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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Advanced Sensors and Instrumentation Issue 10

The Advanced Sensors and Instrumentation (ASI) program, on its seventh fiscal year, continues to address key technology gaps for future nuclear energy systems. ASI’s goal is to enable the advanced instrumentation and control (I&C) technologies essential to the Office of Nuclear Energy’s research and development (R&D) efforts needed to realize its mission goals. Information on our projects can be found in our ASI annual awards summaries, annual review webinars, and newsletters posted in the Office of Nuclear Energy website (energy.gov/ne). This year, we have initiated three newly awarded projects in the area of data analytics. We also initiated a project to develop and demonstrate a prototype passive wireless sensor network. This sensor will use advanced manufacturing and printing, and be tested and demonstrated at a U.S. nuclear power plant facility. In addition, we are developing a sensor database to capture advanced sensor and instrumentation technology that is applicable to nuclear power plants. Under the In-Pile Instrumentation program, we continue to develop unique in-pile sensors and instrumentation to provide real-time, accurate, and spatially resolved information regarding the performance of fuels and materials. Due to the closure of the Halden reactor, we are focusing our near-term research in providing equivalent capability for the instrumentation of irradiation experiments in the U.S. that are needed by the nuclear industry. As the ASI program continues to award projects through competitive solicitations, we encourage the I&C community to visit our website for current funding opportunity announcements, especially for the industry-focused solicitation entitled, “U.S. Industry Opportunities for Advanced Nuclear Technology Development.”

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Laser-Induced Carbon-Based Smart Flexible Sensor Array for Multiflavors Detection

We report a flexible sensor array electronic tongue system that is fabricated on a polymer substrate by the laser direct writing process for multiflavor detection. Electronic tongue is a sensing system that is applied to detect different elements with the same sensor array. By analyzing responses from different measurement units, it enables a cross-sensitivity, namely, the ability of the system to responding to a range of different analytes in solution without specific functionalization of sensors. In this article, a six-unit sensing array system was fabricated by a laser direct writing process. Sensing units were introduced on a flexible polyamide surface. A high surface–volume ratio porous carbon structure was created by a laser-induced carbonization process, which provides stable conductive carbon electrodes with high sensitivity. Different surface treatments, such as gold plating, reduced-graphene oxide coating, and polyaniline coating, were accomplished for different measurement units. By applying principal component analysis, this sensing system shows a promising result for the detection of multiple flavors. The detection limit for each element is about 0.1 mM for NaCl and sugar solutions. Also, it is able to detect 10 –4 times diluted commercial table vinegar solution, which originally contains 5% acetic acid. The detection limit is theoretically lower than the human threshold of 10 mM for NaCl and sugar. Besides, the sensing system shows a high sensitivity and selectivity for mixed elements. Here, by mapping the data points, the sensor system could detect flavor combinations and provide a reliable prediction of analyte concentration ratios.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Automated Vehicle Detection in a Nuclear Facility Using Low-Frequency Acoustic Sensors

This article presents an analysis of the method of construction and results for a classifier intended to identify vehicles using low-frequency acoustic data collected by a dis-tributed sensor network. This data is collected as part of a venture intended to explore data analytics and multisensor fusion techniques for the monitoring of activities at a test bed nuclear facility located at Oak Ridge National Laboratory in Oak Ridge, Tennessee. We describe the associated target signature and design a classifier based on a multilayer perceptron, followed by an analysis of its results. We discuss how overall accuracy is not the only consideration in constructing this classifier, and how for this application, it is actually desirable to operate at a lower level of accuracy in exchange for a reduction in the false alarm rate, as well as how this relates to the actual deployment of the classifier in practical use.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2018

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗
DOE OSTI2022

Real-Time Distribution System State Estimation with Asynchronous Measurements

We report state estimation is a fundamental task in power systems. Although distribution systems are increasingly equipped with sensing devices and smart meters, measurements are typically reported at different rates and asynchronously; these aspects pose severe strains on workhorse state estimation algorithms, which are designed to process batches of data collected in a synchronous manner from all the measurement units. In this paper, we develop a novel state estimation algorithm to continuously update the estimate of the state based on measurements received in an asynchronous manner from measurement units. The synthesis of the algorithm hinges on a proximal-point type method, implemented in an online fashion, and capable of processing measurements received sequentially from sensors. A performance analysis is presented by providing bounds on the estimation error in terms of the mean and variance that hold at each iteration and asymptotically. The scheme is also compared with a more traditional Weighted Least Squares estimator that compensates for the lack of measurement data by using, as pseudo measurements, the measurement retrieved during a certain time window. Numerical simulations on the IEEE 37-bus feeder corroborate the analytical findings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Time-series dissolved oxygen, other bigeochemically-relevant analytes, and pressure gradients associated with the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”

This dataset contains time-series data from a vertical profile within the bed and banks of the Columbia river near Richland, WA. Water was sampled through 3 small tubes embedded in the sediment at 50,100, and 200 cm below the sediment-water interface. The goal of this study was to observe the correlations between hydraulic drivers and biogeochemical responses. The results of this study are published in the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”. The file types included in the data package are all time-series spreadsheet data, including hydraulic head gradients, physical parameters (temperature, pressure, SpC (specific conductivity)), and biogeochemical parameters (dissolved oxygen, pH, NO3 (nitrate) and ORP (oxidation-reduction potential)).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Technologies to Reactors: Enabling Accelerated Deployment of Nuclear Energy Systems Workshop Report

Nuclear energy provides almost 20% of the electric power in the United States, producing power with high availability while accounting for 63% of our carbon-free energy production. While we have the capacity to expand this clean power source within the United States, we have not built any advanced reactor plant designs in our rapidly evolving electricity generation market, where cost is a major driver. With construction cost and plant safety being major elements of cost for new nuclear, advanced reactor technologies could provide lower cost, safer, carbon-free, grid-resilient energy sources for the US electricity grid. When the current fleet of nuclear reactors was originally designed, many of today’s science and technology (S&T) advances were not available. Today these advances show great promise for transforming our national approach to nuclear reactor design. Other industries are capitalizing on rapid innovations in materials, manufacturing, sensors and control systems, and high-fidelity modeling and simulation (M&S), along with data analytics to provide lower costs in manufacturing and extended ranges of operations. In the nuclear industry, these innovations can translate into similar benefits as well as rapid design and qualification of advanced nuclear fuels. Effectively implementing these types of innovations could position American expert leadership to compete more fully in the international nuclear energy market. In July of 2018, the US Department of Energy (DOE) Office of Nuclear Energy sponsored a workshop to address these possibilities: “Technologies to Reactors: Enabling Accelerated Deployment of Nuclear Energy Systems.” Workshop participants explored how modern and emerging S&T can be used to transform the approach to nuclear reactor design, licensing, and operation to accelerate deployment of advanced nuclear energy systems. More than 80 experts from federal agencies, academia, and industry participated in the workshop to develop consensus on priority technology directions (PTDs) and recommend technology and deployment initiatives to explore and demonstrate key benefits for the nuclear community.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗
DOE OSTI2022

Energy Efficient Material Processing through Automated Process Monitoring and Controls

Smart manufacturing is bound to play a crucial role in reducing global energy consumption while accelerating economic development. This phenomenon is evident in advanced sensing technology developments, data analytics and machine learning/AI, automated controls, cloud computing, etc. The immediate opportunity for smart manufacturing is to improve the energy efficiency of manufacturing operations through innovations in processes and controls. The manufacturing industry still consumes about 30% of total global energy production, which is significant. This program focused on the recommendation of heterogeneous sensors to monitor the Chemical Vapor Infiltration (CVI) process parameters, states, and key performance indices and utilize cloud computing to sort and analyze the real-time data. Learning through data analytics helps guide the control parameters affecting energy usage. The target process in the program is the CVI process at the Honeywell South Bend facility, which is one of the most energy-intensive manufacturing industries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Semantic Interaction for Visual Analytics: Toward Coupling Cognition and Computation

The dissertation discussed in this article [1] was written in the midst of an era of digitization. The world is becoming increasingly instrumented with sensors, monitoring, and other methods for generating data describing social, physical, and natural phenomena. Thus, data exist with the potential of being analyzed to uncover, or discover, the phenomena from which it was created. However, as the analytic models leveraged to analyze these data continue to increase in complexity and computational capability, how can visualizations and user interaction methodologies adapt and evolve to continue to foster discovery and sensemaking?

semantic interaction↗
DOE OSTI2013

On-Ramp Wireless: Program Final Report

The objective of the project was to develop and demonstrate a centrally managed, wide area wireless distribution grid sensor and faulted circuit indicator system capable of monitoring underground and other hard-to-reach distribution circuits while delivering the reliability, ease of installation, security, battery life and low cost structure required to be a commercially viable product for the electric utility industry. This integrated wireless grid monitoring system enables utilities across the United States to enhance the reliability of their distribution grids, improve performance metrics (i.e., SAIFI, CAIDI, SAIDI), support dynamic load management and predictive maintenance, and enable future remote monitoring applications necessary to achieve the goals of the Smart Grid. This project scope included: conceptual design; trade studies focused on maximizing ease of installation, sensing capabilities, power budget analysis to reduce maintenance costs and time to replace; sensor product development; and field technology demonstration. The trade studies included Radio Frequency (RF) propagation. The RF Trade Study was completed for the 2.4 GHz ISM-band and not extended to 1 GHz band due to availability of suitable radio solutions and associated impacts to overall commercial value of the solution. The technology demonstrations/field trials were performed in partnership with Southern California Edison and San Diego Gas and Electric. The sensing systems included fault indicator and wireless analog sensor devices from Schweitzer Engineering Labs (SEL) and smart transformer monitoring devices from Gridsense. Both SEL and Gridsense worked with ORW during this project to During the demonstration phase, sensors were deployed in multiple locations. Resulting data was analyzed for the following: RF performance, communication systems reliability, sensor system capabilities, and system level analytics relative to the commercial value to the smart grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Large-Scale Trajectory Analysis via Feature Vectors

The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.

58 GEOSCIENCES↗
DOE OSTI2023

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station’s capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station’s use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station’s open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

PMU Data Integrity Evaluation through Analytics on a Virtual Test-Bed

Power systems are rapidly becoming populated by phasor measurement units (PMUs) in ever increasing numbers. PMUs are critical components of today s energy management systems, designed to enable near real-time wide area monitoring and control of the electric power system. They are able to measure highly accurate bus voltage phasors as well as branch current phasors incident to the buses at which PMUs are equipped. Synchrophasor data is used for applications varying from state estimation, islanding control, identifying outages, voltage stability detection and correction, disturbance recording, and others. However, PMU-measured readings may suffer from errors due to meter biases or drifts, incorrect configurations, or even cyber-attacks. Furthermore, the testing of early PMUs showed a large disparity between the reported values from PMUs provided by different manufacturers, particularly when frequency was off-nominal, during dynamic events, and when harmonic/inter-harmonic content was present. Detection and identification of PMU gross measurement errors are thus crucial in maintaining highly accurate phasor readings throughout the system. In this paper, we present our work in conducting analytics to determine the trustworthiness and worth of the PMU readings collected across an electric network system. By implementing the IEEE 118 bus test case on a virtual test bed (VTB) , we are able to emulate PMU readings (bus voltage and branch current phasors in addition to bus frequencies) under normal and abnormal conditions using (virtual) PMU sensors deployed across major substations in the network. We emulate a variety of failures such as bus, line, transformer, generator, and/or load failures. Data analytics on the voltage phase angles and frequencies collected from the PMUs show that specious (or compromised) PMU device(s) can be identified through abnormal behaviour by comparing the trend of its frequency and phase angle reading with the ensemble of all other PMU readings in the network. If the reading trend of a particular PMU deviates from the weighted average of the reading trends of other PMUs at nearby substations, then it is likely that the PMU is malfunctioning. We assign a weight to each PMU denoting how electric-topology-wise close it is from where the PMU under consideration is located. The closer a PMU is, the higher the weight it has. To compute the closeness between two nodes in the power network, we employ a form of the resistance distance metric. It computes the electrical distance by taking into consideration the underlying topology as well as the physical laws that govern the electrical connections or flows between the network components. The detection accuracy of erroneous PMUs should be improved by employing this metric. We present results to validate the proposed approach. We also discuss the effectiveness of using an end-to-end VTB approach that allows us to investigate different types of failures and their responses as seen by the ensemble of PMUs. The collected data on certain types of events may be amenable to certain types of analysis (e.g., alerting for sudden changes can be done on a small window of data) and hence determine the data analytics architectures is required to evaluate the streaming PMU data.

and data analytics architecture.↗
DOE OSTI2023

Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. The current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques such as X-Ray Computed Tomography (XCT), which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. RTX Technologies Research Center (RTRC) has licensed ORNL’s Peregrine software package – a printer- and camera-agnostic data analytics tool designed specifically for detecting process anomalies using in situ data collected during powder bed printing. The goal of this project was to feed temporally rich, multi-modal sensor data, including visible light, integrated near infrared (NIR), and spatially mapped co-axial melt pool thermal emission data into Peregrine to enable detection of subsurface flaws. XCT data was used as ground truth training data to allow Peregrine’s deep learning algorithms to recognize anomalies in these complex data streams in both test artifacts and industrially relevant geometries. Completion of this program has seen the successful implementation of multi-modal, multi-layer sensor data footprints for training of machine learning models in Peregrine. Flaws detected in XCT data have been successfully detected directly from this in situ data footprint, and initial analyses of the in situ probability-of-detection has been conducted, showing performance levels commensurate with traditional non-destructive evaluation (NDE) methods. The in situ monitoring methodology was then applied to an industrially relevant component that was using post-build NDE, highlighting the utility of the proposed method for hard-to-inspect AM components. As a direct result of this program, two journal manuscripts [1], [2] have been published in Additive Manufacturing, with additional manuscripts planned following program completion.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Digital phase demodulation for low-coherence interferometry-based fiber-optic sensors

In this paper, we describe a digital phase demodulation scheme for low-coherence interferometry-based fiber-optic sensors by employing a simple generation of phase-shifted signals at the interrogation interferometer. The scheme allows a real-time calibration process and offers capability of measuring large variations (up to the coherence of the light source) at the bandwidth that is only limited by the data acquisition system. Finally, the proposed phase demodulation method is analytically derived and its validity and performance are experimentally verified using fiber-optic Fabry–Perot sensors for measurement of strains and vibrations.

47 OTHER INSTRUMENTATION↗