Search NASA⌕ Search

SEARCH · Search NASA

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

Collaborative development of the EPICS Qt framework Phase I Final Report

At Lyncean, a private company spun-off from technology developed at the SLAC National Lab, we have been using EPICS for over a decade. EPICS is ubiquitous on our flagship product – the Compact Light Source. EPICS is not only used to control our laser and accelerator systems, but also to control our x-ray beamlines. The goal of this SBIR is for Lyncean Technologies to spearhead a worldwide collaborative effort for the development of control system tools for EPICS using the Qt framework, a C++-based coding environment that could serve as a competitive alternative to the Java-based Control System Studio (CSS). This grant's Phase I, not unlike a feasibility study, is designed for planning and scoping the preparatory work needed for Phase II or other funding opportunities. The three main objectives of this Phase I are (1) to become better acquainted with the existing EPICS Qt software and Qt framework in order to evaluate the best options for ongoing development, (2) to demonstrate that our engineers can lead the EPICS community and jump-start the Qt collaboration, and (3) to identify a scope for our future work with solicited feedback from the EPICS community. This Phase I report includes key technical findings. It clarifies the differences between the two apparently-competing EPICS Qt implementations, caQtDM and the QE Framework; it explains how to create python-bindings, and compares Qt graphical libraries. But this report is also a personal story that narrates the birth of a collaboration. Starting a collaboration is not the work of a single individual, but the work of many. Therefore this report is also an attempt to publicly give credit to many who supported the effort. The main take-away from this grant is the successful birth of an EPICS Qt collaboration, seeded with existing software from the PSI and the Australian Synchrotron. But a lot more needs to be done for the collaboration founders' vision to be realized, and for the collaboration to reach its full potential. To help define the scope of future work, a useful approach we have identified is user experience design (UXD) and is discussed herein.

43 PARTICLE ACCELERATORS↗

Documentation and Instructions for Running Two Python Scripts that Aid in Setting up 3D Measurements using the Polytec 3D Scanning Laser Doppler Vibrometer

Sandia National Laboratories has recently purchased a Polytec 3D Scanning Laser Doppler Vibrometer for vibration measurement. This device has proven to be a very nice tool for making vibration measurements, and has a number of advantages over traditional sensors such as accelerometers. The non-contact nature of the laser vibrometer means there is no mass loading due to measuring the response. Additionally, the laser scanning heads can position the laser spot much more quickly and accurately than placing an accelerometer or performing a roving hammer impact. The disadvantage of the system is that a significant amount of time must be invested to align the lasers with each other and the part so that the laser spots can be accurately positioned. The Polytec software includes a number of nice tools to aid in this procedure; however, certain portions are still tedious. Luckily, the Polytec software is readily extensible by programming macros for the system, so tedious portions of the procedure can be made easier by automating the process. The Polytec Software includes a WinWrap (similar to Visual Basic) editor and interface to run macros written in that programming language. The author, however, is much more proficient in Python, and the latter also has a much larger set of libraries that can be used to create very complex macros, while taking advantage of Python’s inherent readability and maintainability.

47 OTHER INSTRUMENTATION↗

Uncertainty for Part Density Determination: An Update

Accurate and precise density measurements by hydrostatic weighing requires the use of an analytical balance, configured with a suspension system, to both measure the weight of a part in water and in air. Additionally, the densities of these liquid media (water and air) must be precisely known for the part density determination. To validate the accuracy and precision of these measurements, uncertainty statements are required. The work in this report is a revision of an original report written more than a decade ago, specifically applying principles and guidelines suggested by the Guide to the Expression of Uncertainty in Measurement (GUM) for determining the part density uncertainty through sensitivity analysis. In this work, updated derivations are provided; an original example is revised with the updated derivations and appendix, provided solely to uncertainty evaluations using Monte Carlo techniques, specifically using the NIST Uncertainty Machine, as a viable alternative method.

47 OTHER INSTRUMENTATION↗

MCNP6 and DRiFT modeling efforts for the NEUANCE/DANCE detector array

This report seeks to study and benchmark code predictions against experimental data; determine parameters to match MCNP-simulated detector response functions to experimental stilbene measurements; add stilbene processing capabilities to DRiFT; and improve NEUANCE detector array modeling and analysis using new MCNP6 and DRiFT features.

47 OTHER INSTRUMENTATION↗

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.

47 OTHER INSTRUMENTATION↗

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.

47 OTHER INSTRUMENTATION↗

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.

42 ENGINEERING↗

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.

42 ENGINEERING↗

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.

47 OTHER INSTRUMENTATION↗

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.

42 ENGINEERING↗

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.

47 OTHER INSTRUMENTATION↗

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.

47 OTHER INSTRUMENTATION↗

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.

03 NATURAL GAS↗

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.

01 COAL, LIGNITE, AND PEAT↗

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.

20 FOSSIL-FUELED POWER PLANTS↗