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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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DOE OSTI2026

Initial Feasibility Assessment and Broader Strategy Development for Fiber Integration via Advanced Manufacturing

Structural health monitoring is critical for ensuring the operational safety and cost-competitiveness of the developing advanced reactor designs. The extreme operational envelopes of these advanced architectures, having operating temperatures ranging 400°C–1,000°C and heightened displacement damage doses, render conventional commercially available piezoelectric transducers and resistive strain gauges unviable. Optical fiber sensors present an attractive solution for advanced radiation-hardened instrumentation due to their high thermal stability and distributed sensing capabilities. However, their deployment in embedded applications can be hindered by the severe thermomechanical strain driven by the coefficient of thermal expansion mismatch between fused silica glass and structural metal alloys like stainless steel (e.g., SS316L).

36 MATERIALS SCIENCE↗

NASA NTRS1982

Atmosphere Analyzer

California Measurements, Inc.'s model PC-2 Aerosol Particle Analyzer is produced in both airborne and ground-use versions. Originating from NASA technology, it is a quick and accurate method of detecting minute amounts of mass loadings on a quartz crystal -- offers utility as highly sensitive detector of fine particles suspended in air. When combined with suitable air delivery system, it provides immediate information on the size distribution and mass concentrations of aerosols. William Chiang, obtained a NASA license for multiple crystal oscillator technology, and initially developed a particle analyzer for NASA use with Langley Research Center assistance. Later his company produced the modified PC-2 for commercial applications Brunswick Corporation uses the device for atmospheric research and in studies of smoke particles in Fires. PC-2 is used by pharmaceutical and chemical companies in research on inhalation toxicology and environmental health. Also useful in testing various filters for safety masks and nuclear installations.

Source record↗

NASA NTRS2013

Distilling the Verification Process for Prognostics Algorithms

The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm depends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognostics algorithm is demonstrated as the prognostics algorithm moves up TRLs.

verification↗

NASA NTRSDate not supplied

A Framework for Software Health Management Using Bayesian Statistics

Software Health Management (SWHM) must make sure that the software always remains in safe and healthy regions of the state space. Boundaries between healthy and unhealthy regions are important for the detection of violations and health management.In this position paper, we present a framework, which employs techniques from Bayesian statistical modeling and active learning to efficiently characterize health boundaries in high-dimensional spaces. We will discuss, how this framework supports SWHM during design time and during operation of learning/adapting software systems

software health management↗

DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗

DOE OSTI2025

Proactively Addressing Employee Well-Being to Foster Workplace Safety in a U.S. National Laboratory

The daily conduct of high-risk, high-consequence work, by its very nature, can be mentally demanding. Research demonstrates that failure to manage or mitigate these demands can degrade psychological well-being and mental health. Degradations in employee well-being impact individual performance, jeopardizing safety and mission success in the workplace. This Commentary describes efforts taken at Sandia National Laboratories over the past eight years to evaluate, understand, learn from, and mitigate factors such as stress, burnout, work-life imbalances, and disengagement in the workplace that can degrade employee well-being. After evaluating these four facets of employee well-being, Sandia National Laboratories initiated several programs to address observations and outcomes, including the Thrive program, the Take 10 Initiative, work-life balance resources, employee resource groups such as the Sandia Parents Group, and Workplace Improvement Networks. Collectively, the intent of these programs is to ensure employee mental readiness to conduct hazardous high-risk work effectively and safely. Preliminary data suggest that these programs are succeeding. In conclusion, other national laboratories and organizations, regardless of size, may wish to apply similar approaches to improve employee well-being and thereby increase the likelihood of mission success.

Absorption↗

NASA NTRS2016

Real-Time Safety Monitoring and Prediction for the National Airspace System

As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have both an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecasts, predicted health of assets in the airspace, and so on. To this end, we have developed a Real-Time Safety Monitoring (RTSM) that first, estimates the state of the NAS using the dynamic models. Then, given the state estimate and a probability distribution of future inputs to the NAS, the framework predicts the evolution of the NAS, i.e., the future state, and analyzes these future states to predict the occurrence of unsafe events. The entire probability distribution of airspace safety metrics is computed, not just point estimates, without significant assumptions regarding the distribution type and or parameters. We demonstrate our overall approach by predicting the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress.

National Airspace System↗

NASA NTRS2018

New Sensors and Techniques for Aircraft Engine Health Monitoring

The ability to monitor the health of the rotating components in aircraft engines is of major interest to aero community in improving safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, able to operate in a high temperature environment, and sensitive enough to detect minute changes and hidden flaws before undesired events occur. The National Aeronautics and Space Administration (NASA), through several Aviation Safety Programs, has taken a leadership role in the development of new sensor technologies and techniques for the in-situ health monitoring of aircraft turbine engines. Along with the development of new instrumentation for aircraft engines, the Glenn Research Center has a diverse set of ground test facilities that are used to simulate the conditions that a vehicle would see in flight allowing new concepts to be safely developed and evaluated on the ground before progressing to actual flight. In this presentation Mark will give an overview of the NASA Glenn Research Center, discuss its past aviation safety initiatives, present some select sensors that have been developed for aircraft engine safety, and then conclude with some new measurement techniques that have been developed for use at the NASA Glenn test facilities, enabling better measurement and simulation of flight conditions. He will discuss current research and development efforts that he is working on at the NASA Glenn Research Center in the areas of new sensors and techniques for aircraft engine health monitoring. This work is being conducted in support of aviation safety.

Turbine↗

NASA NTRS2008

How Past Loss of Control Accidents May Inform Safety Cases for Advanced Control Systems on Commercial Aircraft

This paper describes five loss of control accidents involving commercial aircraft, and derives from those accidents three principles to consider when developing a potential safety case for an advanced flight control system for commercial aircraft. One, among the foundational evidence needed to support a safety case is the availability to the control system of accurate and timely information about the status and health of relevant systems and components. Two, an essential argument to be sustained in the safety case is that pilots are provided with adequate information about the control system to enable them to understand the capabilities that it provides. Three, another essential argument is that the advanced control system will not perform less safely than a good pilot.

Holloway, C. M.↗

DOE OSTI1989

Nuclear Safety [Vol. 30, No. 3, July-September 1989]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 325 Safety of Framatome Advanced Nuclear Steam Supply Systems Designs by J. A. Charles and D. Lange, 333 Book Review of Nuclear Accidents: Intervention Levels for the Protection of the Public by H. B. Piper; ACCIDENT ANALYSIS: 335 Living PRA Computer Systems by S. C. Dinsmore and H.-P. Balfanz, 343 Summary of ICAP Assessments of RELAP5/MOD2 by W. E. Driskell and R. G. Hanson; CONTROL AND INSTRUMENTATION: 352 Thermal Performance Monitoring System at Maanshan Nuclear Power Plant by H.-J. Chao, Y.-P. Lin, G.-H. Jou, L.-Y. Liao, and Y.-B. Chen; DESIGN FEATURES: 358 Warning Systems for Nuclear Power Plant Emergencies by J. H. Sorensen and D. S. Mileti; WASTE AND SPENT FUEL MANAGEMENT: 371 Activities Related to Waste Management Compiled by E G. Silver; OPERATING EXPERIENCES: 382 Steam Generator Tube Performance: Experience with Water-Cooled Nuclear Power Reactors During 1985 by O. S. Tatone and R. L. Tapping, 400 Systems Interaction Analyses: Concepts and Techniques (Part II) by M. D. Muhlheim and G. A. Murphy, 413 Reactor Shutdown Experience Compiled by J. W. Cletcher, 416 Operating U.S. Power Reactors Compiled by E G. Silver; RECENT DEVELOPMENTS: 440 General Administrative Activities Compiled by E G. Silver, 460 Reports, Standards, and Safety Guides by D. S. Queener, 466 Status of Power-Reactor Licensing Activities Compiled by E G. Silver, 470 Proposed Rule Changes as of Mar. 31, 1989; ANNOUNCEMENTS: 334 Proceedings Published, 351 CEC Seminar on Methods and Codes for Assessing the Off-Site Consequences of Nuclear Accidents, 357 Short Course on Multiphase Flow and Heat Transfer: Bases and Applications in A: The Nuclear Power Industry B: The Process Industries, 357 International Conference on Probabilistic Safety Assessment and Management, 478 International Topical Meeting on the Safety, Status, and Future of Non-Commercial Reactors and Irradiation Facilities, 475 The Authors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

NCS Program Health Tool

At the end of 2019, there were assessments of multiple safety management programs relied on by NCS that had significant issues identified. Discuss outliers with responders. Better definitions of categories. Expand audience. Move from Excel spreadsheet to web-based.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2004

Sensor Needs for Control and Health Management of Intelligent Aircraft Engines

NASA and the U.S. Department of Defense are conducting programs which support the future vision of "intelligent" aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized.

Simon, Donald L.↗

NASA NTRS2002

Propulsion Controls and Health Management Research at NASA Glenn Research Center

Garg, Sanjay↗

DOE OSTI1995

Nuclear Safety [Vol. 36, No. 1, January-June 1995]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. THE CHORNOBYL ACCIDENT: 1 The Chornobyl Accident Revisited, Part II: The State of the Nuclear Fuel Located Within the Chornobyl Sarcophagus, A A. Borovoi and A. R. Sich; GENERAL SAFETY CONSIDERATIONS: 33 Nuclear Power Safety in Central and Eastern Europe, R. Wilson; 46 Safety of Nuclear Power Reactors in the Former Eastern European Countries, S. Chakraborty; 53 Technical Note: On the Definition of Common-Cause Failures, H. Paula; ACCIDENT ANALYSIS: 58 Modeling and Analysis of Core-Debris Recriticality During Hypothetical Severe Accidents in the Advanced Neutron Source Reactor, S.-H. Kim, V. Georgevich, D. B. Simpson, C. O. Slater, and R. P. Taleyarkhan; 68 Ignitability of Hydrogen/Oxygen/Diluent Mixtures in the Presence of Hot Surfaces, R. K. Kumar and G. W. Koroll; 94 Coupled RELAP5 and CONTAIN Accident Analysis Using PVM, K. A. Smith, A. J. Baratta, and G. E. Robinson; CONTROL AND INSTRUMENTATION: 109 Application of Fuzzy Logic in Nuclear Reactor Control Part I: An Assessment of State-of-the-Art, A. S. Heger, N. K. Alang-Rashid, and M. Jamshidi; DESIGN FEATURES: 122 Twenty-Third DOE/NRC Nuclear Air-Cleaning and Treatment Conference, R. R. Bellamy, J. J. Hayes, and M. W. First; ENVIRONMENTAL EFFECTS: 135 Atmospheric Dispersion and the Radiological Consequences of Normal Airborne Effluents from a Nuclear Power Plant, D. Fang, C. Z. Sun, and L. Yang; 142 Calculation of Distribution Coefficients for Radionuclides in Soils and Sediments, I. Puigdomenech and U. Bergstrom: OPERATING EXPERIENCES: 155 Reactor Shutdown Experience, Compiled by J. W. Cletcher; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 158 Operating Experience Feedback Report—Reliability of Safety-Related Steam Turbine-Driven Standby Pumps Used in U.S. Commercial Nuclear Power Plants, J. R. Boardman; 166 Turbine Building Hazards, H. L Ornstein; RECENT DEVELOPMENTS: 169 Reports, Standards, and Safety Guides, D. S. Queener; 175 Proposed Rule Changes as of Dec. 31,1994; ANNOUNCEMENTS: 32 Harvard School of Public Health In-Place Filter Testing Workshop; 134 International Conference on Advances in the Operational Safety of Nuclear Power Plants; 193 30th Tennessee Industries Week; 193 DOE Technical Standards Program 1995 Workshop; 194 Multiphase Flow Experiments and Instrumentation; 180 The Authors; 185 Indexes to Nuclear Safety, Volumes 34 and 35.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2008

Sensor Selection and Data Validation for Reliable Integrated System Health Management

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Garg, Sanjay↗

NASA NTRS1991

Real-time in-flight engine performance and health monitoring techniques for flight research application

Procedures for real time evaluation of the inflight health and performance of gas turbine engines and related systems were developed to enhance flight test safety and productivity. These techniques include the monitoring of the engine, the engine control system, thrust vectoring control system health, and the detection of engine stalls. Real time performance techniques were developed for the determination and display of inflight thrust and for aeroperformance drag polars. These new methods were successfully shown on various research aircraft at NASA-Dryden. The capability of NASA's Western Aeronautical Test Range and the advanced data acquisition systems were key factors for implementation and real time display of these methods.

Ray, Ronald J.↗

NASA NTRS2004

Controls and Health Management Technologies for Intelligent Aerospace Propulsion Systems

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Technology Branch at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of an Intelligent Engine. The key enabling technologies for an Intelligent Engine are the increased efficiencies of components through active control, advanced diagnostics and prognostics integrated with intelligent engine control to enhance component life, and distributed control with smart sensors and actuators in an adaptive fault tolerant architecture. This paper describes the current activities of the Controls and Dynamics Technology Branch in the areas of active component control and propulsion system intelligent control, and presents some recent analytical and experimental results in these areas.

Garg, Sanjay↗

NASA NTRS2012

Turbine Engine Disk Rotor Health Monitoring Assessment Using Spin Tests Data

Detecting rotating engine component malfunctions and structural anomalies is increasingly becoming a crucial key feature that will help boost safety and lower maintenance cost. However, achievement of such technology, which can be referred to as a health monitoring remains somewhat challenging to implement. This is mostly due to presence of scattered loading conditions, crack sizes, component geometry and material properties that hinders the simplicity of imposing such application. Different approaches are being considered to assist in developing other means of health monitoring or nondestructive techniques to detect hidden flaws and mini cracks before any catastrophic events occur. These methods extend further to assess material discontinuities and other defects that have matured to the level where a failure is very likely. This paper is focused on presenting data obtained from spin test experiments of a turbine engine like rotor disk and their correlation to the development of a structural health monitoring and fault detection system. The data collected includes blade tip clearance, blade tip timing measurements and shaft displacements. The experimental results are collected at rotational speeds up to 10,000 Rpm and tests are conducted at the NASA Glenn Research Center s Rotordynamics Laboratory via a high precision spin system. Additionally, this study offers a closer glance at a selective online evaluation of a rotating disk using advanced capacitive, microwave and eddy current sensor technology.

Abdul-Aziz, Ali↗