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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling and Characterization of TES-Based Detectors for the Ricochet Experiment

Coherent elastic neutrino-nucleus scattering (CE nu NS) offers a valuable approach in searching for physics beyond the standard model. The Ricochet experiment aims to perform a precision measurement of the CE nu NS spectrum at the Institut Laue-Langevin nuclear reactor with cryogenic solid-state detectors. The experiment plans to employ an array of cryogenic thermal detectors, each with a mass of around 30 g and an energy threshold of below 100 eV. The array includes nine detectors read out by transition-edge sensors (TES). These TES-based detectors will also serve as demonstrators for future neutrino experiments with thousands of detectors. In this article, we present an update on the characterization and modeling of a prototype TES detector.

Chang, C. L.↗
DOE OSTI2025

Reversible parts-per-trillion-level detection of perfluorooctane sulfonic acid in tap water using field-effect transistor sensors

Widespread, persistent and toxic per- and polyfluoroalkyl substances (PFAS) pose a major threat to water systems and human health. Current detection methods are relatively expensive, slow and complex, underscoring the need for more accessible alternatives to meet increasingly stringent PFAS regulations. Here, in this study, we present an ultrasensitive sensing platform for perfluorooctane sulfonic acid detection in tap water with a reporting limit ( ~ 250 parts per quadrillion) lower than the US Environmental Protection Agency’s regulatory standard (4 parts per trillion), using a remote gate field-effect transistor featuring β-cyclodextrin (β-CD)-modified reduced graphene oxide as the sensing membrane. The sensor exhibits excellent selectivity against common inorganic ions, natural organic matter and select organic pollutants in tap water. The reversible and rapid response ( < 2 min) indicates the potential of remote gate field-effect transistor for continuous in-line monitoring. Mechanistic studies using quartz crystal microbalance and molecular dynamics simulations reveal key roles of analyte adsorption and charge properties in sensing performance and offer insights for designing more selective PFAS capture probes.

Wang, Yuqin [University of Chicago, IL (United Sta↗
DOE OSTI2025

Operation and performance of the CMS silicon strip tracker with proton-proton collisions at the CERN LHC

Salient aspects of the commissioning, calibration, and performance of the CMS silicon strip tracker are discussed, drawing on experience during operation with proton-proton collisions delivered by the CERN LHC. The data were obtained with a variety of luminosities. The operating temperature of the strip tracker was changed several times during this period and results are shown as a function of temperature in several cases. Details of the system performance are presented, including occupancy, signal-to-noise ratio, Lorentz angle, and single-hit spatial resolution. Saturation effects in the APV25 readout chip preamplifier observed during early Run 2 are presented, showing the effect on various observables and the subsequent remedy. Studies of radiation effects on the strip tracker are presented both for the optical readout links and the silicon sensors. The observed effects are compared to simulation, where available, and they generally agree well with expectations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗
DOE OSTI2024

Surface Pressure Fluctuations Induced by a Hypersonic Turbulent Boundary Layer on a Sharp Cone at Angle of Attack

High-fidelity simulations are performed to characterize the turbulence-induced wall pressure fluctuations on a sharp cone at a 5.5° angle-of-attack in a Mach 8 flow. Wall-resolved large-eddy simulation (LES) and wall-modeled large-eddy simulation (WMLES) results are compared to measurements at several locations on the cone body, where comparisons with high-frequency PCB sensors are good, while comparison with low-frequency kulite sensors varies depending on the location. For a given streamwise location, simulation results show significant azimuthal variation in the wall pressure fluctuation statistics. Comparisons between LES and WMLES results indicate that WMLES can accurately reproduce the low-frequency component of the autospectra quite well, while slight sensitivity is apparent in the coherence. A grid sensitivity study for WMLES further demonstrated sensitivity in the wall pressure fluctuation coherence, but not in the autospectra.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

CACTI: Best Estimate Aerosol Size Distribution by airborne measurements

These data were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI; https://www.arm.gov/research/campaigns/amf2018cacti ) field campaign in the Sierras de Córdoba mountain range of north-central Argentina as part of ARM Aerial Facility (AAF) deployment. The ARM Aerial Facility Gulfstream-1 was operated from Las Higueras Airport (IATA: RCU, ICAO: SAOC), Río Cuarto, Córdoba, Argentina, for the Intensive Observation Period (IOP) from Nov. 1 through Dec. 15, 2018. The G-1 aircraft performed 22 research flights over the first ARM Mobile Facility (AMF1) location in the Sierras de Córdoba mountain range to measure atmospheric state and turbulence, cloud water content and droplet size distributions, aerosol precursor gases, aerosol chemical composition and size distributions. The current data set presents Best Estimate Aerosol Size Distribution: a merged aerosol size distribution composed of the data from 5 sensors; three aerosol spectrometers: Scanning Mobility Particle Sizer (SMPS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), and Passive Cavity Aerosol Spectrometer (PCASP); and two cloud probes: Cloud Aerosol Spectrometer (CAS) and Fast Cloud Droplet Probe (FCDP). The SMPS data were interpolated to 1 second from “native” time resolution of about 64 second to match all other probes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Searching for Dark Matter with Superconducting Qubits

Dark matter is an enduring mystery in our quest to understand the fundamental constituents of our universe. Low mass bosons, such the axion or hidden photon, are compelling dark matter candidates. We leverage their potential interactions with electromagnetic fields, whereby the dark matter (of unknown mass) on rare occasion converts into a single photon, to devise a method of detecting these candidates. Current dark matter searches operating at microwave frequencies use a resonant cavity to coherently accumulate the field sourced by the dark matter and a near standard quantum limited (SQL) linear amplifier to read out the cavity signal. To further increase sensitivity to the dark matter signal and enable future searches, sub-SQL detection techniques are required.In this thesis, I report the development of a novel microwave photon counting technique and a new exclusion limit on hidden photon dark matter. We operate a superconducting qubit to make repeated quantum non-demolition measurements of cavity photons and apply a hidden Markov model analysis to reduce the noise to 15.7 dB below the quantum limit, with overall detector performance limited by a residual background of real photons. With the present device, we perform a hidden photon search and constrain the kinetic mixing angle to less than 1.68 x 10-15 in a band around 6.011 GHz (24.86 micro-eV) with an integration time of 8.33 s. This demonstrated noise reduction technique enables future dark matter searches to be sped up by a factor of 1300. By coupling a qubit to an arbitrary quantum sensor, more general sub-SQL metrology is possible with the techniques presented in this work.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗
DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗
DOE OSTI2020

Flat optics for image differentiation

Image processing has become a critical technology in a variety of science and engineering disciplines. Although most image processing is performed digitally, optical analog processing has the advantages of being low-power and high-speed, but it requires a large volume. Here, we demonstrate flat optics for direct image differentiation, allowing us to significantly shrink the required optical system size. We first demonstrate how the differentiator can be combined with traditional imaging systems such as a commercial optical microscope and camera sensor for edge detection with a numerical aperture up to 0.32. We next demonstrate how the entire processing system can be realized as a monolithic compound flat optic by integrating the differentiator with a metalens. The compound nanophotonic system manifests the advantage of thin form factor as well as the ability to implement complex transfer functions, and could open new opportunities in applications such as biological imaging and computer vision.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum metrology of low-frequency electromagnetic modes with frequency upconverters

We present the RF Quantum Upconverter (RQU) and describe its application to quantum metrology of electromagnetic modes between dc and the very high frequency band (VHF) ( ≲ 300 MHz). The RQU uses a Josephson interferometer made up of superconducting loops and Josephson junctions to implement a parametric interaction between a low-frequency electromagnetic mode (between dc and VHF) and a mode in the microwave C Band ( ∼ 5 GHz), analogous to the radiation pressure interaction between electromagnetic and mechanical modes in cavity optomechanics. We analyze RQU performance with quantum amplifier theory and show that the RQU can operate as a quantum-limited op-amp in this frequency range. It can also use nonclassical measurement protocols equivalent to those used in cavity optomechanics, including back-action evading (BAE) measurements, sideband cooling, and two-mode squeezing. These protocols enable experiments using dc VHF electromagnetic modes as quantum sensors with sensitivity better than the standard quantum limit (SQL). We demonstrate signal upconversion from low frequencies to the microwave C band using an RQU and show a phase-sensitive gain (extinction ratio) of 46.9 dB , which is a necessary step towards the realization of full BAE. Published by the American Physical Society 2025

Kuenstner, Stephen E. (ORCID:0000000346128846)↗
DOE OSTI2021

Passive Inter-Photon Imaging

Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. This direct measurement method, while conceptually simple, suffers from limited dynamic range and poor performance under extreme illumination --- electronic noise dominates under low illumination, and pixel full-well capacity results in saturation under bright illumination. We propose a novel intensity cue based on measuring inter-photon timing, defined as the time delay between detection of successive photons. Based on the statistics of inter-photon times measured by a time-resolved single-photon sensor, we develop theory and algorithms for a scene brightness estimator which works over extreme dynamic range; we experimentally demonstrate imaging scenes with a dynamic range of over ten million to one. The proposed techniques, aided by the emergence of single-photon sensors such as single-photon avalanche diodes (SPADs) with picosecond timing resolution, will have implications for a wide range of imaging applications: robotics, consumer photography, astronomy, microscopy and biomedical imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring

Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

03 NATURAL GAS↗
DOE OSTI2024

Development of Miniaturized High-Temperature Multi-Process Monitoring System

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Towards Three-Dimensional Neutron Imaging with Light-field Technology

A broad range of applications in nuclear safeguards and security can benefit from compact, fast neutron imagers with large angular acceptance. However, accurate 3D multi-vertex reconstruction in a monolithic detector remains a significant barrier to realization. Current approaches, such as the centroiding approach and the use of shadow masks, have not yet resulted in a successful demonstration in a light-starved environment. This project explored the use of cutting-edge technology—a light field camera—which inherently preserves both the spatial and directional information of incident light. Such technology can, in principle, resolve multi-vertex events and offer accurate position reconstruction in 3D with relatively simple readout electronics. Since the project's inception in May 2023, we have built an experimental setup to calibrate and characterize a commercial light-field camera (Lytro Illum). We assessed its 3D event reconstruction capability in a relatively low-light intensity environment by analyzing the cross-correlation of a series of 2D images of a characterized tunable light source taken at various distances. A sub-cm resolution, lower than typical neutron interaction separations in a compact scintillator volume, was observed with the LED light source, suggesting the promising capability of its nominal optics design to provide the adequate resolution required for a compact monolithic neutron directional detector. However, using a light-field-based readout for a neutron camera requires incorporating an ultra-low-noise light sensor, which is beyond the scope of this project. We also initiated the development of a 3D light-field-based reconstruction algorithm tailored to sparse scenes, as expected from particle interactions in a scintillator medium. Additionally, we demonstrated the capability of the algorithm to replicate the ground truth. Finally, we began the development of a neutron directional detector simulation to determine the performance criteria for light-field-based reconstruction that allows for good neutron directional reconstruction. This Feasibility Study has led to a successful follow-up project under the DNN R&D innovation portfolio starting May 2024.

42 ENGINEERING↗
DOE OSTI2023

Automated Progress Monitoring in Modular Construction Factories Using Computer Vision and Building Information Modeling

Modular construction methods have recently gained interest due to the advantages offered in terms of safety, quality, and productivity for projects. In this method, a significant portion of the construction is performed off-site in factories where modular components are built in different workstations, assembled on the production line, and shipped to the site for installation. Due to the labor-intensive nature of tasks, cycle times in modular construction factories are highly variable, which commonly leads to major bottlenecks and delays in construction projects. To remedy this effect, recent methods rely on sensors such as RFID to monitor the production process, which is reportedly expensive, and intrusive to the work process. Recently, computer vision-based methods have been proposed to track the production process in modular construction factories. However, these methods overlook monitoring the assembly process on the production line. Therefore, this paper presents a method to monitor the assembly process by integrating computer vision-based methods with Building Information Modeling (BIM). The proposed method detects the modular units using object segmentation; superimposes the installation area with the corresponding 2D region using BIM, and identifies the installation of the components using image processing techniques. The proposed method has been validated using surveillance videos captured from a modular construction factory in the US. Successful implementation of the proposed method can lead to timely identification of delays during the assembly process and reduce delays in modular integrated construction projects.

building information modeling↗
DOE OSTI2020

The EGS Collab Project: Learnings from Experiment 1

The primary objective of the EGS Collab Project sponsored by DOE is to increase the understanding needed to efficiently implement enhanced geothermal systems (EGS). One goal of the EGS Collab project is to create a collaborative research environment in which to study stimulation of crystalline rock at the 10 meter scale. Key to this effort is the collection of high quality data to allow comparison to numerical coupled process models in an effort to build confidence in the codes and modeling techniques used. In response to this, the EGS Collab team has created an underground test bed at the Sanford Underground Research Facility (SURF) in Lead SD at a depth of approximately 1.5 km to examine hydraulic fracturing (Experiment 1). We are currently designing a second test bed aimed at investigating shear stimulation (Experiment 2). At the Experiment 1 location, we have characterized our host rock using laboratory testing and numerous field-based geophysical and geological techniques, and created a well-instrumented test bed to allow us to carefully monitor stimulation events and flow tests. In addition to the installed geophysical sensors, we have used tracer tests, differences in the ambient microbial communities at flow collection locations, and cold water injection to inform us about dynamic flow pathways. In Experiment 1, we have hydraulically stimulated the host rock in a number times at several locations in one well, creating new fractures that connect to existing fractures between the injection and production boreholes. We have performed long-term ambient and chilled water injection tests as an analog to EGS, and have monitored system changes resulting from these water injections through geophysical monitoring, flow and pressure measurements, tracer tests, and microbiology. Here, we summarize the tests performed, issues identified including poroelastic and thermoelastic effects, Joule-Thomson effects, restarting effects, indications of flow channeling, and the primary learnings from Experiment 1.

Enhanced Geothermal Systems, EGS Collab, stimulati↗
DOE OSTI2022

Integration of the Kromek D3S Detector and Spot Robot For Secondary Inspections

Inspecting vehicles and containers for the presence of nuclear material is a challenging task for border control and security. When performed manually by inspectors, this task also has an associated risk of exposing the inspectors to unknown radiation. With the advent of agile, easy-to-program, quadruped robots like the Boston Dynamics Spot, automation of secondary inspection can improve the efficiency of the inspection process and alleviates the radiation risks to inspectors. In this project, Brookhaven National Laboratory and the University of Massachussetts at Lowell explored how to automate a simple secondary inspection mission. The Spot robot comes with its own software development kit (SDK) that allows clients/users to write custom code in the Python programming language to control the robot. Spot also has a payload computer called Spot-CORE, which runs the Ubuntu Linux operating system and allows users to integrate external sensors, such as a radiation detector, with Spot. In this study, the Kromek D3S detector has been integrated with Spot via the Spot-CORE, allowing Spot to capture gamma spectra and neutron counts for a specified acquisition period. Two custom routines, search and confirmation, have been developed and executed in this specified order. The search routine directs Spot to go around the nearest obstacle, e.g., vehicle and container, in a preset distance and step to collect gamma and neutron gross counts with the D3S detector. The radiation data and the robot location corresponding to each step are stored and fed to the confirmation routine at the end of the search. The confirmation routine then navigates Spot to the locations of the highest gamma or neutron counts to perform a long, e.g., one minute, measurement, and gives the operators the signature gamma spectra and neutron counts at the hotspots. This paper presents a detailed description of this automated system along with results of the preliminary tests in identifying the location and signature of a 137Cs radiation source in a vehicle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗