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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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34 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Space Station Module Power Management and Distribution System (SSM/PMAD)

This report provides an overview of the Space Station Module Power Management and Distribution (SSM/PMAD) testbed system and describes recent enhancements to that system. Four tasks made up the original contract: (1) common module power management and distribution system automation plan definition; (2) definition of hardware and software elements of automation; (3) design, implementation and delivery of the hardware and software making up the SSM/PMAD system; and (4) definition and development of the host breadboard computer environment. Additions and/or enhancements to the SSM/PMAD test bed that have occurred since July 1990 are reported. These include: (1) rehosting the MAESTRO scheduler; (2) reorganization of the automation software internals; (3) a more robust communications package; (4) the activity editor to the MAESTRO scheduler; (5) rehosting the LPLMS to execute under KNOMAD; implementation of intermediate levels of autonomy; (6) completion of the KNOMAD knowledge management facility; (7) significant improvement of the user interface; (8) soft and incipient fault handling design; (9) intermediate levels of autonomy, and (10) switch maintenance.

Miller, William↗
NASA NTRS2010

Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations

This chapter will provide a thorough end-to-end description of the process for evaluation of three different data-driven algorithms for anomaly detection to select the best candidate for deployment as part of a suite of IVHM (Integrated Vehicle Health Management) technologies. These algorithms were deemed to be sufficiently mature enough to be considered viable candidates for deployment in support of the maiden launch of Ares I-X, the successor to the Space Shuttle for NASA's Constellation program. Data-driven algorithms are just one of three different types being deployed. The other two types of algorithms being deployed include a "nile-based" expert system, and a "model-based" system. Within these two categories, the deployable candidates have already been selected based upon qualitative factors such as flight heritage. For the rule-based system, SHINE (Spacecraft High-speed Inference Engine) has been selected for deployment, which is a component of BEAM (Beacon-based Exception Analysis for Multimissions), a patented technology developed at NASA's JPL (Jet Propulsion Laboratory) and serves to aid in the management and identification of operational modes. For the "model-based" system, a commercially available package developed by QSI (Qualtech Systems, Inc.), TEAMS (Testability Engineering and Maintenance System) has been selected for deployment to aid in diagnosis. In the context of this particular deployment, distinctions among the use of the terms "data-driven," "rule-based," and "model-based," can be found in. Although there are three different categories of algorithms that have been selected for deployment, our main focus in this chapter will be on the evaluation of three candidates for data-driven anomaly detection. These algorithms will be evaluated upon their capability for robustly detecting incipient faults or failures in the ground-based phase of pre-launch space shuttle operations, rather than based oil heritage as performed in previous studies. Robust detection will allow for the achievement of pre-specified minimum false alarm and/or missed detection rates in the selection of alert thresholds. All algorithms will also be optimized with respect to an aggregation of these same criteria. Our study relies upon the use of Shuttle data to act as was a proxy for and in preparation for application to Ares I-X data, which uses a very similar hardware platform for the subsystems that are being targeted (TVC - Thrust Vector Control subsystem for the SRB (Solid Rocket Booster)).

Martin, Rodney Alexander↗
NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗
DOE OSTI2025

Advanced Intelligent Sensor Development and Demonstration for Future Distribution Systems with High Penetration DERs

The nation’s electric grid is evolving to supply the electricity needed for economic growth, health, and quality of life. As the DOE has pointed out “Old Tools Won’t Solve New Problems” This project conceived, designed, built, and demonstrated a new tool that promises to make the emerging grid dependable at a lower cost. The tool is a smart sensor with the sensitivity and the bandwidth to measure actionable information about grid health that was unmeasurable.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Condition monitoring of permanent magnet AC machines for all-electric transportation systems: State of the art

The current state of the art on emerging and efficient techniques for condition monitoring of permanent magnet (PM) alternating-current (AC) machines deployed in electric vehicle (EV) applications is presented. The discussion includes the most common and specific types of faults in PM motors, such as rotor demagnetisation and stator inter-turn faults, respectively. Fault indicators, such as voltage (v s ) and current (i s ) signals and machine signatures based on motor back electromotive force (EMF) (E B ) and magnetic flux (φ), are taken into account as a measuring quantity in diagnosing motor faults. Other signatures, including thermal analysis, acoustic noise, and vibrations, are also illustrated as some of the emerging techniques in estimating the performance of EV motors while under operations. In addition, various fault modelling methods, condition monitoring techniques, and comprehensive approaches applied in diagnosing the effect of machine faults during its incipient stages are illustrated. Since most of the fault diagnostic techniques discussed here include only machine-based quantities as fault indices/indicators, the provided solutions are therefore found to be more reliable and accurate for diagnosing the motor faults. This comprehensive review study is inclusive of the existing fault diagnostic techniques, which are currently employed in industrial and commercial practices, in addition to the new methodologies proposed by the authors. All the given condition monitoring schemes therefore seem significantly vital in estimating the state of health of PM AC machines while under operation in all-electric transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
NASA NTRS1990

Functional requirements for an intelligent RPC

An intelligent remote power controller (RPC) based on microcomputer technology can implement advanced functions for the accurate and secure detection of all types of faults on a spaceborne electrical distribution system. The intelligent RPC will implement conventional protection functions such as overcurrent, under-voltage, and ground fault protection. Advanced functions for the detection of soft faults, which cannot presently be detected, can also be implemented. Adaptive overcurrent protection changes overcurrent settings based on connected load. Incipient and high-impedance fault detection provides early detection of arcing conditions to prevent fires, and to clear and reconfigure circuits before soft faults progress to a hard-fault condition. Power electronics techniques can be used to implement fault current limiting to prevent voltage dips during hard faults. It is concluded that these techniques will enhance the overall safety and reliability of the distribution system.

Aucoin, B. M.↗
NASA NTRS1980

Grooved terrain on Ganymede

It is postulated that during a period in Ganymede's history when its lithosphere was thin, upwelling convection currents caused incipient rifting accompanied by intensive normal faulting. Where the rifting went to completion, crustal segments separated, spread apart locally, and sheared past one another. Subduction and compression may have occurred in places, but the evidence is inconclusive. The grooved terrain on Ganymede may therefore record an early phase of ice-plate tectonics that caused rifting and drifting of the icy lithosphere, but which, unlike silicate plate tectonics on earth, may have resulted in only minor vertical turnover. It is concluded that grooved terrain grew at the expense of cratered terrain, that cratered tracts were converted into grooved terrain in situ, and that vertical tectonism and shear movements dominated in the restructuring of Ganymede's surface.

Lucchitta, B. K.↗
NASA NTRS1993

Fault geometries and extension in the Valles Marineris, Mars

The central troughs of the Valles Marineris system on Mars are generally regarded to be of tectonic origin because they are linear and bounded by faults. Their radial orientation to the Tharsis rise suggests that at least their incipient formation was influenced by the formation of the rise, but the exact role of Tharsis is not yet well understood. Collapse and erosion may have significantly modified the original tectonic troughs. To better understand the magnitude and mechanism of crustal extension across the Valles Marineris, the attitudes of trough-wall faults that parallel the long axes of the troughs were studied. Our results will help to determine if the extension is related to the Tharsis rise, to a local arch along which the troughs lie, or to other mechanisms. Fault-plane attitudes were obtained by performing a photogrammetric study of fault traces in the walls of Candor and Melas Chasmata. If the exposed wall faults are not major trough-bounding faults, it is assumed that these major faults are buried at the base of the walls and have similar geometries to the exposed faults. Three dimensional coordinates of several points along each fault trace were obtained and three-point solutions to their geometries were constructed. As expected, the results show that the faults dip toward the interior of the troughs, suggesting that they are indeed normal faults associated with trough formation.

Chadwick, D. J.↗
NASA NTRS2009

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Tejada, Arturo↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
NASA NTRSDate not supplied

Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles. The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks, thus introducing depend-ability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric power-train system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.

Fault Isolation↗
NASA NTRS2000

Chuar Group of the Grand Canyon: record of breakup of Rodinia, associated change in the global carbon cycle, and ecosystem expansion by 740 Ma

The Chuar Group (approximately 1600 m thick) preserves a record of extensional tectonism, ocean-chemistry fluctuations, and biological diversification during the late Neoproterozoic Era. An ash layer from the top of the section has a U-Pb zircon age of 742 +/- 6 Ma. The Chuar Group was deposited at low latitudes during extension on the north-trending Butte fault system and is inferred to record rifting during the breakup of Rodinia. Shallow-marine deposition is documented by tide- and wave-generated sedimentary structures, facies associations, and fossils. C isotopes in organic carbon show large stratigraphic variations, apparently recording incipient stages of the marked C isotopic fluctuations that characterize later Neoproterozoic time. Upper Chuar rocks preserve a rich biota that includes not only cyanobacteria and algae, but also heterotrophic protists that document increased food web complexity in Neoproterozoic ecosystems. The Chuar Group thus provides a well-dated, high-resolution record of early events in the sequence of linked tectonic, biogeochemical, environmental, and biological changes that collectively ushered in the Phanerozoic Eon.

NASA Discipline Exobiology↗
NASA NTRS1988

Petrology and tectonic development of supracrustal sequence of Kerala Khondalite Belt, Southern India

The granulite terrain of southern India, of which the Kerala Khondalite belt (KKB) is a part, is unique in exposing crustal sections with arrested charnockite growth in different stages of transformation and in varied lithological association. The KKB with rocks of surficial origin and incipient charnockite development, poses several problems relating to the tectonics of burial of vast area and mechanisms involved in expelling initial H2O (causes of dryness) for granulite facies metamorphism. It is possible to infer the following sequence of events based on the field and laboratory studies: (1) derivation of protoliths of KKB from granitic uplands and deposition in fault bounded basin (cratonic rift); (2) subhorizontal deep burial of sediments; (3) intense deformation of infra and supracrustal rocks; (4) early granulite facies metamorphism predating F sub 2 - loss of primary structure in sediments and formation of charnockites from amphibole bearing gneisses and khondalites from pelites; (5) migmatisation and deformation of metasediments and gneisses; (6) second event of charnockite formation probably aided by internal CO2 build-up; and (7) isothermal uplift, entrapment of late CO2 and mixed CO2-H2O fluids, formation of second generation cordierites and cordierite symplectites.

Kumar, G. R. Ravindra↗
NASA NTRS1991

On the initiation of subduction

Estimates of shear resistance associated with lithospheric thrusting and convergence represent lower bounds on the force necessary to promote trench formation. Three environments proposed as preferential sites of incipient subduction are investigated: passive continental margins, transform faults/fracture zones, and extinct ridges. None of these are predicted to convert into subduction zones simply by the accumulation of local gravitational stresses. Subduction cannot initiate through the foundering of dense oceanic lithosphere immediately adjacent to passive continental margins. The attempted subduction of buoyant material at a mature trench can result in large compressional forces in both subducting and overriding plates. This is the only tectonic force sufficient to trigger the nucleation of a new subduction zone. The ubiquitous distribution of transform faults and fracture zones, combined with the common proximity of these features to mature subduction complexes, suggests that they may represent the most likely sites of trench formation if they are even marginally weaker than normal oceanic lithosphere.

Mueller, Steve↗
NASA NTRS2015

Mission Adaptive UAS Platform for Earth Science Resource Assessment

NASA Ames Research Center has led a number of important Earth science remote sensing missions including several directed at the assessment of natural resources. A key asset for accessing high risk airspace has been the 180 kg class SIERRA UAS platform, providing mission durations of up to 8 hrs at altitudes up to 3 km. Recent improvements to this mission capability are embodied in the incipient SIERRA-B variant. Two resource mapping problems having unusual mission characteristics requiring a mission adaptive capability are explored here. One example involves the requirement for careful control over solar angle geometry for passive reflectance measurements. This challenges the management of resources in the coastal ocean where solar angle combines with sea state to produce surface glint that can obscure the ocean color signal. Furthermore, as for all scanning imager applications, the primary flight control priority to fly the UAS directly to the next waypoint should compromise with the requirement to minimize roll and crab effects in the imagery. A second example involves the mapping of natural resources in the Earth's crust using precision magnetometry. In this case the vehicle flight path must be oriented to optimize magnetic flux gradients over a spatial domain having continually emerging features, while optimizing the efficiency of the spatial mapping task. These requirements were highlighted in several recent Earth Science missions including the October 2013 OCEANIA mission directed at improving the capability for hyperspectral reflectance measurements in the coastal ocean, and the Surprise Valley Mission directed at mapping sub-surface mineral composition and faults, using high-sensitivity magentometry. This paper reports the development of specific aircraft control approaches to incorporate the unusual and demanding requirements to manage solar angle, aircraft attitude and flight path orientation, and efficient (directly geo-rectified) surface and sub-surface mapping, including the near-time optimization of these sometimes conflicting requirements. *

Flight Control↗