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DOE OSTI2019

PEDOT:PSS for Flexible and Stretchable Electronics: Modifications, Strategies, and Applications

Abstract Substantial effort has been devoted to both scientific and technological developments of wearable, flexible, semitransparent, and sensing electronics (e.g., organic/perovskite photovoltaics, organic thin‐film transistors, and medical sensors) in the past decade. The key to realizing those functionalities is essentially the fabrication of conductive electrodes with desirable mechanical properties. Conductive polymers (CPs) of poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) have emerged to be the most promising flexible electrode materials over rigid metallic oxides and play a critical role in these unprecedented devices as transparent electrodes, hole transport layers, interconnectors, electroactive layers, or motion‐sensing conductors. Here, the current status of research on PEDOT:PSS is summarized including various approaches to boosting the electrical conductivity and mechanical compliance and stability, directly linked to the underlying mechanism of the performance enhancements. Along with the basic principles, the most cutting edge‐progresses in devices with PEDOT:PSS are highlighted. Meanwhile, the advantages and plausible problems of the CPs and as‐fabricated devices are pointed out. Finally, new perspectives are given for CP modifications and device fabrications. This work stresses the importance of developing CP films and reveals their critical role in the evolution of these next‐generation devices featuring wearable, deformable, printable, ultrathin, and see‐through characteristics.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Automated Inspection of Criticality Control Overpacks for Surplus Plutonium Disposition: Qualification Update – 25313

In an effort to reduce the amount of nuclear waste in South Carolina, the Department of Energy (DOE) tasked the Savannah River Site (SRS) with diluting and disposing of the amount of plutonium in the state. This process involves the movement and shipment of over 100,000 criticality control overpacks (CCOs) throughout the project lifespan, lending itself to the use of automation to reduce worker radiation exposure and more efficiently utilize human capital. Due to the large scope, this overarching process was broken down into several different “automation projects” to be developed. The first opportunity pursued was the receipt and inspection of empty CCO drums coming into SRS, identified as Automation Project 1 (AP1), and is the focus of this paper. AP1 was developed to unpack incoming CCOs and inspect them for unwanted foreign objects and any damage to the drum or its contents. This process is accomplished by the combination of an automated guided vehicle (AGV) that delivers CCOs to a robotic arm which uses a suite of custom tools to disassemble a CCO, inspect the inside and outside of the CCO and its inner criticality control container (CCC), reassemble the CCC and CCO, and apply a tamper indicating device (TID) to the inspected drum. In past years, the robotic work cell had been developed in a small-scale testing facility for proof-of-concept. This year, major improvements were made to the robotic work cell to perform the process, including integration into the final facility where CCOs will be inspected. Other technical improvements include the implementation of sensor feedback and safety relays into the control system to allow the state of the work cell to be better tracked, and additional development of the TID application process to complete the robotic inspection. Further enhancements were made to the robotic vision processes and robot pathing, as well as development on a computer vision inspection process to detect inspection criteria anomalies in CCOs. In addition to developmental improvements, the work cell underwent a six-month testing period to ensure the project requirements were met. Results of this testing period demonstrate the work cell’s capability to meet project throughput goals at an acceptable level, successfully document the status of each CCO inspected, and reduce the toll on technical operations’ human power by two thirds. At the time of this paper, the work cell is capable of autonomously handling up to eight CCOs with an AGV, delivering CCOs to and from the robot work cell, and having a robotic arm perform a full receipt and inspection procedure on each CCO. Moving forward, repeatability will be improved so that these CCOs can be run back-to-back seamlessly, as well as improving the system to handle more significant edge cases and failure modes.

Spivey, Nicholas↗

DOE OSTI2016

Development of flexible, scalable, low cost readout for beam tests of the high granularity calorimeter for the CMS endcap

As part of the development of the High Granularity Calorimeter for the CMS Endcap at HL-LHC, The CMS collaboration is conducting a comprehensive series of beam tests. The first beam test, with a single HGC module was carried out in March of 2016 at the Fermilab Test Beam Facility, continuing to a 16 module test in July of 2016. We describe here the development of a low cost readout system that is simple to implement and is able to grow with the system under test. The system is based on the low cost Zynq SoC that allows simple DAQ development in a Linux environment. For this application we used the Digilent ZedBoard, which allows high speed LVDS links and Linux software development on a single commercial board. A small custom FPGA board designed to comply with the VITA 57 Field Programmable Mezzanine Card standard implements the interface to the readout ASIC mounted on the HGC sensor modules and provides the LVDS links to the ZedBoard, either directly over the FMC connector or via a custom carrier card. This architecture provides scalability, ease of development and low cost.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

High-Performance Piezoelectric MEMS Manufacturing & Application on Micro Power Generators (CRADA Final Report)

This project aimed to develop and characterize a micro-fabrication technology for wafer-scale heterogeneous integration of bulk piezoelectric materials on silicon substrates that could enable manufacturing of high-performance micro transducers. Typical technical challenges for piezoelectric microfabrication technologies are high-temperature processing, non-uniform film quality, and low process repeatability, which will be addressed during this project. Progress was made on preliminary research towards development of a technology platform that will enable a new micro-fabrication process to provide the highest electromechanical coupling among any other thin/thick-film deposition methods available today. This work has tested and characterized samples that we hope will further our goal of refining and enabling existing micro transducers to achieve unprecedentedly high performance. This project focused solely on the characterization of the proposed microfabrication process technology, and it did not consist of design or manufacturing of new transducers, such as sensors, actuators or energy harvesters based on this fabrication process. Future work will be aimed at the research and development of process steps specifically to enable integration of on-chip electronics with piezoelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Status Update on the Development of Transducers and Bonding Techniques for Enabling Acoustic Measurements of Damage in Microreactor Components

This report provides an overview of potential sensors and sensor-bonding techniques to enable the online acoustic interrogation of microreactor components and enhance structural health monitoring capabilities. The report focuses primarily on optical fiber–based acoustic sensors and describes initial experimental progress toward the deployment of these sensors for microreactor applications. The general approach is to monitor the resonant frequencies of microreactor components and search for evidence of structural defects that could indicate imminent failure. If properly identified, then the components could be repaired during the next reactor outage to prevent costly unplanned shutdowns. The ability to monitor the structural health of components could also reduce the need for time-consuming visual inspections and reduce staffing to improve microreactor economic viability. Increased sensor density is also one of the first steps to moving toward eventual semiautonomous operation. The expected microreactor conditions in which acoustic sensors must survive are characterized, including temperatures, neutron fluences, thermomechanical strains, and vibrational frequencies. Optical fiber–based acoustic sensors are identified as an attractive candidate for acoustic monitoring because of their high accuracy, immunity to electromagnetic interference, and resiliency in high-temperature, high-radiation environments. Optical fiber–based intrinsic sensors, such as type-II fiber-Bragg gratings and Fabry-Pérot Cavities (FPCs), are particularly attractive for a microreactor environment because of their high temperature stability, and FPCs also enable higher frequency interrogation with a lower sensitivity to radiation-induced drift. This report describes multiple interrogation systems, but the best interrogation system for a given situation will depend on the specific microreactor application, including the desired acoustic vibrational amplitudes, modes, and resonant frequencies. Initial experiments included fabricating three FPCs, tack-welding these FPCs to stainless-steel pipes or rods and performing room-temperature acoustic sensing tests to capture the vibrational frequency content. Peaks were identified in the measured frequency spectra and compared with the theoretical fundamental frequencies obtained from Euler-Bernoulli beam theory. Two of the three FPCs measured vibrational frequencies that generally matched those obtained from theory. Future work will include similar testing on pipes or other microreactor components with intentional flaws to evaluate the ability to determine changes in resonant frequencies. Finally, these tests will be repeated at high temperatures, potentially with an applied thermomechanical stress, to include environmental conditions similar to those for a microreactor application.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Thin-Entrance Window Sensors for Soft X-rays at LCLS-II

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

First Soft X-ray Quantum Efficiency Measurements on Microwave Annealed Thin-Entrance Window Sensors

Free electron lasers, such as SLAC’s LCLS-II, will provide unique scientific imaging opportunities. In order to fully utilize these facilities, we need to develop detectors with shallow entrance windows that will enable detection of soft x-rays from 250eV-1.5KeV. In addition, there are other light sources such as the FERMI, EuXFEL, and FLASH XFELs, as well as numerous synchrotrons such as the ALS, where soft x-ray science would benefit from detectors with higher quantum efficiency in the 20 eV to 1.5KeV range.A new microwave annealing technology provides an efficient way to achieve shallow entrance windows in fully depleted high-resistivity silicon sensors. Previously, SRP and SIMS measurements were used to characterize the shallow dopant profile, and sensors with the new entrance window process were deployed successfully to measure an Fe-55 x-ray spectrum. For the first time, we present quantum efficiency measurement for soft x-rays. Furthermore, the resulting estimate of the entrance window is 20nm for an arsenic implanted entrance window and 55nm for a boron implanted entrance window.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Surrogate Model of Flexible Research Platform EnergyPlus Models to Enable Sensitivity Analysis

This letter report describes the surrogate models developed from the EnergyPlus model of Oak Ridge National Laboratory’s Flexible Research Platform. Two data-driven black-box models were developed, and the outputs of the surrogate models were compared with the EnergyPlus model. The two models developed are a multilayer perceptron deep learning model, and a long short-term memory (LSTM) neural network model. The three factors for selecting the black-box models are scalability, computation time, and accuracy. A total of 107 input variables were the dominant variables in determining the outputs of building energy consumptions and thermal comfort. A total of 54 output variables were identified as the prediction targets, including the system- and zone-level outputs. The large set of the simulation cases were generated by integrating sensor errors into an emulator based on EnergyPlus and Python EMS, which includes advanced control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. The surrogate models were developed based on a set of large-scale simulation runs (i.e., 4,000 runs) on a cloud platform. The comparison analysis shows that the two black-box models had good accuracy for predicting new outputs for sensitivity analysis using the root mean square error metric. As a next step, the developed surrogate models will be used to perform sensitivity analysis for different sensor impacts (e.g., sensor types, sensor locations).

42 ENGINEERING↗

DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Thin-Entrance Window Process for Soft X-Ray Sensors

New free electron lasers, such as SLAC’s LCLS-II, will provide unique scientific imaging opportunities. In order to fully utilize these facilities, we need to develop detectors with shallow entrance windows that will enable detection of soft x-rays from 250 eV to 1.5 KeV. Achieving adequately shallow entrance windows is challenging because the high temperature anneal needed to activate the dopant also drives the dopant profile deeper, growing the region that is insensitive to soft x-rays. A new microwave annealing technology provides an efficient way to achieve shallow entrance windows in fully depleted high-resistivity silicon sensors. The microwave anneal technique can activate dopants at low substrate temperature, with minimal dopant diffusion, and can be used to fabricate both n-type and p-type entrance windows. SRP and SIMS measurements were used to verify dopant activation with negligible dopant diffusion. We then applied the microwave anneal process to a planar sensor wafer, using the new process to create the backside diode contact. Electrical test of the resulting sensors shows good reverse bias characteristics. The sensors have been bump-bonded to a read-out ASIC and used successfully to measure an Fe-55 x-ray spectrum. Process and device simulations were performed to characterize the quantum efficiency of the entrance window for soft x-rays. This technique is useful for other sensor applications requiring a shallow entrance window, including detectors for UV photons, low energy ions and low energy electrons.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Peptoid-Based Programmable 2D Nanomaterial Sensor for Selective and Sensitive Detection of H 2 S in Live Cells

During the past decades, a variety of two-dimensional (2D) nanosheets have been developed through supramolecular self-assembly. Among them, those assembled from sequence-defined molecules have received particularly attention because they enable a precise displaying of functional groups, including fluorescent dyes, within nanosheet surfaces. On the other hand, due to the fluorescence self-quenching, synthesis of organic 2D nanosheets exhibiting high fluorescence quantum yields is a significant challenge. Herein, we report the utilization of H 2 S-responsive probes as peptoid sidechains to design and synthesize crystalline 2D nanomembranes (2DNMs) as selective and sensitive H 2 S sensors. These 2DNM sensors exhibit a high quantum yield as a result of high crystallinity and tunable probe density. Compared with amorphous state of peptoid assembly and pre-assembled peptoid-probe conjugates, these crystalline 2DNM sensors exhibit a significantly strong fluorescence intensity and a high sensitivity (~ 0.36 nM). By sonication-cutting these 2DNM sensors into a colloidal form in aqueous solution, we further demonstrated the use of colloidal 2DNMs for detecting both exogenous and endogenous H 2 S inside cells or targeted cell organelles. As far as we know, this is the first example of using sequence-defined 2D nanomaterials for selective and sensitive detection of H 2 S. Because peptoids are biocompatible and peptoid-based 2DNMs are highly programmable, we expect that this new class of 2DNM sensors offer great potentials for detecting H 2 S and for biological applications.

2D nanomaterials↗

DOE OSTI2025

A Miniaturized, High-Bandwidth Optical Fiber Fabry–Perot Cavity Vibration Sensor Demonstrated up to 800 °C

A typical structural health monitoring technique involves measuring the vibrational characteristics of components or systems to detect signs of degradation or damage. Many industrial applications require engineered systems to safely operate under extreme, high-temperature environments that pose challenges not only to materials but also to sensors that would be used for structural health monitoring. Here, in this study, miniaturized optical Fabry-Perot cavities (FPCs) were developed and tested as a means of measuring the resonant frequencies of metal components that are most relevant to extreme-environment applications. Two of the three candidate FPC designs tested up to 800 ° C provided accurate measurements (validated by theoretical models and laser Doppler vibrometry) of the fundamental vibrational mode of the specimen to which each was bonded, although both sensors failed during thermal cycling. An analysis of the reflected optical spectrum from the FPC and X-ray computed tomography revealed two opportunities to improve the sensor reliability. First, the Cu optical fiber coating that was used could either be replaced with a more oxidation-resistant material or protected with commercially available films. Second, the adhesives used to bond the fibers to metal capillaries and establish the FPC could be replaced with a more robust solution, although the Resbond 907TS adhesive appeared to outperform Resbond 907.

Birri, Anthony [Oak Ridge National Laboratory (ORN↗

DOE OSTI2015

Minimizing User Burden in Building Energy Analysis

Infrared (IR) imagery can provide valuable data to building energy auditors and the developers of Building Energy Models (BEM). IR by itself is not a panacea to the challenges facing energy auditors and modelers, but when combined with other techniques and Open Studio software, IR can play an important role in the minimization of the burden of creating building energy models and the diagnosing of building energy problems. While IR can be used to detect and quantify many aspects of a building’s thermal and energy performance, in Phase I we focused on a single capability: determining the R value of exterior walls. Two key attributes of using IR methods for R value determination is that IR methods measure the actual, installed performance in-situ – as opposed to relying on manufacturer or handbook values – and they can integrate performance over a wide area and thus can accurately account for thermal bridging and spatial anomalies such as “spotty” insulation installation. There is a trade between user burden and accuracy when using IR to determine a wall’s R value. The least burdensome approach is computing R values from a single IR snapshot. The most burdensome method we considered was to combine IR images with thermocouple and heat flux sensor (HFS) measurements made over the course of several days. The more effort users expend in making the measurements, the more accurate and robust the method becomes. Through a series of field tests at the ThermoAnalytics Inc. (TAI) HQ at Calumet, MI and a simulation of a 1980s era office building in Flagstaff, AZ we investigated methods to compute the R value of exterior walls using data from IR cameras, thermocouples and heat flux sensors. To compute R values the heat flux through the wall, along with temperatures on either side of the wall, must be known, modeled, or estimated – and the temperature difference through the wall should be at least 10 C. If only outdoor IR images are used in the method, the inside ambient air temperature must be estimated, and a simulation model created to predict heat fluxes and the indoor wall temperature. The accuracy of the resulting R value depends on the accuracy of the simulation and the assumptions it employs. Applying thermocouple sensors, either as iButtons or as part of a wireless sensor network, to the walls allows for the acquisition of temperatures over time. The longer the duration of the measurements, the more accurate and robust the method will be. Even with this level of measurement effort, modeling is still required to calculate the heat flux through the wall. The addition of a heat flux sensor alleviates the need for any modeling, and that results in improved accuracy. Driving the accuracy of the methods are several factors that can be quantified. How well are the wall surface temperatures known? Modeling the heat flux through walls requires knowing the details of the surrounding radiant environment and the heat transfer due to convection. How well are these boundary conditions known? Even when the heat flux is measured, error can arise when some of the measured heat is not conducted through the wall but rather goes into the thermal mass of the wall. Thermal mass effects occur when conditions and the heat flux values are fluctuating. A technique that is not robust will suffer in accuracy when these fluctuations occur. The R value determination method must be incorporated into the Open Studio process. In our proposed process, IR images and other measured sensor data are input into a module that would send R value estimates to a building library developed by team member Skidmore, Owings, & Merrill (SOM). This library of parameterized building models would use these R values along with information about the building’s age, type, and location to query the Open Studio BCL to identify the most likely constructions for walls, roof, and windows. Being based on the IMF feature of Energyplus, the SOM building library can quickly produce Energyplus models of buildings based on a limited number of user inputs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Novel Energy-Efficient Drying Technologies for Food, Pulp and Paper, and other Energy Intensive Manufacturing Industries

The purpose of this project was to develop innovative dryer technologies for the food and pulp & paper industry sectors to reduce their corresponding drying energy by at least 25%. By working on these novel drying technologies and the aid of smart sensors and a physics-based artificial intelligence methodology, the foundation for the next generation energy efficient smart dryers for the food and pulp & paper industry sectors have been developed. The innovative dryer technologies developed are applicable to other energy intensive industries as well.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Technical Assessment of the Application of Digital Twin and Prognostic Tools for Condition Monitoring

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to present use cases of the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components (SSCs). The advanced technologies considered in this work, collectively referred to as digital twin (DT) technologies, are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), and physics-based models. The report presents two use cases of reactor coolant pumps (RCPs) and heat pipes in nuclear power plants (NPPs) with technical and regulatory considerations and opportunities in using advanced technologies for conditional monitoring. Key findings from the exploration of these considerations are as follows: - Uncertainties in sensor data and model predictions must be rigorously addressed through validation and verification processes - Regulatory compliance is paramount, necessitating data driven models to be developed in line with existing codes and standards, as well as considering potential future guidelines for advanced reactors - Explainability and transparency in ML/AI models are essential for developing operator trust and regulatory review, including methods that enhance the interpretability of complex data-driven predictions - Condition monitoring programs must be evaluated for their effectiveness in reducing maintenance-preventable function failures (MPFF) and aligning with plant performance criteria - The deployment of advanced technologies for condition monitoring could lead to a transition from periodic to continuous monitoring, thereby optimizing maintenance schedules - Collaborative efforts between industry stakeholders, regulatory bodies, and technology developers are crucial for the successful adoption of advanced technologies for condition monitoring systems in nuclear facilities In summary, the introduction of advanced technologies into condition monitoring programs represents a significant leap forward in the domain of NPP maintenance. By harnessing the capabilities of advanced sensors, data analytics, and ML/AI, NPP operators can transition from a time-based to a condition-based maintenance approach. This shift can potentially enhance the reliability and safety of critical plant components while optimizing maintenance efforts and minimizing unnecessary outages. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of inservice inspection and inservice testing (ISI and IST) programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Integrated Harsh Environment Gas / Temperature Wireless Microwave Acoustic Sensor System for Fossil Energy Applications

There is a significant need for sensors capable of detecting gases, such as H 2 , O 2 , NO x , SO x within harsh environments encountered in power plants, industrial manufacturing, oil and gas exploration, and aerospace applications. This project successfully demonstrated the use of wireless microwave acoustic sensor technology for the detection of gases (H 2 or O 2 ) from ambient temperatures up to 650°C. The work focused on langasite (LGS) based surface acoustic wave resonator (SAWR) sensors as the harsh-environment sensor platform and explored multiple combinations of high-temperature thin films and device structures which were used to increase the sensor platform stability and detection capability at temperatures in the operational range of 150°C to 700°C. Specific material configurations that were investigated include: yttria-stabilized-zirconia (YSZ) decorated with Pt nanoparticles, atomic layer deposited (ALD) Al 2 O 3 , palladium, and Pt/Al 2 O 3 co-deposited electrode alloys. Through the deposition of YSZ at temperatures as high as 850°C and the use of graded alloy concentrations of Pt in the fabrication of the Pt/Al 2 O 3 electrode structures, film stress problems were mitigated, and sensor operation and stability achieved. To test and evaluate SAWR sensor performance for the detection of H 2 and O 2 under the influence of temperature variations, a comprehensive gas sensor control system and test apparatus was created to operate within a laboratory box furnace-controlled environment. In addition to the advancement in thin film materials through the deposition and fabrication techniques mentioned above, the work characterized the performance of sensors containing these films in the presence of oxidizing and reducing gases between 25°C and 700°C. In particular, the work revealed that the exposure of the SAWR sensor surfaces to oxidizing environments significantly improve the sensor response to H 2 , whereas the exposure of the sensor to reducing environments at high temperatures (≈ 500°C) renders the sensor irresponsive to H 2 , requiring sensor surface treatment at high temperatures (above 500°C) to recover the responsiveness to H 2 . The SAWR sensors have been also tested for wireless operation and array operation using multiple orientations to resolve the detection of gases under temperature variations. The work developed at the University of Maine was aided by a collaboration with the NETL Research and Innovation Center, Pittsburgh, PA, where thin film materials and device structures fabricated at UMaine were tested and characterized using NETL gas reactors and surface analysis techniques. SAWR sensors fabricated at UMaine were exposed multiple times to temperatures up to 700°C and H 2 concentrations up to 100% in the NETL facilities to measure the sensor performance. Environetix Technologies Corporation, a UMaine harsh-environment sensor spin-off company, also provided support and assistance in sensor system testing and implementation. The sensor small size and configuration allows flexible sensor placement and embedding of multiple sensor arrays into a variety of components within power systems and other aerospace or industrial settings that need to be interrogated wirelessly. The SAW platform is an attractive option for high-temperature harsh-environment gas sensing applications due to its inherent features, namely small size, capability of battery-free and wireless operation, and cost effective scale production using well-established production techniques from the semiconductor industry. The research findings achieved in this work, particularly advances regarding the fabrication and performance of the SAWR gas sensor platform, can be adapted and transferred to industrial power plants and other harsh environments.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗