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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
NASA NTRS2010

Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

Martin, Rodney A.↗
DOE OSTI2022

Facilitating Machine Learning Collaborations Between Labs, Universities, And Industry

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

Edelen, J.P.↗
NASA NTRSDate not supplied

A survey of thermocouple anomalies: mechanisms, interpretation, and mitigation

Thermocouples (TCs) are a critical diagnostic sensor for high enthalpy environments including thermal protection systems (TPS) for atmospheric re-entry, hypersonic wind tunnels, and arc jet test facilities. They are used for both evaluating facility environmental conditions (enthalpy, heat flux) as well as for measuring in-situ material thermal response under test conditions (thermal conductivity, heat capacity, B’, etc.). Consequently, accurate TC measurements are fundamental for both material and facility verification and validation (V&V). However, a number of TC anomalies have been observed in both ground tests and flight instrumentation. These anomalies are caused via various coupling mechanisms: electrostatic gradients, temperature-dependent electrical shorts, radio pickup, magnetic induction, and pressure-dependent leaks. All of these mechanisms can, if unaddressed, result in obviously compromised results or worse, non-physical measurements which appear reasonable. This work will survey known anomaly mechanisms and discuss ongoing efforts to quantify and mitigate anomaly mechanisms including anomaly modeling, diagnostics, and new TC designs/development.

thermocouple anomalies↗
NASA NTRSDate not supplied

A Survey of Thermocouple Anomalies: Mechanisms, Interpretation, and Mitigation

Thermocouples (TCs) are a critical diagnostic sensor for high enthalpy environments including thermal protection systems (TPS) for atmospheric re-entry, hypersonic wind tunnels, and arc jet test facilities. They are used for both evaluating facility environmental conditions (enthalpy, heat flux) as well as for measuring in-situ material thermal response under test conditions (thermal conductivity, heat capacity, B’, etc.). Consequently, accurate TC measurements are fundamental for both material and facility verification and validation (V&V). However, a number of TC anomalies have been observed in both ground tests and flight instrumentation. These anomalies are caused via various coupling mechanisms: electrostatic gradients, temperature-dependent electrical shorts, radio pickup, magnetic induction, and pressure-dependent leaks. All of these mechanisms can, if unaddressed, result in obviously compromised results or worse, non-physical measurements which appear reasonable. This work will survey known anomaly mechanisms and discuss ongoing efforts to quantify and mitigate anomaly mechanisms including anomaly modeling, diagnostics, and new TC designs/development.

Thermocouple Anomalies↗
NASA NTRSDate not supplied

Dynamic Anomaly Response and Integrated Analysis (DARIA): A Fault Investigation Toolset Supporting Earth-Independent Operations in Future Crewed Mars Missions

NASA's Moon to Mars Objectives outline a strategic vision for human spaceflight culminating in crewed missions to Mars. A critical component of this objective is the development of systems that are capable of being Earth-Independent Operated (EIO). A key aspect of EIO is the ability to rapidly detect, diagnose, and respond to anomalies in crew-supporting habitat and connected systems. To address this, the Dynamic Anomaly Response and Integrated Analysis (DARIA) architecture has been developed. DARIA incorporates a network of compact, wireless sensing devices called the System for Telemetry Amalgamation of Multimodal PrognosticS (STAMPS) for increased state awareness of EIO habitats. Able to perform on-the-fly data acquisition, STAMPS are connected to integrated anomaly data dashboards for crew visualization. DARIA is designed to seamlessly integrate into various off-world environments, including the International Space Station, Lunar Gateway, Artemis Base Camp, and future Mars habitats. By leveraging Commercial Off-The-Shelf (COTS) hardware, the NASA Internet of Things (NASA IoT) framework, and EIO fault detection methods, DARIA provides a cost-effective and adaptable solution for anomaly detection and response in EIO habitats.

Diagnostics↗
NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗
DOE OSTI2025

Subspace-Driven Learning for Anomaly Detection in Process Transients

Nuclear power plant (NPP) monitoring and diagnostic centers are actively investigating and implementing automated anomaly detection algorithms to help plants catch anomalies sooner, thereby preventing or reducing the duration of unexpected shutdowns. Current machine learning-based anomaly detection methods are expected to be highly effective during stable, full-power operations because NPPs typically operate as baseload power generators, meaning there are extensive operating data available from plant equipment. However, it is expected that anomaly detection methods will face significant challenges during transient conditions (i.e., when power output falls below full power) because plants only occasionally operate at these lower power levels, generating sparse transient operational data, and resulting in false alarms or missed detections. Here, to address this issue, transfer learning is used, which for this problem leverages knowledge (in the form of learned features) from stable, full-power operations to improve detection accuracy during transient conditions, even with limited data. In this effort, a novel subspace approach is developed to transfer a subset of the data features from full power operation to transients. This approach is validated through experiments using synthetic data and was found to outperform two baseline transfer learning approaches in anomaly detection performance across a range of amounts of transient data used in the training process.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
NASA NTRS2001

Climate Modeling and Prediction at NSIPP

The talk will review modeling and prediction efforts undertaken as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus will be on atmospheric model results, including its use for experimental seasonal prediction and the diagnostic analysis of climate anomalies. The model's performance in coupled experiments with land and atmosphere models will also be discussed.

Suarez, Max↗
NASA NTRS2016

Mars Cube One (MarCO) Shifting the Paradigm in Relay Deep Space Operations

A very significant challenge in the planetary mission design and operations is communications with ground control teams during critical events and highly risky maneuvers. These include entry, descent, and landing (EDL) and orbit insertion, which should not be carried out in the blind. Although vast planetary distances and long round-trip light times disallow real-time intervention from controllers, acquiring the relevant event performance parameters in near real-time can be imperative for determining the corrective actions needed immediately following or, in the case of significant anomalies, aid in the diagnostic analysis. During several previous Mars missions landing events, and the Huygens probe landing on Titan, the communications strategy relied on proximity links to planetary orbiters, which then relayed the data to the Deep Space Network (DSN). In addition, attempts were successfully made in parallel to receive the signal carrier directly at Earth often using large radio telescopes when the wavelength was outside the DSN’s reception bands. This Direct-to-Earth (DTE) back-up method was only possible due to special techniques utilizing the DSN’s open-loop Radio Science Receivers. In every case, it was very challenging since the link budget of a landing vehicles were designed for proximity orbiter relays and not for distances across the solar system. A new method is introduced since not all future missions can rely on the presence of pre-existing orbiters at their planetary targets to relay their critical data ad, furthermore, most missions would not likely have the resources to implement a reliable DTE link at acceptable data rates, bypassing a need for a relay asset. With the advent CubeSat form-factor spacecraft, one or more, for added reliability, CubeSats can be launched with the primary mission, travel to the target, and be positioned to view the critical event, such as EDL, and carry out real-time relay of the data to the DSN at higher rates. CubeSats have flown in the Earth environment but never flown or been operated in deep space or planetary environment so careful design as well as flight experience are needed. The relay function requires the development of radio and antenna systems to meet challenging specifications. After initial technical demonstration of the concept and operational experience, the cost can decrease as systems become more standardized with increased reliability. This paper describes the invention of the “carry your own relay” concept and the formulation of the mission likely to be the first planetary CubeSat mission called Mars Cube One (MarCO). It also describes the operational concept of relay small spacecraft and their role reducing mission risk as well as overall mission cost.

Asmar, Sami↗
DOE OSTI2026

Tori, Klein bottles, and modulo 8 parity/time-reversal anomalies of 2+1d staggered fermions

We study the symmetries of lattice staggered fermions in 2+1d. Using the symmetries, we can place the system on any sheared torus or Klein bottle. These different backgrounds provide diagnostics of various ’t Hooft anomalies associated with the crystalline symmetries. We then compare the lattice model to its continuum limit. The symmetries of the lattice system are mapped in a nontrivial way to the symmetries of the continuum theories. Using this map, we match the ’t Hooft anomalies on the lattice and the continuum. Along the way, we develop a general formalism to study Hamiltonian lattice models on nontrivial, compact, flat spaces.

Algorithms and Theoretical Developments↗
NASA NTRS2005

Acoustic Holography of the Solar Convection Zone with SOHO-MDI Observations

The original grant with the title stated above was NAG5-10984, awarded to the Solar Physics Research Corporation (SPRC) in July, 2001, and was to be a three-year project. The basic theme of the project was the development and application of computational seismic holography for imaging, diagnostics, and monitoring of magnetic anomalies beneath active regions, in the deep solar interior, and on the Sun's far surface. The project was roughly separated into the following five tasks: (1) A holographic survey of active regions. (2) p-Mode absorption diagnostics of magnetic regions. (3) Acoustic modeling of the shallow subphotospheres of active regions and the quiet-Sun supergranulation based on phase-correlation seismic holography. (4) Seismic holography of the deep convection zone. (5) Improvements in holographic imaging of the far surface of the Sun. Following the death of Karen Harvey, President of SPRC, during the first year, the grant was transferred to Northwest Research Associates as NAG5-12901. Substantial but progress had been made on most of the above tasks in the first year under NAG5-10984, but none were completed. This work was continued under NAG5-12901.

Lindsey, Charles↗
NASA NTRS2013

Data Mining for Anomaly Detection

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

Biswas, Gautam↗
DOE OSTI2021

The case for digital twins in metal additive manufacturing

The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).

36 MATERIALS SCIENCE↗
NASA NTRS1972

Precision signal power measurement

Accurate estimation of signal power is an important Deep Space Network (DSN) consideration. Ultimately, spacecraft power and weight is saved if no reserve transmitter power is needed to compensate for inaccurate measurements. Spectral measurement of the received signal has proved to be an effective method of estimating signal power over a wide dynamic range. Furthermore, on-line spectral measurements provide an important diagnostic tool for examining spacecraft anomalies. Prototype equipment installed at a 64-m-diameter antenna site has been successfully used to make measurements of carrier power and sideband symmetry of telemetry signals received from the Mariner Mars 1971 spacecraft.

Winkelstein, R.↗
NASA NTRS1993

The dynamics of large-scale cyclogenesis over the North Pacific Ocean

Wintertime cases of persistent cyclonic flow abnormalities over the North Pacific were studied with the aim of identifying the primary mechanisms for persistent anomaly development. Processes influencing the time evolution of eddy potential enstrophy and potential vorticity anomalies were examined using intensive diagnostic analyses. Analysis indicates that the primary source region for large-scale development is located over the mid-Pacific jet exit region. Potential enstrophy analysis shows that eddy enstrophy increases result from local conversions between the large-scale eddy and the climatological-mean flow. The occurrence of a form of baroclinic 'self development' during large-scale intensification is suggested by potential vorticity inversions. Baroclinic and barotropic processes contribute positively to the large-scale developments. Results indicate that the primary mechanism for the developments is a large-scale instability of the three-dimensional time-mean flow, and suggest that nonmodel transient growth plays a significant role during development.

Black, Robert X.↗
NASA NTRS1993

Space Shuttle main engine OPAD: The search for a hardware enhanced plume

The process of applying spectroscopy to the Space Shuttle Main Engine (SSME) for plume diagnostics, as it exists today, originated at Marshall Space Flight Center in Huntsville, Alabama, and its implementation was assured largely through the efforts of Sverdrup AEDC, in Tullahoma, Tennessee. This team continues to lead and guide efforts in the plume diagnostics arena. The process, Optical Plume Anomaly Detection (OPAD), formed the basis for various activities in the development of ground-based systems as well as the development of in-flight plume spectroscopy. OPAD currently provides and will continue to provide valuable information relative to future systems definitions, instrumentation development, code validation, and data diagnostic processing. OPAD is based on the detection of anomalous atomic and molecular species in the SSME plume using two complete, stand-alone optical spectrometers. To-date OPAD has acquired data on 44 test firings of the SSME at the Technology Test Bed (TTB) at MSFC. The purpose of this paper will be to provide an introduction to the OPAD system by discussing the process of obtaining data as well as the methods of examining and interpreting the data. It will encompass such issues as selection of instrumentation correlation of data to nominal engine operation, investigation of SSME component erosion via OPAD spectral data, necessity and benefits of plume seeding, application of artificial intelligence (AI) techniques to data analysis, and the present status of efforts to quantify specie erosion utilizing standard plume and chemistry codes as well as radiative models currently under development.

Powers, W. T.↗
NASA NTRS1989

LEADER - An integrated engine behavior and design analyses based real-time fault diagnostic expert system for Space Shuttle Main Engine (SSME)

The LEADER expert system has been developed for automatic learning tasks encompassing real-time detection, identification, verification, and correction of anomalous propulsion system operations, using a set of sensors to monitor engine component performance to ascertain anomalies in engine dynamics and behavior. Two diagnostic approaches are embodied in LEADER's architecture: (1) learning and identifying engine behavior patterns to generate novel hypotheses about possible abnormalities, and (2) the direction of engine sensor data processing to perform resoning based on engine design and functional knowledge, as well as the principles of the relevant mechanics and physics.

Gupta, U. K.↗