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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Preventing Reverse Engineering of Critical Industrial Data with DIOD

Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk-assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse-engineering, stolen technology, etc. that often have disastrous consequences for businesses and their stakeholders alike. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data which are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse-engineering by discarding proprietary information and only preserving AI/ML-relevant information. Additionally, constraints of the original DIOD manuscript are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. As an example, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target application.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Government-Industry Data Exchange Program (GIDEP) and NASA Advisories

This viewgraph presentation reviews the Government-Industry Data Exchange Program (GIDEP) and NASA Advisories policy to practice. The contents include: 1) Purpose of the Government-Industry Data Exchange Program (GIDEP); 2) NASA and GSFC Documentation; 3) NASA Advisories, differences from GIDEP; 4) GIDEP Distribution by Originator; 5) New Interim GIDEP Policy for Suspect Counterfeits; 6) NASA and Suspect Counterfeits; 7) Threats to GIDEP; and 8) Conclusions and Contact Information.

Sampson, Michael J.↗
NASA NTRS1974

Government-Industry Data Exchange Program (GIDEP)

The Government-Industry Data Exchange Program (GIDEP) was originated in 1959 by the Army, Navy, and Air Force Ballistic Missile Agencies. Known at that time as IDEP--Interservice Data Exchange Program--its intent was to eliminate duplicate testing of parts and components by disseminating pertinent test data among Department of Defense contractors and various government agencies. In 1966 both the National Aeronautics and Space Administration and the Canadian Military Electronics Standards Agency (CAMESA) recognized the value of the data provided by the program and became participants. Today, GIDEP provides the interchange of specialized technical data to all the military services, participating government contractors and numerous government agencies such as the Atomic Energy Commission, Federal Aviation Administration, Defense Supply Agency, and the Small Business Administration.

Richards, E. T.↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

The Foundational Industrial Energy Dataset (FIED): Open-Source Data on Industrial Facilities

The state of data on industrial energy use has co-evolved over several decades with the demands of industrial energy analysis. The most recent development - analysis in support of decarbonizing the industrial sector - has changed the characteristics of industrial data that are useful for analysts and model developers. Although data and its collection processes may be cast from a conventional viewpoint as objective and free from the influence of social dynamics, this provides an incomplete picture of not only the processes by which information is generated, but also the limitations and opportunities of data to be useful for analysis. The foundational industry energy data set (FIED) is a result of the confluence of trends in open data and the demand for higher resolution industrial energy analysis. The general approach to compiling the FIED involves accessing, filtering, and formatting data published by federal organizations on the Internet for public use. Unlike most industrial energy datasets, which are published by the U.S. Energy Information Administration (EIA), the FIED relies on core datasets from the U.S. Environmental Protection Agency (EPA). The FIED addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization - including estimates of energy use, greenhouse gas emissions, and design capacities - for facilities that are identified by latitude and longitude. This enables local-level analysis of existing combustion equipment, as well as regional comparisons with traditional industrial energy data estimates. The report summarizes the general logic behind compiling the FIED. The FIED itself and its Python code are available from OpenEI and GitHub, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Design Choices in Anomaly Detection for Industrial Control Systems: Insights from Gas Pipeline Data

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and naïve imputation—prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensor-decomposition–based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

2018 Industrial Energy Data Book

The 2018 Industry Energy Data Book summarizes the status of, and it identifies the key trends in energy use and its underlying economic drivers across the four industrial subsectors: agriculture, construction, manufacturing, and mining.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2011

Solid Propulsion Systems, Subsystems, and Components Service Life Extension

The service life extension of solid propulsion systems, subsystems, and components will be discussed based on the service life extension of the Space Transportation System Reusable Solid Rocket Motor (RSRM) and Booster Separation Motors (BSM). The RSRM is certified for an age life of five years. In the aftermath of the Columbia accident there were a number of motors that were approaching the end of their five year service life certification. The RSRM Project initiated an assessment to determine if the service life of these motors could be extended. With the advent of the Constellation Program, a flight test was proposed that would utilize one of the RSRMs which had been returned from the launch site due to the expiration of its five year service life certification and twelve surplus Chemical Systems Division BSMs which had exceeded their eight year service life. The RSRM age life tracking philosophy which establishes when the clock starts for age life tracking will be described. The role of the following activities in service life extension will be discussed: subscale testing, accelerated aging, dissecting full scale aged hardware, static testing full scale aged motors, data mining industry data, and using the fleet leader approach. The service life certification and extension of the BSMs will also be presented.

Hundley, Nedra H.↗
DOE OSTI2024

Mesozoic Deserts and CO2 Storage: The Glen Canyon Group, Utah, USA

For much of the Mesozoic an extensive desert environment extended across the Western Interior of the USA. These desert systems deposited vast dunefields, including one of the largest ergs preserved in the rock record. The Glen Canyon Group in Utah is a Triassic-Jurassic aged aeolian succession, consisting of thick, laterally extensive aeolian dune deposits, interdune lakes and oases, and dryland fluvial systems. These aeolian deposits have been identified as potential CO2 sequestration reservoirs. The Jurassic-aged Navajo Sandstone has been the subject of research into its potential as a CO2 reservoir, as well as naturally occurring CO2 seeps in the Green River area. It has excellent reservoir properties, consisting of thick sandstones with high porosity and permeability, and has industry data including well logs and core. It occurs in both outcrop and subcrop, allowing for comparison and sense checking of interpretations across small and large scales. The Triassic Wingate Sandstone has received much less attention, however it too is composed of thick aeolian dune deposits. Like the Navajo Sandstone, the Wingate Sandstone has industry data, and occurs in both outcrop and subcrop. Using extensive industry data which exists across Utah is an effective way to pivot from a hydrocarbon focus to CO2 injection, and so contribute to green energy and carbon neutral emission goals. Here we use historic industry data in conjunction with field work, to study the Navajo and Wingate Sandstones and their potential as CO2 reservoirs, in addition to increasing our understanding of lithologic and stratigraphic complexity in one of the most significant aeolian systems in the world.

Mahon, Elizabeth↗
DOE OSTI2022

Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

AWS↗
DOE OSTI2020

Putting Our Industry's Data to Work: A Case Study of Large-Scale Data Aggregation: Preprint

With increasing deployment of Advanced Metering Infrastructure (AMI), Building Automation System (BAS) controls, Internet of Things (IoT) network devices, and data-driven evaluation, measurement, and verification studies, the building sector is currently generating a staggering amount of energy-related data. In the right hands, these data sets can contribute to increased comfort and energy savings for building occupants and a more reliable electrical grid; however, due to a combination of factors, including significant privacy concerns, much of the data that are presently generated and stored are not used outside of basic operational applications. In the past year, our team has dedicated over 2,000 person-hours to accessing building energy data for a project funded by the U.S. Department of Energy (DOE) Building Technologies Office. We sought whole-building or end-use (e.g., lighting) timeseries data at the individual-building or equipment level where possible or aggregated information, such as timeseries averages and quartiles by building type (e.g., office, retail, hospital), where sharing individual building information was not an option. We are additionally working with IoT and BAS data sets to derive information important to the project. We have assembled an extensive data set that will enable the development of publicly available end-use load profiles to benefit the U.S. building and electricity industries. Here we present an overview of the data set that we have assembled to date, the motivators and approaches that got us here, and the lessons we learned through our efforts. We also discuss work underway that presents additional options for future data access.

building energy data↗
DOE OSTI2024

Jurassic deserts and CO2 sequestration: the Glen Canyon Group in Utah, western USA

The Early Jurassic in the Western Interior of USA consisted of an extensive desert environment, including one of the largest ergs in geologic history. The Navajo erg has been estimated to extend as much as 2.2 million square kilometers, though the preserved extent is somewhat smaller. The Mesozoic was a global greenhouse phase, and during the Jurassic this region experienced fluctuations in climate aridity, reflected in the depositional environment. In addition to extensive dunefields, interdune deposits (lakes and oases) and fluvial systems are also documented within formations of the Glen Canyon Group. The Navajo Sandstone has received much attention as a potential CO2 injection target in recent years. It consists of thick, aeolian sandstones with high porosity and permeability, occurs in both outcrop and subcrop, and has industry data. Mapping of stratigraphic and sedimentologic changes within the Navajo Sandstone has been undertaken in localized areas, however piecing together these studies and developing regional models for CO2 potential has not received as much attention. There is significant industry data across Utah, and utilizing this data to pivot from a focus on hydrocarbon extraction to CO2 injection is an effective way to move forward with green energy, and to meet carbon neutral emission goals. Using legacy well data, we are developing a more comprehensive study of the Navajo Sandstone as a potential CO2 reservoir, in addition to identifying other zones of interest within the Glen Canyon Group, and improving understanding of stratigraphic complexity within one of the most significant aeolian systems in the world.

Mahon, Elizabeth↗
DOE OSTI2021

Unleashing the Power of Industrial Big Data through Scalable Manual Labeling

Big Data plays a central role in the remarkable results achieved by Machine Learning (ML) and especially Deep Learning (DL) in the recent years. However, the difficulty in obtaining a reasonable amount of labeled samples limits ML/DL application in various domains, including industrial equipment and system monitoring. In this paper the need for methods that turn manual labeling into a scalable process is highlighted. A real world problem is analyzed for which weak supervision methods, successfully employed in other domains, did not produce acceptable results. An alternative approach based on clustering ensembles is described and tested, achieving good performance.

Paes Leao, Bruno↗
NASA NTRS1998

Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.

Esogbue, Augustine O.↗
DOE OSTI2024

Catalyzing deep decarbonization with federated battery diagnosis and prognosis for better data management in energy storage systems

Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.

asset health management↗
NASA NTRS1990

Test plan pressure fed thrust chamber technology

Aerojet is developing the technology for the design of a reliable, low cost, efficient, and lightweight LOX/RP-1 pressure fed engine. This technology program is a direct result of Aerojet's liquid rocket booster (LRB) study and previous NASA studies that identified liquid engines using high bulk density hydrocarbon fuels as very attractive for a space transportation system (STS). Previous large thrust LOX/RP-1 engine development programs were characterized by costly development problems due to combustion instability damage. The combustion stability solution was typically obtained through trial and error methods of minimizing instability damage by degrading engine performance. The approach to this program was to utilize existing and newly developed combustion analysis models and design methodology to create a thrust chamber design with features having the potential of producing reliable and efficient operation. This process resulted in an engine design with a unique high thrust-per-element OFO triplet injector utilizing a low cost modular approach. Cost efficient ablative materials are baselined for the injector face and chamber. Technology demonstration will be accomplished through a hot fire test program using appropriately sized subscale hardware. This subscale testing will provide a data base to supplement the current industry data bank and to anchor and validate the applied analysis models and design methodology. Once anchored and validated, these analysis models and design methodology can be applied with greatly increased confidence to design and characterize a large scale pressure fed LOX/RP-1 thrust chamber. The objective of this test program is to generate a data base that can be used to anchor and validate existing analysis models and design methodologies and to provide early concept demonstration of a low cost, efficient LOX/RP-1 thrust chamber. Test conditions and hardware instrumentation were defined to provide data sufficient to characterize combustion stability, performance, and thermal operation over a wide thrust chamber throttling range.

Dunn, Glenn↗