Real-Time, Radiation-Hardened Video System for Operational Monitoring in Nuclear Reactor Vessels Phase I Final Report
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11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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Results for “monitoring system”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Power Electronics Based Self-Monitoring and Diagnosing for Photovoltaic Systems
Faults in photovoltaic (PV) systems can seriously affect the efficiency, energy yield, cost, safety, and reliability of PV plants. Condition monitoring of PV plants is, therefore, a very important approach to estimating the health condition of PV modules and power electronics in the system. However, additional hardware for PV system monitoring adds cost to the system's operation; delayed maintenance service also causes additional energy production loss. The Center for Power Electronics Systems (CPES) at the Virginia Polytechnic Institute and State University and Siemens Cooperate Research developed the online impedance measurement for a PV panel self-monitoring and diagnosing technology using the DC-DC converter connected to the panel. Small-signal impedances of a monocrystalline silicon PV panel were modeled and simulated to reflect fault conditions such as the short-circuit, hot-spot, and junction box faults. Modeling and simulation results were validated firstly using a test setup consisting of a solar simulator, a network analyzer, small-signal injectors, and a monocrystalline PV panel rated at 300 W.
14 SOLAR ENERGY↗
Opdefender Network Monitoring And Control System
OpDefender is a new system designed to enhance the cyber security of control system networks. It accomplishes this by inspecting each network packet using a novel, patent-pending method that extends software defined networking into the application/ICS protocol layer. OpDefender consists of two key components: 1. Smart, “ICS-aware” network switches that analyze and filter network traffic in real time. 2. A network management human machine interface (HMI) that allows an operator to monitor and control network traffic in real time. The “ICS-aware” switches can serve as a drop-in replacement for typical network switches, or they can be used in conjunction with existing network switches. OpDefender is configured and controlled in real time via a custom-built, web-based HMI designed with the operator in mind.
Johnson, BriamE.↗
Multi-Sensor Data Acquisition System for Process Monitoring
Idaho National Laboratory is building a new nuclear fuel cycle test bed. This fuel cycle test bed named “Beartooth” will give researchers the opportunity to study the nuclear fuel cycle process. The process operations include the use of centrifugal contactors and process flow for the purification of special nuclear material recovery in used fuel. This new research facility has the need to use non-traditional measurement sensors to determine the state-of-health of the solvent extraction process. These non-traditional sensors can also enhance nuclear nonproliferation supervision and other activities. The non-traditional sensors include accelerometers, acoustic, current, flow, colorimetric, temperature, pH and conductivity. The challenge behind deploying all these sensors, is the need for fast and reliable data collection. The data acquisition system (DAQ) needs to be capable of recording up to 12 accelerometers and/or acoustic microphones simultaneously at rates up to 12.8 kSamples/second/channel. This requires the need for a strong architecture and data collection solution. This summary identifies the system architecture, DAQ, and sensors needed to support non-traditional measurements in a solvent extraction process.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Creation of Laboratory-scale Testbed for Autonomous Monitoring and Control of Subsurface Systems
This report describes the results of an LDRD project aiming to develop a laboratory-scale testbed for autonomous subsurface monitoring and control systems. Modifications were made to an existing polyaxial loading frame to incorporate electrical resistivity tomography (ERT) and acoustic emission (AE) monitoring systems. This allows engineered subsurface processes to be studied under realistic temperatures and stresses in a scaled-down laboratory environment with similar monitoring systems as are deployed in field-scale systems. The paper outlines some challenges that were faced in implementing the ERT system at the laboratory scale and the solutions that were devised. Results are shown from two demonstration tests—one focusing on the ERT system, and one focusing on the AE system.
42 ENGINEERING↗
Development and Commercialization of an IDAES-Based Power Plant Performance Monitoring and Optimization System
DOE and NETL have created an advanced, open-source computational platform through the Institute for the Design of Advanced Energy Systems (IDAES). The IDAES platform is a very extensive modeling environment that can be used for a broad range of power plant and process applications. MapEx Software is developing and commercializing a software application that makes it easier to set-up and run IDAES-based analyses. The MapEx-developed software application will replace the need for custom Python language coding with a user-friendly graphical user interface where the user can construct a flowsheet diagram of the IDAES model by inserting icons representing the plant equipment onto the screen. This application will make the implementation of modeling and optimization of existing fossil-fired power plants more straight-forward and less time-consuming. The effort focuses on performance monitoring and optimization of plant operations for the existing coal-fired power plant fleet but is built on a structure that allows expansion into the broad range of applications where IDAES methods may be applied.
20 FOSSIL-FUELED POWER PLANTS↗
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.
43 PARTICLE ACCELERATORS↗
Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)
The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.
97 MATHEMATICS AND COMPUTING↗
Event-driven receding horizon control for distributed persistent monitoring in network systems
Not Available
Welikala, Shirantha↗
Greedy initialization for distributed persistent monitoring in network systems
Not Available
Welikala, Shirantha↗
Monitoring of Photovoltaic System Performance Using Outdoor Suns-VOC
Not Available
Killam, Alexander C.↗
Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach
As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.
97 MATHEMATICS AND COMPUTING↗
Real-time series resistance monitoring in photovoltaic systems
A device includes at least one processor configured to determine a target irradiance value based on an operating current value of a photovoltaic (PV) device, a short-circuit current value of the PV device, and an operating irradiance value of the PV device. The at least one processor is also configured to determine an open-circuit voltage value of the PV device at the target irradiance value, determine a series resistance value of the PV device based on an operating voltage value of the PV device, the operating current value, and the open-circuit voltage value at the target irradiance value, and execute at least one programmatic action based on the series resistance value.
14 SOLAR ENERGY↗
Memory object tagged memory monitoring method and system
Described are a method and processing apparatus to tag and track objects related to memory allocation calls. An application or software adds a tag to a memory allocation call to enable object level tracking. An entry is made into an object tracking table, which stores the tag and a variety of statistics related to the object and associated memory devices. The object statistics may be queried by the application to tune power/performance characteristics either by the application making runtime placement decisions, or by off-line code tuning based on a previous run. The application may add a tag to a memory allocation call to specify the type of memory characteristics requested based on the object statistics.
Roberts, David A.↗
Wireless, Distributed Data Acquisition and Interpretation System for Seismic Monitoring and Carbon Storage Site Characterization
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Kalantari Khandani, Mehdi↗
Development of Autonomous Nodal Electromagnetic Measurement System for Geophysical Monitoring of Geologic Carbon Storage Processes, Final Report
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CCUS Co2 Monitoring Electromagnetics↗
Numerical Studies of the On-Line Reactivity Monitoring Techniques in Subcritical Systems: Revisit of the Area-Ratio Method, the Neutron Source-Modulation Method, and the Flux-to-Current Ratio Method
In the subcritical systems, the subcriticality level must be monitored continuously to ensure the safe operation of the systems. Techniques such as the neutron source-modulation method and the flux-to-current ratio method have been proposed for on-line reactivity measurements of the subcritical systems. However, the accuracy of these methods is unknown in the actual applications. In this report, the fundamental concepts of these techniques are visited, with numerical simulation techniques developed to validate these on-line reactivity monitoring methodologies. Specifically, the Monte Carlo numerical simulation results show that the neutron source-modulation method is not accurate in determining the reactivity of the subcritical systems. The flux-to-current ratio method can provide on-line reactivity measurements, and its spatial effects have been studied and well understood through the numerical simulations of the fuel loading of the neutron source facility of Ukraine. In addition, a modified version of the area-ratio method is proposed for the on-line measurements of the absolute reactivity of the subcritical systems. Monte Carlo numerical simulations showed that this method can provide the absolute reactivity of the subcritical systems within ~5% error, if the neutron detectors are placed close to the fuel assembly interface with the reflector zone or in the reflector zones away from the neutron source.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗