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DOE OSTI2022

Mu2e DAQ and slow control systems

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the feld of an aluminum nucleus with a sensitivity improvement by a factor of 10,000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, fltering, and processing. otsdaq is an online DAQ software suite with a focus on fexibility and scalability and provides a multiuser, web-based, interface accessible through a web browser. The data stream from the detector subsystems is read by a software flter algorithm that selects events which are combined with the data fux coming from a Cosmic Ray Veto System. The Detector Control System (DCS) has been developed using the Experimental Physics and Industrial Control System (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS System has been integrated into otsdaq. A prototype of the TDAQ and the DCS systems has been built at Fermilab’s Feynman Computing Center. In this paper, we report on the progress of the integration of this prototype in the online otsdaq software.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2005

System Developed for Real-Time Blade-Flutter Monitoring in the Wind Tunnel

A real-time system has been developed to monitor flutter vibrations in turbomachinery. The system is designed for continuous processing of blade tip timing data at a rate of 10 MB/sec. A USB 2.0 interface provides uninterrupted real-time processing of the data, and the blade-tip arrival times are measured with a 50-MHz oscillator and a 24-bit pipelined architecture counter. The input stage includes a glitch catcher, which reduces the probability of detecting a ghost blade to negligible levels. A graphical user interface provides online interrogation of any blade tip from any light probe sensor. Alternatively, data from all blades and all sensors can be superimposed into a single composite scatter plot displaying the vibration amplitude of each blade.

Kurkov, Anatole P.↗

DOE OSTI2021

Data acquisition and slow control interface for the Mu2e experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The data stream from the detector subsystems is read by a software filter algorithm that selects events which are combined with the data flux coming from a cosmic ray veto system. The Detector Control System (DCS) has been developed using the Experimental Physics and Industrial Control System (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS system has been integrated into otsdaq. A prototype of the TDAQ and the DCS systems has been built at Fermilab's Feynman Computing Center. In this study, we report on the progress of the integration of this prototype in the online otsdaq software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2010

Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, fault diagnosis, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of fault diagnosis.

Dwyer, Matthew B.↗

DOE OSTI2025

Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach

Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, which masks one variable at a time to predict the others, learning underlying process correlations. Detection performance was assessed with synthetic and experimental data, while identification performance used only synthetic data due to its ability to generate labeled anomaly types. For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. However, the comparative methods required selecting a latent size, and these conclusions pertain to using the optimal size. In practice, it would not be feasible to always select the optimal value, and incorrect selections impacted performance. In contrast, the LOVO model does not require a latent space. For identification using synthetic data, the LOVO model was slightly outperformed in interpretability and repeatability but still demonstrated impressive results. These outcomes suggest that the LOVO model is an effective model and may be more easily implemented without the challenging tuning process of selecting a latent size.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Trigger-DAQ and Slow Controls Systems in the Mu2e Experiment

The muon campus program at Fermilab includes the Mu2e experiment that will search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus, improving by four orders of magnitude the search sensitivity reached so far. Mu2e's Trigger and Data Acquisition System (TDAQ) uses otsdaq as its solution. Developed at Fermilab, otsdaq uses the artdaq DAQ framework and art analysis framework, under the-hood, for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability, while providing a multi-user, web-based, interface accessible through the Chrome or Firefox web browser. The detector Read Out Controller (ROC), from the tracker and calorimeter, stream out zero-suppressed data continuously to the Data Transfer Controller (DTC). Data is then read over the PCIe bus to a software filter algorithm that selects events which are finally combined with the data flux that comes froma Cosmic Ray Ve to System (CRV). A Detector Control System (DCS) for monitoring, controlling, alarming, and archiving has been developed using the Experimental Physics and Industrial Control System (EPICS) Open Source Platform. The DCS System has also been itegrated into otsdaq. The installation of the TDAQ and the DCS systems in the Mu2e building is planned for 2021-2022, and a prototype has been built at Fermilab's Feynman Computing Center. We report here on the developments and achievements of the integration of Mu2e's DCS system into the online otsdaq software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Integration of Condition-Based, Diagnostic, Prognostic, And Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context

Reliability data employed in plant reliability models are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating actual health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). This paper proposes a reliability modeling approach that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. We show how state-of-the art condition-based, diagnostic, prognostic, and anomaly detection models can be linked to system reliability models not in probability terms, but in terms of margin where margin is defined as the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Then, we show how the propagation of margin data from the asset to the system level is performed through classical reliability models such as fault trees or reliability block diagrams. The described method is in fact able to propagate heterogenous health data from the asset to the system level in order to analytically assess system health.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

A Review of Diagnostic Techniques for ISHM Applications

System diagnosis is an integral part of any Integrated System Health Management application. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, fault propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic , models of the nominal system, derived from design documents, are also employed for fault isolation and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve very complex analysis routines, such as signal processing, learning or classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule-based expert systems, case-based reasoning systems, model-based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches ta diagnostic reasoning. Many engineering disciplines have specific approaches to modeling, monitoring and diagnosing anomalous conditions. Therefore, there is no "one-size-fits-all" approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers and maintenance teams. While the emphasis of this paper is automation of health management functions, striking the correct balance between automated and human-performed tasks is a vital concern.

Patterson-Hine, Ann↗

DOE OSTI2023

Laboratory Instrument Software Controlled Spread Spectrum Time Domain Reflectometry for Electrical Cable Testing

This research discusses development of a software-controlled laboratory instrument based spread spectrum time domain reflectometry system (SSTDR). This constitutes one task within PNNL’s Light Water Sustainability Program (LWRS) whose mission includes advancing nondestructive examination (NDE) techniques for off-line and on-line in-situ cable condition monitoring. In 2022, PNNL evaluated SSTDR for detection and characterization of a number of cable anomalies (Glass et al. 2022). The review included comparison of SSTDR to Frequency Domain Reflectometry (FDR) techniques which have enjoyed encouraging feedback and are starting to be used in nuclear power plants for periodic cable condition monitoring of cable systems as part of the plant’s overall cable aging management program. The FDR test introduces a broad-band chirp onto the cable at the cable end then listens for any reflection from a change of impedance along the cable caused by a damaged conductor or insulation, splices, contact with moisture, or other cable anomalies. The signal is captured in the frequency domain then transformed back to the time domain using an inverse Fourier transform (IFT). Based on the velocity of propagation, the impedance response signal is plotted against distance along the cable. Peak locations along the X-axis indicate the distance along the cable where a portion of the signal has been reflected back to the instrument as a result of a cable anomaly. The FDR test is considered the gold standard of reflectometry however it does require the cable to be de-energized to perform the test. The LIVEWIRE commercial SSTDR produces a similar plot to the FDR however all processing is in the time domain. A pseudo-random noise code (PN code) is input onto the cable conductor and the instrument listens for any reflected response from cable anomalies. The SSTDR processes the signal as an autocorrelation comparing the input PN code to any reflected signal detected. The autocorrelation analysis for thermal aging, water and water ingress detection, ground fault and phase-to-phase fault detection at various locations along the cable and with the cable attached and detached from a motor load, and on both energized and un-energized conditions were performed. These results were contrasted to Frequency Domain Reflectometry (FDR) measurements of the un-energized cable. Results were encouraging but indicated more work was warranted – particularly with the SSTDR, it seemed that the insulation damage would likely be better evaluated with multiple bandwidth cable tests particularly including larger bandwidths than were possible with the current commercial instrument. The commercial instrument’s bandwidth was set at 6, 12, 24, and 48MHz but note that SSTDR and FDR definitions of bandwidth trend similarly but are not the same. The FDR response could be more broadly adjusted, and the bandwidth of 100 to 500 MHz produced the best responses. FDR responses to anomalies were clearer than SSTDR responses and indications were that a broader bandwidth SSTDR may lead to improved SSTDR detection capability. This project used a laboratory instrument based SSTDR (primarily using an Arbitrary Waveform Generator (AWG) and a digital oscilloscope plus Python in-house software) that allowed software adjustment of the SSTDR bandwidth, window functions applied to the exciting Pseudo-random Noise (PN) code plus and other aspects of the SSTDR signal processing. Hereafter, this will be referred to as the PNNL SSTDR. Evaluating specific performance of the PNNL SSTDR is left to a separate report. This report documents hardware and software development to produce the SSTDR cable test system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2004

Multi-Sensor Distributive On-Line Processing, Visualization, and Analysis Infrastructure for an Agricultural Information System at the NASA Goddard Earth Sciences DAAC

The Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC) Distributed Active Center (DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM On-line Visualization and Analysis System precipitation and other satellite data products and services. AIS outputs will be ,integrated into existing operational decision support system for global crop monitoring, such as that of the U.N. World Food Program. The ability to use the raw data stored in the GES DAAC archives is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. To gain this understanding is a time-consuming process and not a productive investment of the user's time. This is an especially difficult challenge when users need to deal with multi-sensor data that usually are of different structures and resolutions. The AIS has taken a major step towards meeting this challenge by incorporating an underlying infrastructure, called the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni," that integrates various components to support web interfaces that ,allow users to perform interactive analysis on-line without downloading any data. Several instances of the Giovanni-based interface have been or are being created to serve users of TRMM precipitation, MODIS aerosol, and SeaWiFS ocean color data, as well as agricultural applications users. Giovanni-based interfaces are simple to use but powerful. The user selects geophysical ,parameters, area of interest, and time period; and the system generates an output ,on screen in a matter of seconds.

Teng, William↗

DOE OSTI2021

Prototype Data Acquisition and Slow Control Systems for the Mu2e Experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10 000 over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable with direct searches at either present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system exploits otsdaq as its online Data Acquisition System (DAQ) solution. Furthermore, developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The read out controllers (ROCs) stream out zero-suppressed data continuously from the detector subsystems to the data transfer controllers (DTCs). The data stream is then read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events which are combined with the data flux coming from a cosmic-ray veto (CRV) system. The detector control system (DCS) has been developed using the experimental physics and industrial control system (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS has been integrated into otsdaq. A prototype of the TDAQ system and the DCS has been built at Fermilab's Feynman Computing Center. In this article, we report on the progress of the integration of this prototype in the online otsdaq software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations

Parallel computing plays a pivotal role in the efficient processing of large-scale graphs. Complex network analysis stands as a capti- vating research frontier, holding promise across diverse scientific domains such as sociology, biology, online media, and recommenda- tion systems. In this era, Machine Learning (ML) and Deep Learning (DL) have emerged as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around advancing parallel algorithms tailored for large-scale graph operations. To achieve this, I harness the power of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on the High-Performance Computing (HPC) platforms to unlock optimal performance. I also apply ML/DL techniques to HPC operational data, to streamline the monitoring and maintenance of supercomputers, alleviating the complexities associated with their upkeep and enhancing user support. My research echoes the syn- ergy between parallel computing, large-scale graph analysis, and ML/DL, improving computational efficiency and user experience.

Sattar, Naw Safrin↗

DOE OSTI2022

Large-Scale Additive Manufacturing of Silicon Carbide with Process Monitoring

This report summarizes new capabilities in additive manufacturing (AM) of large-scale silicon carbide (SiC) components under the Advanced Materials and Manufacturing Technologies (AMMT) program. SiC is a promising material that is being considered for many advanced reactor designs due to its high temperature strength, radiation tolerance, minimal neutron absorption, and oxidation resistance [1-8]. One of the primary limitations to using SiC as an in-core structural material is the inability to fabricate large SiC components with complex geometries. While under the former Transformational Challenge Reactor (TCR) program, ceramic AM systems at the Oak Ridge National Laboratory (ORNL) were limited in the quantity and overall size achievable for producing ceramic AM components. To address this, ORNL has improved its infrastructure for binder jet and chemical vapor infiltration (CVI) processes with cutting-edge equipment coming online in a newly renovated laboratory space capable of producing larger components and higher throughput without compromise on part size. This report details the laboratory renovation progress, focusing specifically on the DesktopMetal (formerly ExOne) X25Pro binder jet, a large scale CVI furnace, and the added functionality of a software tool called Peregrine for quality assurance and control purposes.

36 MATERIALS SCIENCE↗

NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗

DOE OSTI2020

ENERGY STAR for Tenants: An Online Energy Estimation Tool for Commercial Office Building Tenants

The commercial building sector consumes over 18% of the total energy in the United States. According to the 2012 Commercial Building Energy Consumption Survey (CBECS), office buildings comprise nearly 16 billion square feet of floor space and consume 253 billion kWh of energy annually. Office buildings represent nearly one-fifth of the energy consumed by commercial buildings, more than any other building type. Therefore, monitoring and reducing energy consumption in office buildings has become an increasingly important focus across the United States. The goal of the ENERGY STAR for Tenants program is to recognize office building tenants who demonstrate commitment to energy efficiency and environmental stewardship. Tenants seeking recognition must complete five major steps: estimate energy use, meter energy use, use efficient lighting, use efficient equipment, and share data. NREL is responsible for the underlying analysis performed for step one, estimating energy use. An online survey was developed for tenants to input information about their office space and its internal loads. NREL performed a parametric analysis across a large parameter space of office buildings. The analysis results were processed to map the user inputs to output energy ranges. The mappings enable the web tool to provide users with instant energy usage estimates based on a limited number of inputs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

Diao, Xiaoxu (ORCID:0000000346726352)↗

DOE OSTI2024

Leveraging Calibration Transfer Techniques for Remote Monitoring of Samarium and Europium in LiCl Using Laser-Induced Florescence Spectroscopy for Radioisotope Production Applications

Radioisotope production relies on complex chemical processes that must be performed in radiological hot cells or glove boxes because of the radioactive and otherwise hazardous materials being used. In these situations, optical sensors can provide real-time monitoring to users, which is unobtainable by more traditional methods. This study explores the use of calibration transfer methods to train a model on one instrument and date and then transfer it to another instrument of the same or different configuration on a different date. By performing laser-induced fluorescence measurements of Eu(III) and Sm(III) in 10 M LiCl over the course of 6 months using two disparate spectrometers and two different training sets, a strategy for calibrating and deploying models for online monitoring was established. Three transfer techniques were compared: direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). DS and PDS outperformed EPO for day-to-day transfers, and EPO was not effective for instrument-to-instrument transfers. Transferring the initial date’s full factorial model provided better prediction performance compared with retraining models the day of measurements using a D-optimal designed calibration set. For both day-to-day and instrument-to-instrument transfers, five Kennard–Stone selected samples were sufficient. Based on this choice, the initial-date, high-resolution spectrometer model was transferred to a lower-resolution, compact spectrometer 6 months later to monitor a simulated, real-time demonstration. Here, the combined predictions of the DS and PDS transferred models were able to accurately track the anticipated concentration profiles, maintaining root-mean-square error of prediction values below 10 ppm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Integrating Data Sources for Improved Safeguards and Accountancy of Electrochemical Fuel Reprocessing Systems. Final Report

Electrochemical reprocessing (also commonly known as “pyroprocessing”) of used nuclear fuel is an alternative to aqueous reprocessing that confers a number of advantages, including the ability to process more recently-discharged fuel, smaller resultant waste volumes, and the lack of isolation of plutonium in the product stream. While electrochemical reprocessing systems have seen a significant research and development effort, nuclear safeguards and security of these systems remains under-developed, particularly given the significant differences in operating environment and process flowsheet compared with established aqueous methods. Objective 4 of DOE’s nuclear energy research and development activities in Nuclear Energy Research and Development Roadmap is: “Understand and minimize the risks of nuclear proliferation and terrorism.” Material Control & Accountancy (MC&A) programs at nuclear processing plants deter and detect theft and diversion of nuclear material by both outside and inside adversaries. Empirical modeling and data analytics of online measurements and state indicators can improve the timeliness of detection and reduce the uncertainty in accountancy measures. To support the development and demonstration of such methods, data are needed that capture expected measurement values collected throughout electrochemical reprocessing facilities. A review of the available literature of current research in measurements for electrochemical reprocessing systems identified twenty-two candidate measurement methodologies, which characterize fuel/salt composition, concentrations of various isotopes and elements, and operational parameters throughout the reprocessing facility. Four primary measurement methodologies have been simulated based on either physical simulations or empirical correlations: gamma emissions, neutron emissions, hybrid k-edge densitometry (HKED), and cyclic voltammetry. Initial investigation into the use of these measures for process monitoring and detection suggests the approach is viable and worthy of further investigation. This report summarizes efforts to generate signatures of key measurements during operation of electrochemical reprocessing facilities and initial investigation of data analytics to support process monitoring, diversion detection, and accountancy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗