2017 Summer research
This presentation provides an introduction to Wave Energy Converters (WEC); describes impedance measurements gathered through piezoelectric transducers, laboratory measurements and algorithms created.
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Results for “Computing, Instrumentation”
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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An Extended Study on the Uncertainty Quantification of a Photodiode's Spectral Responsivity
An extended study was done on the analysis procedure for spectral responsivity of a silicon detector from the range of 200 nm to 1100 nm for the Optical Component Calibration laboratory at Livermore Operations in Livermore California. As a result, the uncertainty of the procedure was quantified to account for variables that contribute to uncertainty; such as the reproducibility and repeatability of the working standards, quantifying Type A and B uncertainties, validating laboratory equipment and associated software, and looking at environmental variables. The findings lead to a revised analysis reporting procedure that reports uncertainties depending on three spectral ranges, 300 nm to 400 nm, 400 nm to 700 nm, and 700 nm to 1100 nm. A proficiency test was done to compare the difference between both methods and a noticeable difference was found. The results showed that the previous reporting method over estimated uncertainty as much as 3.5% for the 300 nm to 400 nm range, 4% in the >400 nm to 700 nm range, and 2% in the >700 nm to 1100 nm range. The findings lead to the use of the revised procedure when producing calibration certificates, better quantified measurement uncertainties, and will be used for the process of updating the scope of accreditation from the National Voluntary Laboratory Accreditation Program.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Comparative Assessment of Experimental Testing of Instrument with an Embedded Digital Device Using Model-Based and Conventional Methods
The use of digital equipment has not been widespread in U.S. nuclear facilities. A major contributing factor to this situation arises from regulatory concerns about common cause failures (CCFs) stemming from the use of identical software in redundant systems, questions as to how to quantify the reliability of software-based products, validity of tools that are used for software verification and validation (V&V), and the effectiveness of software QA procedures. Compounding the concerns are sporadic incidents of digital equipment malfunctions in nuclear power plants, including lockups resulting from software memory leaks, feedwater control malfunctions due to excessive traffic on Ethernet networks, software logic errors, and other problems leading to plant trips. Additionally, the limited amount of demonstrated research and a lack of industry consensus on quantitative software reliability methodologies has hindered the widespread adoption of digital I&C in the nuclear industry. In a recent Regulatory Issue Summary (RIS), the U.S. Nuclear Regulatory Commission (NRC) identified concerns about the impact of greater use of instrumentation and control (I&C) equipment with an embedded digital device (EDD) [1]. Specifically, the NRC staff states that increased use of such devices “may increase a facility’s vulnerability to a CCF” (i.e., common-cause failure). The prevailing NRC guidance regarding software CCF (SCCF) identifies two design attributes that are acceptable for eliminating CCF concerns: (1) diversity or (2) testability (specifically, 100% testability). Either solution can result in high costs and remaining licensing uncertainty (i.e., how much diversity is enough? how to ensure test coverage of every possible sequence of device states?). Consequently, many utilities and reactor designers have limited or avoided more extensive use of digital technology to minimize licensing, scheduling, and financial risk. Without development of cost effective qualification methods to satisfy regulatory requirements and address the potential for CCF vulnerability associated with EDDs, the nuclear power industry may not be able to realize the benefits of digital technology achieved by other industries. In order to address these challenges, the U.S. Department of Energy (DOE) is sponsoring this research to assess the prospect of CCF in digital I&C equipment. The purpose of the current research is to develop an effective approach employing science-based methods to resolve concerns about CCF vulnerability that serve to inhibit deployment of advanced instrumentation (e.g., sensors, actuators, microcontrollers) with EDDs in nuclear power applications. The research objectives address the challenge of establishing high levels of safety and reliability assurance needed for the qualification of EDDs (e.g., microprocessors, programmable logic devices) that are subject to software design faults, complex failure modes, and CCF vulnerability. Specific objectives are: (1) assess the regulatory context for treatment of CCF vulnerability a graded approach to their qualification, (3) develop and extend model-based testing methods to enable effective demonstration of whether devices are subject to CCF, which may arise from vulnerabilities introduced at any stage of the design lifecycle, (4) establish a cost-effective testing framework that incorporates automation and test scenario prioritization, and (5) demonstrate the qualification approach through selection and testing of candidate digital device(s). This report documents an assessment of the results from experimental testing of a representative instrument with an embedded digital device. The experiment serves as a proof-of-concept demonstration of a model-based testing (MBT) framework. The objective of MBT is to ensure that an EDD undergoes suitably comprehensive testing to establish that it is not subject to SCCF. The approach by which MBT accomplishes this objective is through development and application of an effective test suite that provides the appropriate coverage to detect the full range of postulated faults that may arise at the requirements, design, and code (implementation) level of the EDD software. The findings of the experiment involve the application of the MBT framework by Ohio State University (OSU) through testing of the selected demonstration instrument (i.e., the prototype smart pressure sensor developed by Virginia Commonwealth University, VCU). In addition, the experiment findings also involve application of a more conventional black-box testing approach that serves as a baseline against which the MBT methodology can be compared. The primary outcome documented in this report is the comparative analysis of the capabilities demonstrated for each method. The results of the analysis demonstrate clear benefits that arise from the MBT methodology. The comparison indicates greater effectiveness from the more comprehensive evidence available through MBT and enhanced efficiency achieved through automation within the MBT framework. The structure of the report addresses the approach and application of each test method as well as the comparative analysis of the experimental results. Chapter 2 gives a summary overview of the demonstration approach. Chapter 3 covers the baseline testing and Chapter 4 documents the model-based testing. Chapter 5 describes the findings of the comparative analysis and presents the conclusions that can be drawn about the effectiveness and efficiency of the MBT methodology.
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Predicting and Preventing Failures [Slides]
Unpredicted bearings failures can be costly and dangerous. Current methods of detecting bearing faults rely on trained specialists with expensive detection equipment. Specialists must react daily, as the sheer number of bearings and the machines they operate are critical and any downtime will result in loss of productivity and endanger personal. My method utilizes machine learning algorithms coupled with relatively inexpensive accelerometers to remotely monitor data for specialists for fault detection, and estimate the diameter of the fault that has been caused by the bearing. The algorithm offers high accuracy detection using solely accelerometer data that can be obtained remotely, reducing the strain on analysists and ensuring that equipment is always running at an optimal and safe level. The algorithms operate in two layers, one layer detects the type of fault, and the second estimates the diameter of wear to determine the severity of the fault. The detection layer operates in the frequency space using a high accuracy Support Vector Machine algorithm and is trained on different types and sizes of machines to ensure generalization. The second layer uses a modified k-means clustering algorithm and vector calculus to estimate the bearing wear.
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Bearing Fault Detection and Wear Estimation Using Machine Learning
Unpredicted bearings failures can be costly and dangerous. Current methods have been examining the use of machine learning algorithms to automatically detect bearing faults. Vibration specialists must react daily, as the sheer number of bearings and the machines they operate are critical and any downtime will result in loss of productivity and endanger personal. The method presented in this paper utilizes a novel approach to detect and determine bearings fault diameters. The detection algorithm uses the cubic support vector machine (SVM) for high accuracy inner and outer race fault detection. After detecting the type of fault, the data is then passed through a second layer that uses a modified K-means clustering and a linear interpolation to accurately estimate the diameter of the fault that was detected.
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Optical Sampler for Broad-Spectrum Sensing
We investigated the feasibility of using non-uniform optical sampling to measure wide bandwidth RF signals more efficiently. This combined two areas of signal measurement research: optical sampling, which uses optics to alleviate the bandwidth limitations of traditional electronic sampling, and compressed sensing, which seeks to measure signals with fewer samples than typically necessary. This can both increase accuracy and reduce cost over current state-of-the-art sampling devices. However, several different compressed sensing architectures and algorithms offer varying levels of performance, robustness and complexity. In this work, we compared the tradeoffs between several of these methods in simulation of a remote sensing application. We found that while feasible, compressed sensing is inherently less sensitive than fully sampled systems, and so must be paired with a very low noise optical system. We also built an experimental prototype of the system and took initial measurements.
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PNT Resilience RFI Response
The use of the Global Positioning System (GPS) is a fundamental requirement for most navigation systems today, and this heavy reliance means that denial of GPS service (or extended threats) can pose a significant risk to modern navigation. There is an urgent need for enabling, high-accuracy navigation technologies that can operate without the need for GPS. Ideally, these solutions must be able to initialize in a completely GPS-free environment and continue to navigate even through challenging scenarios. The increasing risk posed to GPS means that trust in this platform is waning—and solutions are required. A future navigator should leverage GPS whenever possible and be capable of identifying and responding to risks while maintaining mission accuracy needs. In the absence of GPS, fully alternative navigation (altnav) technologies are required. This report describes an introductory view of altnav for GPS-impaired and contested environments. Various technologies are collected, presented, and evaluated as potential solutions. A wide snapshot of currently available technologies with a first-order summary of their potential is presented. While this report attempts to be as broad and complete as possible, this is a quickly evolving field.
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Coherent Processing of Up/Down Linear Frequency Modulated Chirps
A useful and popular waveform for high-performance radar systems is the Linear Frequency Modulated (LFM) chirp. The chirp may have a positive frequency slope with time (up-chirp) or a negative frequency slope with time (down-chirp). There is no inherent advantage to one with respect to the other, except that the receiver needs to be matched to the proper waveform. However, if up-chirps and down-chirps are employed on different pulses in the same Coherent Processing Interval (CPI), then care must be taken to maintain coherence in the range-compressed echo signals. We present the mathematics for doing so, for both correlation processing and stretch processing.
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Lynceus 2020Q3 Report: Data Ecosystem Updates [Slides]
Abstract not provided.
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Performance Monitoring using Pecos (V.0.1)
Advances in sensor technology have rapidly increased our ability to monitor natural and human-made physical systems. In many cases, it is critical to process the resulting large volumes of data on a regular schedule and alert system operators when the system has changed. Automated quality control and performance monitoring can allow system operators to quickly detect performance issues. Pecos is an open source python package designed to address this need. Pecos includes built-in functionality to monitor performance of time series data. The software can be used to automatically run a series of quality control tests and generate customized reports which include performance metrics, test results, and graphics. The software was developed specifically for solar photovoltaic system monitoring, and is intended to be used by industry and the research community. The software can easily be customized for other applications. The following Pecos documentation includes installation instructions and examples, description of software features, and software license. It is assumed that the reader is familiar with the Python Programming Language. References are included for additional background on software components.
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Smart Methane Emission Detection System Development Final Report
Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators.
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Improving Coal-Fired Plant Performance Through Integrated Predictive and Condition-Based Monitoring Tools
The project demonstrated the ability to improve boiler performance and reliability through the integrated use of condition-based monitoring (CBM) and predictions of the impacts of coal quality on boiler operations at a full-scale coal-fired power plant. The advanced tool developed actively monitors and manages coal quality and overall boiler conditions that maximizes availability and maintains generating capacity while reducing cost. The tool is used to forecast and alert plant operators and engineers about poor boiler conditions which may occur as a result of incoming coal and/or current power plant operating conditions. The Combustion System Performance Indices (CSPI) and CoalTracker (CT) programs predicts fireside performance of the plant including slagging, fouling, erosion, slag flow, strength development (sintering/densification), and slag layer thickness based on the composition of delivered coal. CT program is tailored for each plant and is used to track the coal from the point of delivery to the burner. The most successful application of the CSPI-CT has been at plants where the coal composition is determined through the use of a full stream elemental analyzers based on prompt gamma neutron analysis (PGNAA). The CSPI-CT program information is used by coal procurement, coal mining and plant operations personnel to select and blend coal properties for the best plant performance.
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AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report
Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.
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Neural network denoising of HED x-ray images, with an introduction to neural networks [Slides]
We created and trained a new neural network denoiser model using estimates of noise in the data. We showed that our denoiser significantly reduces the combined blur and noise on the training and testing set. We demonstrated the efficacy of neural network denoisers for reducing noise on HED x-ray images. Changes to network architecture and improvements to the noise model in training could improve the model.
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EIC detector, software, experimental possibilities [Slides]
Abstract not provided.
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VOLTTRON Modular Framework: Enabling flexible and scalable deployment solutions
VOLTTRON ™ is an open-source platform for distributed sensing and control. The platform provides services for collecting and storing data from buildings and devices and provides an environment for developing applications which interact with that data. The platform allows developers to build out their use cases by utilizing these frameworks and integrating new capabilities. To simplify the deployment of systems built on VOLTTRON, a new way of organizing the codebase is being explored. This document details these efforts through a new code repository layout for the VOLTTRON platform and services, and how this new layout provides targeted deployments using standard python deployment packages (wheels). In addition, this paper will discuss the development of third-party agents and how they can be integrated within the VOLTTRON ecosystem. Finally, we will discuss core platform development and direction for the modularized version of VOLTTRON.
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Vehicle Exhaust Monitoring Applications using loT platform
Vehicle emission is a major source of air pollution. A significant number of deaths is associated with air pollution. The existing vehicle emission monitoring system is unable to mitigate the pollution properly. Real-time pollution measurement is necessary to address the problem more precisely. The purpose of this project is to develop novel Vehicle Emission Monitoring Applications (VEMA) using the real-time measurement of pollutants from a vehicle tailpipe where exhaust gases enter the environment. As a part of the project, 2 technologies have been used for exhaust gas monitoring. First, an off-the-shelf exhaust gas monitoring prototype has been acquired. Second, a brand new platform was developed using off-the-shelf components. The project focused on predictive application for smart mobility and for large large fleet companies and enterprises. A data collection methodology was developed and tested by collecting close to 20 hours of data within Washington State under varying speeds, altitudes, road conditions etc. An approach for data curation was also used and finally, a simple Machine Learning was used for recognizing patterns between inputs such as vehicle speed and altitude and output such as NOx emission from vehicle exhaust pipe.
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Combustion Performance and Emissions Optimization Through Integration of a Miniaturized High-Temperature Multi Process Monitoring System
The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.
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