteristics. A fiber-optic sensor composed of eight measurement channels detected the flame front and the direction of the flame propagation in the combustion chamber. Solid particle emissions in the exhaust were measured using an engine exhaust particle sizer and a micro soot sensor. Results of the experiments showed that two lean combustion modes by injection strategies resulted in distinct combustion and flame development. Lean combustion modes generated high diffusion flame by burning stratified rich-mixture. Although the lean cases resulted in strong diffusion flames, the lean-homogeneous produced similar particle size distributions with the stoichiometric mode with high ash particles. Piston pool fires on the piston surface in the lean-stratified mode induced a large accumulation mode with high particle mass concentrations.
42 ENGINEERING↗
Resilient Reinforcement in Secure State Estimation against Sensor Attacks with a priori Information
This paper deals with the state estimation problem in the presence of malicious sensor attacks. Unlike the existing work, in this paper, we address the problem with a priori information of the state to be estimated. Specifically, we address three prior knowledge: the sparsity information, (alpha, n_0)-sparsity information, and side information, and in each scenario, we show that the state can be reconstructed even if more sensors are compromised. This implies that the prior information reinforces the system resilience against sensor attacks. Then, an estimator under sensor attacks considering the information is developed and, under a certain condition, the estimator can be relaxed into a tractable convex optimization problem. Further, we extend this analysis to systems in presence of measurement noises, and it is shown that the prior information reduces the state-estimation error caused by the noise. Here, the numerical simulations in a diffusion process finally illustrate the reinforcement and error-reduction results with the information.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra
Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.
54 ENVIRONMENTAL SCIENCES↗
Infrared Cloud Imager Instrument Intercomparison Report
The Infrared Cloud Imager Instrument Intercomparison was a guest instrument deployment by NWB Sensors to the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility observatory on the Southern Great Plains (SGP) between May 18 and December 12, 2023. NWB Sensors is a company that has developed a commercially available infrared cloud imager (ICI). The ICI provides radiometrically calibrated, full-sky images of the downwelling infrared radiance in the 7.3-14 µm band. In addition, it provides cloud radiance as the residual between the observed radiance and the modeled cloud-free radiance as well as derived cloud products. The instrument is used in applications that require consistent detection of clouds across day and night. For more information, consult the instrument's webpage. The primary goal of the deployment was to validate the radiometric accuracy of the ICI. The ICI uses a proprietary calibration method to convert the raw data from its infrared camera into downwelling radiance. Unlike similar instruments, the system does not have an onboard blackbody calibration standard. Instead, NWB Sensors characterizes each ICI camera individually in an environmental chamber while looking at a blackbody standard. The resulting (proprietary) calibration is used operationally in the instrument and has been demonstrated to be stable over long periods. To validate the radiometric products from the ICI, an intercomparison between the ICI data products and those from ARM’s atmospheric emitted radiance interferometer (AERI) was made. The AERI is a best-in-class instrument for measuring downwelling infrared radiance (Gero et al. 2025). A weighted integration of the AERI’s spectral radiances across the ICI’s camera response was performed. The resulting radiance (herein called the AERI radiance) was directly compared to the zenith radiance concurrently observed by the ICI. The results of these comparisons are reported in the next section of this report.
54 ENVIRONMENTAL SCIENCES↗