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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Particle showers detected on ISS in Timepix pixel detectors

We detect regular particle showers in several compact pixel detectors, distributed over the International Space Station. These showers are caused by high energy galactic cosmic rays, with energies often in the 10 s of TeV or higher. We survey the frequency of these events, their dependence on location on ISS, and their independence of the location of ISS, on its orbit. The Timepix detectors used allow individual particle tracks to be resolved, providing a possibility to perform physical analysis of shower events, which we demonstrate. In terms of radiation dosimetry, these showers indicate certain possible limitations of traditional dosimetric measures, in that (a) the dose measured in small sensor may be less than that received in a larger distribution of matter, such as a human and (b) the spatial and temporal extent of these events represents a regime of poorly documented biological response.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Silicon pinhole strip defects and their impact on ATLAS Inner Tracker HV current measurement

In preparation for the High-Luminsity LHC (HL-LHC) [1], the ATLAS detector will undergo major detector upgrades, including the replacement of the current Inner Detector with the new all-silicon Inner Tracker (ITk) [2]. The ITk consists of a pixel detector close to the beamline surrounded by a large-area strip detector. During detector production, the electrical properties of silicon sensors and readout electronics must be characterized through a series of quality control (QC) and quality assurance tests. These tests ensure any defect is captured at the earliest possible stage. One such defect, callled a pinhole, occurs when the strip implant and the metal readout electrode are shorted through the intermediary dielectric layer. Notably, the introduction of pinholes during module assembly and pinhole effects on completed modules, especially on leakage current measurement circuitry, have never been studied. In this paper, we investigate the effect of such connections on the sensor leakage current measurements of completed modules and introduce new ways to locate pinholed strips. Here, with minor modifications to testing procedures, such defects are shown not to impede module testing or performance.

FOS: Physical sciences↗
DOE OSTI2023

Cost Benefit Analyses through Integrated Online Monitoring and Diagnostics (Final Report)

The objective of this research is to improve the economic competitiveness of advanced reactors through the optimization of cost and plant performance, which can be achieved by coupling intelligent online monitoring with asset management decision-making. As advanced reactors are early in the development life-cycle, online monitoring systems and associated sensor networks can be incorporated directly into the design without constraints related to retrofitting and system upgrades

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A Predictive Model for Internal Magazine Temperatures

This report describes data and methodology used to predict temperatures inside magazines used to store explosive devices, based on outside temperatures from a local weather station and data logged by sensors inside a single magazine over a limited period. It follows on earlier work and predictive models. In conjunction with aging models, predicted temperatures can be used to estimate degradation in the performance of explosives over long periods of storage.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

An Algebra of Machine Learners with Applications

Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra (A;⊕,Ⓧ), wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference Ⓧ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.

Rao, Nageswara↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2024

Digital twin advanced distribution management systems (ADMS) and methods

Advanced Distribution Management Systems not generally optimize over the entire feeder because there are few high-fidelity distribution circuit models and real-time distribution-connected sensors are rare. The limited observability at the distribution level makes it difficult to globally optimize distribution operations and issue control setpoints to power systems equipment or Distributed Energy Resources (DER) to perform grid-support services. For example, setpoints can be issued to DER based on results from an optimization module that incorporates a static or time-series feeder simulation. Feeder simulation initial conditions are populated with photovoltaic (PV) and load forecasts, state estimation results, and/or digital twin measurements or state output. The real-time (RT) digital twin runs a model of the feeder to generate state estimation pseudo-measurements since there are limited live feeder measurements.

Johnson, Jay Tillay↗
DOE OSTI2025

Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System With Machine Learning and Statistical Models

We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.

ARIMA↗
DOE OSTI2025

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗
DOE OSTI2025

Real-Time FPGA Implementation For Frequency Sweep Interferometry In The LBNF Complex

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, A. Jacob [Fermilab; Unlisted]↗
DOE OSTI2019

Use of Thermistor Temperature Sensors for Cyber-Physical System Security

The last few decades have seen a large proliferation in the prevalence of cyber-physical systems. This has been especially highlighted by the explosive growth in the number of Internet of Things (IoT) devices. Unfortunately, the increasing prevalence of these devices has begun to draw the attention of malicious entities which exploit them for their own gain. What makes these devices especially attractive is the various resource constraints present in these devices that make it difficult to add standard security features. Therefore, one intriguing research direction is creating security solutions out of already present components such as sensors. Physically Unclonable Functions (PUFs) are one potential solution that use intrinsic variations of the device manufacturing process for provisioning security. In this work, we propose a novel weak PUF design using thermistor temperature sensors. Our design uses the differences in resistance variation between thermistors in response to temperature change. To generate a PUF that is reliable across a range of temperatures, we use a response-generation algorithm that helps mitigate the effects of temperature variation on the thermistors. We tested the performance of our proposed design across a range of environmental operating conditions. From this we were able to evaluate the reliability of the proposed PUF with respect to variations in temperature and humidity. We also evaluated the PUF’s uniqueness using Monte Carlo simulations.

Internet of Things (IoT)↗
DOE OSTI2024

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8 fg mL –1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

DynamiteNSM

DynamiteNSM is a free Network Security Monitor (NSM), built on top of several leading, enterprise-grade technologies. The tool provides network and cybersecurity operators with holistic insights into their networks while giving them the ability to deep-dive into lower-level activities. The solution presents powerful dashboards, giving comprehensive view into performance and threat-based metrics. Dynamite-NSM can be easily deployed in different environments including high-speed data centers, small-to-large enterprises, IoT & industrial networks, and even at home. Dynamite-NSM handles massive volumes of network traffic through scalable ingestion and optimized network sensors. The solution includes two key components: the agent and the monitor. The agent analyzes and forwards network events, while the monitor processes incoming events and displays analytic information. The monitor component builds upon the OpenSearch stack and is coupled with network packet inspection by Zeek and Suricata. Dynamite-NSM is designed to be deployed very quickly with minimal configuration. Unlike many other tools, it can be installed and managed with a standalone command-line utility. The system is inherently passive without disruption to the network. There is no need to install agents on every computer, perform network scans, or directly interact with network assets.

Becker, Jamin↗
DOE OSTI2021

Chalcogenide Glass-Capped Fiber-Optic Sensor for Real-Time Temperature Monitoring in Extreme Environments

We demonstrate a novel chalcogenide glass (ChG)-capped optical fiber temperature sensor capable of operating within harsh environment. The sensor architecture utilizes the heat-induced phase change (amorphous-to-crystalline) property of ChGs, which rapidly (80–100 ns) changes the optical properties of the material. The sensor response to temperature variation around the phase change of the ChG cap at the tip of the fiber provides abrupt changes in the reflected power intensity. This temperature is indicative of the temperature at the sensing node. We present the sensing performance of six different compositions of ChGs and a method to interpret the temperature profile between 440 °C and 600 °C in real-time using an array structure. The unique radiation-hardness property of ChGs makes the devices compatible with high-temperature and high-radiation environments, such as monitoring the cladding temperature of Light Water (LWR) or Sodium-cooled Fast (SFR) reactors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Online System ID for Predicting Power Plant Performance Throughout Cycling Operations

This presentation represents a review of the background research conducted by NETL to apply artificial intelligence, i.e. auto-recursive algorithms and data analytics to detect leaks in utility scale boilers and laboratory power systems. The new project being funded by the Advanced Sensors and Controls Program is part of the Field Work Proposal funded in EY21 as Task 53 to demonstrate the application of these techniques on a utility scale power system.

Shadle, Lawrence↗
DOE OSTI2021

Field Experiments and Model Validation: The EGS Collab Project

Implementing enhanced geothermal systems (EGS) will require improvements in understanding stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The US Department of Energy (DOE) is addressing these and other challenges at multiple scales. The EGS Collab project, addressed here, is performing tests and modeling at the 10 m scale. The FORGE project is performing tests at the full reservoir scale. The EGS Collab team created an underground testbed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km to examine hydraulic fracturing (Experiment 1). We are currently building a second testbed at SURF at a depth of about 1.25 km aimed at investigating shear stimulation (Experiment 2). In Experiment 1, we drilled eight boreholes in a well-characterized phyllite and installed geophysical sensors in six of them to create a well-instrumented testbed to allow careful monitoring of stimulation events and flow tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results both in near-real-time and in detailed process studies. Stimulations performed in this testbed allowed quantification of processes occurring during stimulation and the examination of dynamic flow occurrences. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Our second testbed, targeted at shear stimulation, is currently being built at the SURF Facility at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1.

artificial intelligence↗
DOE OSTI2019

Experimental Assessment of a Novel Touchless Interface for Intraprocedural Imaging Review

Purpose: To examine the feasibility of a novel technology platform that enables real-time touchless interaction with radiology images in both a simulated and an actual clinical setting. Materials and Methods: This platform offers three different modes for image interaction. The gesture recognition mode uses a depth camera to detect the user’s hand gestures which are translated to image manipulation commands. The light projection mode uses the same camera to detect finger point-and-tap movements above the icons which are projected on a surface to activate the commands. The capacitive sensing mode is enabled by a handheld, portable device, over which finger movements are detected by capacitive sensors to control the image review. Following initial feedback, light projection and capacitive sensing modes were selected for further testing by comparing with the conventional mode of image interaction in time trials for performing a series of standardized image manipulation tasks. Finally, the usability of the technology platform was examined in actual clinical procedures. Results: The light projection and the capacitive sensing modes were evaluated in the time trials and exhibited 60% and 71% reduction in time, respectively, relative to the control mode (p < 0.001). Clinical feasibility for this platform was demonstrated in three actual interventional radiology cases. Conclusion: Accessing, navigating, and extracting relevant information from patient images intraprocedurally are cumbersome and time-consuming tasks that affect safety, efficiency, and decision-making during image-guided procedures. This study demonstrated that the novel technology addressed this issue by allowing touchless interaction with these images in the sterile field.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Polarization-Insensitive Medium-Switchable Holographic Metasurfaces

The adoption of metasurfaces has led to revolutionary advances in holography due to improved compactness, integrability, and performance. Switchable meta-holograms projecting different replay field images in a controllable manner are highly desirable. Still, existing technologies generally rely on the use of polarized light and additional optics to facilitate switching. Consequently, the potential benefits afforded through the use of metasurfaces are limited both by the system complexity and a fixed relationship between the optical input and output. In this manuscript, we demonstrate polarization-insensitive metasurfaces encoding arbitrary and independent holograms, which can be switched between by changing the refractive index of the infiltration medium while maintaining identical illumination conditions. Finally, by sidestepping the requirements for high-performance light sources, switching optics, or delicate alignment, this approach points toward ultracompact and cost-effective switchable meta-holograms for various practical applications, such as holographic image projection, eye-perceptible sensors, optical information storage, processing, and security.

36 MATERIALS SCIENCE↗