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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Big Data Framework with Machine Learning for D and D Applications - 19108

The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Practical procedures for sensor quality assessment

Sensors are increasingly deployed for process monitoring and control. These produce on-line measurements at a high frequency, in parallel with low-frequency laboratory measurements. Compared to laboratory practices, sensor data quality assessment and control practices are far less structured at most utilities. This leads to inaccurate sensor data with unknown uncertainty factors.This chapter shows how to establish standard operating procedures (SOPs) to support sensor data quality assessment and control and subsequent maintenance actions by producing relevant sensor metadata. Furthermore, SOPs are provided for the most commonly used wastewater quality sensors, inspired by utility and academic best practices. This chapter builds on definitions provided in Chapter 3 and provides additional definitions specifically related to sensors maintenance. Chapter 6 complements the methods in this chapter, which are based on reference measurements, with data-analytical techniques.

Alferes, Janelcy↗
DOE OSTI2021

Introduction to the special issue on smart transportation

Transportation is getting smarter and smarter, with the prominence of connected automated vehicle technologies in the global auto industry’s near-term growth strategies, of big data analytics and unprecedented access to sensing data of mobility, and of integration of this analytics into the optimization of mobility and transport. Further, these developments are setting off a wave of smart transportation innovations, which are featured by new methods and applications driven by various forms of sensor data such as GPS, CAN bus, LIDA, images, etc. At the same time, complexities surrounding the use, conflation, and processing of disparate data in near real-time is of essence for the design and development of futuristic smart transportation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Stream-Processing Architecture for Real-time Cyber-physical Security (SPARCS) v0.9

The Stream-Processing Architecture for Real-time Cyber-physical Security is a generalized framework for extracting power-related sensor information from sensors, optionally analyzing that data on computing platforms local to the sensors, transmitting that information to a central location, storing that information, running centralized analytics on the information, and providing both API and visual access to that information.

Mcparland, Charles McparlandP↗
DOE OSTI2020

A data analytical approach for assessing the efficacy of Operational Technology active defenses against insider threats

In recent years, the need for Operational Technology (OT) defenses has been recognized, serving as an additional line of defense when Information Technology (IT) defenses are bypassed. This is no longer considered an uncommon possibility when dealing with advanced persistent threat (APT) actors expected to be state-sponsored and receiving insider assistance. In these extreme adversarial situations, OT defenses aim to provide another layer of defense for the system, introduced directly at the physical process level, as described by the sensors data, the system model, and control actions. Just like IT defenses, two schools of thought, i.e., passive and active defenses, have emerged to address this challenge. In active defenses, representing the focus of this paper, known signatures, synthesized based on the system's unique characteristics, are inserted into the system. In contradistinction, passive methods rely solely on observing system behavior in search of patterns of normal behavior with deviations thereof representing abnormal behavior. In their most sophisticated implementations, both passive and active defenses rely on the use of data analytics to identify the patterns and synthesize the observed and/or inserted signatures. Past research has shown that passive defenses may be bypassed by APT actors relying on data analytics and their intimate knowledge of the system to evade detection by respecting the patterns identified by the defenders. Here this manuscript explores the use of active defenses under the assumption that the attacker has privileged access to the system, including access to the system's model and sensors data. Specifically, this manuscript assesses the ability of active defenses to remain invisible to the attackers, and discusses the associated challenges that must be addressed to ensure their resiliency against APT actors.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

(U) An Analytic Examination of Piezoelectric Ejecta Mass Measurements

Ongoing efforts to validate a Richtmyer-Meshkov instability (RMI) based ejecta source model [1, 2, 3] in LANL ASC codes use ejecta areal masses derived from piezoelectric sensor data [4, 5, 6]. However, the standard technique for inferring masses from sensor voltages implicitly assumes instantaneous ejecta creation [7], which is not a feature of the RMI source model. To investigate the impact of this discrepancy, we define separate “areal mass functions” (AMFs) at the source and sensor in terms of typically unknown distribution functions for the ejecta particles, and derive an analytic relationship between them. Then, for the case of single-shock ejection into vacuum, we use the AMFs to compare the analytic (or “true”) accumulated mass at the sensor with the value that would be inferred from piezoelectric voltage measurements. We confirm the inferred mass is correct when creation is instantaneous, and furthermore prove that when creation is not instantaneous, the inferred values will always overestimate the true mass. Finally, we derive an upper bound for the error imposed on a perfect system by the assumption of instantaneous ejecta creation. When applied to shots in the published literature, this bound is frequently less than several percent. Errors exceeding 15% may require velocities or timescales at odds with experimental observations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2010

An architecture for real time data acquisition and online signal processing for high throughput tandem mass spectrometry

Independent, greedy collection of data events using simple heuristics results in massive over-sampling of the prominent data features in large-scale studies over what should be achievable through “intelligent,” online acquisition of such data. As a result, data generated are more aptly described as a collection of a large number of small experiments rather than a true large-scale experiment. Nevertheless, achieving “intelligent,” online control requires tight interplay between state-of-the-art, data-intensive computing infrastructure developments and analytical algorithms. In this paper, we propose a Software Architecture for Mass spectrometry-based Proteomics coupled with Liquid chromatography Experiments (SAMPLE) to develop an “intelligent” online control and analysis system to significantly enhance the information content from each sensor (in this case, a mass spectrometer). Using online analysis of data events as they are collected and decision theory to optimize the collection of events during an experiment, we aim to maximize the information content generated during an experiment by the use of pre-existing knowledge to optimize the dynamic collection of events.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.

42 ENGINEERING↗
DOE OSTI2025

A co-registered in-situ and ex-situ dataset from wire arc additive manufacturing process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data-centric approach emphasizes leveraging sensor data available throughout the production process to optimize performance. Integration of extensive data analysis provides opportunities for improving precision, reducing waste, and enhancing the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes a comprehensive description of the deposition process, process parameters, welding characteristics and acoustic data collected in-situ, and X-Ray Computed Tomography data of the build.

42 ENGINEERING↗
DOE OSTI2014

Resilient Monitoring Systems: Architecture, Design, and Application to Boiler/Turbine Plant

Resilient monitoring systems, considered in this paper, are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this work is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools. The performance of the overall system is evaluated using a simplified boiler/turbine plant. The measure of resiliency is quantified using Kullback-Leibler divergence, and is shown to be sufficiently high in all scenarios considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Online System ID for Predicting Power Plant Performance Throughout Cycling Operations

This presentation represents a review of the background research conducted by NETL to apply artificial intelligence, i.e. auto-recursive algorithms and data analytics to detect leaks in utility scale boilers and laboratory power systems. The new project being funded by the Advanced Sensors and Controls Program is part of the Field Work Proposal funded in EY21 as Task 53 to demonstrate the application of these techniques on a utility scale power system.

Shadle, Lawrence↗
DOE OSTI2013

Resilient Plant Monitoring System: Design, Analysis, and Performance Evaluation

Resilient monitoring systems are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this paper is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools, and the performance of the overall system is evaluated using simulations. The measure of resiliency of the resulting system is evaluated using Kullback Leibler divergence, and is shown to be sufficiently high in all scenarios considered.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine Tool Data Analytics for Digital Twin and Machine Predictive Maintenance

The primary objective of this project is to improve machining process performance using in-process machining data from the machine tool controller and external sensors. Advances in the Industrial Internet of Things (IIoT) enable monitoring of machines using controller data. Examples of the data provided by a controller include execution status of the controller, part count, block of code being executed, door status, tool position, the spindle and axis load, etc. MTConnect and OPC-UA are the two common protocols for capturing machine information. In this collaboration, methods for retrieving the machine controller data from selected machine tool controls and making these data accessible in different subsystems (such as digital twins and machine maintenance portals, etc.) will be developed and tested. In addition, analytics to improve machining process performance (by increasing productivity and reducing downtime) will be developed.

42 ENGINEERING↗
DOE OSTI2018

A Big Data Analytics Framework for HPC Log Data: Three Case Studies Using the Titan Supercomputer Log

Reliability, availability and serviceability (RAS) logs of high performance computing (HPC) resources, when closely investigated in spatial and temporal dimensions, can provide invaluable information regarding system status, performance, and resource utilization. These data are often generated from multiple logging systems and sensors that cover many components of the system. The analysis of these data for finding persistent temporal and spatial insights faces two main difficulties: the volume of RAS logs makes manual inspection difficult and the unstructured nature and unique properties of log data produced by each subsystem adds another dimension of difficulty in identifying implicit correlation among recorded events. To address these issues, we recently developed a multi-user Big Data analytics framework for HPC log data at Oak Ridge National Laboratory (ORNL). This paper introduces three in-progress data analytics projects that leverage this framework to assess system status, mine event patterns, and study correlations between user applications and system events. We describe the motivation of each project and detail their workflows using three years of log data collected from ORNL's Titan supercomputer.

Park, Byung↗
DOE OSTI2017

Can Social Media Play a Role in the Development of Building Occupancy Curves?

The demand for building occupancy estimation continues to grow in a wide array of application domains, such as population distribution modeling, green building technologies, public safety, and natural hazards loss analytics. While much has been gained in using survey diaries, sensor technologies, and dasymetric modeling, the volume and velocity of social media data provide a unique opportunity to measure and model occupancy patterns with unprecedented temporal and spatial resolution. If successful, patterns or occupancy curves could describe the fluctuations in population across a 24 h period for a single building or a class of building types. Although social media hold great promise in responding to this need, a number of challenges exist regarding representativeness and fitness for purpose that, left unconsidered, could lead to erroneous conclusions about true building occupancy. As a mode of discussion, this chapter presents an explicit social media model that assists in delineating and articulating the specific challenges and limitations of using social media. It concludes by proposing a research agenda for further work and engagement in this domain.

Stewart, Robert↗
DOE OSTI2011

Towards a Real-Time Cluster Computing Infrastructure

Real-time computing has traditionally been considered largely in the context of single-processor and embedded systems, and indeed, the terms real-time computing, embedded systems, and control systems are often mentioned in closely related contexts. However, real-time computing in the context of multinode systems, specifically high-performance, cluster-computing systems, remains relatively unexplored, largely due to the fact that until now, there has not been a need for such an environment. In this paper, we motivate the need for a cluster computing infrastructure capable of supporting computation over large datasets in real-time. Our motivating example is an analytical framework to support the next generation North American power grid, which is growing both in size and complexity. With streaming sensor data in the future power grid potentially reaching rates on the order of terabytes per day, the task of analyzing this data subject to real-time guarantees becomes a daunting task which will require the power of high-performance cluster computing capable of functioning under real-time constraints. One specific challenge that such an environment presents is the need for real-time networked communication between cluster nodes. In this paper, we discuss the need for real-time high-performance cluster computation, along with our work-in-progress towards an infrastructure which will ultimately enable such an environment.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

The rapid expansion of distributed and edge computing platforms—spanning autonomous vehicles, IoT sensors, and healthcare monitors—has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2012

Communication and Energy Efficiency in Visual Sensor Networks for People Localization

This paper addresses the communication and energy efficiency in collaborative visual sensor networks (VSNs) for people localization, a challenging computer vision problem of its own. We focus on the design of a light-weight and energy efficient solution where people are localized based on distributed camera nodes integrating the so-called certainty map generated at each node, that records the target non-existence information within the camera s field of view. We first present a dynamic itinerary for certainty map integration where not only each sensor node transmits a very limited amount of data but that a limited number of camera nodes is involved. Then, we perform a comprehensive analytical study to evaluate communication and energy efficiency between different integration schemes, i.e., centralized and distributed integration. Based on results obtained from analytical study and real experiments, the distributed method shows effectiveness in detection accuracy as well as energy and bandwidth efficiency.

Karakaya, Mahmut↗