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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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Precise optical systems tested without contamination. Vibration isolator is one of three such units supporting cast-iron table. Table used to test optical systems in vacuum. Isolator made from commercially available components. Scaled-down version finds wider application in electron optics or electron-beam lithography.
Crustal anomaly detection with MAGSAT data is frustrated by the inherent resolving power of the data and by contamination from the external and core fields. The quality of the data might be tested by modeling specific tectonic features which produce anomalies that fall within the proposed resolution and crustal amplitude capabilities of the MAGSAT fields. To test this hypothesis, the north African hotspots associated with Ahaggar, Tibestia and Darfur have been modeled as magnetic induction anomalies due solely to shallower depth to the Curie isotherm surface beneath these features. The MAGSAT data were reduced by subtracting the external and core fields to isolate the scalar and vertical component crustal signals. The predicted model magnetic signal arising from the surface topography of the uplift and the Curie isotherm surface was calculated at MAGSAT altitudes by the Fourier transform technique modified to allow for variable magnetization. In summary it is suggested that the region beneath Ahaggar is associated with a strong thermal anomaly and the predicted anomaly best fits the associated MAGSAT anomaly if the African plate is moving in a northeasterly direction.
The potential of thermal barrier coatings (TBCs) on high-pressure turbine (HPT) nozzles and blades is limited at present by the inability to quantitatively predict TBC life for these components. The goal is to isolate the major TBC failure mechanisms, which is part of the larger program aimed at developing TBC life prediction models. Based on the results of experiments to isolate TBC failure mechanisms, the effects of bond coat oxidation and bond coat creep on TBC integrity is discussed. In bond coat oxidation experiments, Rene prime 80 specimens coated with a NiCrAlY/ZrO2-8 percent Y2O3 TBC received isothermal pre-exposures at 2000 F in static argon, static air, or received no pre-exposure. The effects of oxidation due to the pre-exposures were determined by thermal cycle tests in both static air and static argon at 2000 F. To study the effect of bond coat creep on TBS behavior, four bond coats with different creep properties were evaluated by thermal cycle tests in air at 2000 F. The test results, the relative importance of these two failure mechanisms, and how their effects may be quantified will also be discussed.
This paper presents models to diagnose the condition of the Space Shuttle Main Engine (SSME). A qualitative physics based model derived from the thermodynamic processes and the flow characteristics underlying the SSME operation was developed to detect and isolate engine failures to the component level. This qualitative model was augmented with empirical models derived from the SSME test-stand data to rapidly detect faults which manifest themselves in engine parameter measurements as deviations from the nominal values. The empirical models integrated with the qualitative model offer the potential for building a complex modeling framework for performing comprehensive engine health monitoring.
The work under this grant has been directed to two aspects of the control of flexible spacecraft: (1) the modeling of deployed or erected structures including nonlinear joint characteristics; and (2) the detection and isolation of failures of the components of control systems for large space structures. The motivation for the first of these research tasks is the fact that very large assemblies in space will have to be built or deployed in situ. A likely scenario is, in fact, a combination of these wherein modules which are folded for transportation into orbit are erected to their final configuration and then jointed with other such erected modules to form the full assembly. Any such erectable modules will have joints. It remains to be seen whether or not joints designed for operational assemblies will have nonlinear properties, but it seems prudent to develop a methodology for dealing with that possibility. The motivation for the second of these research tasks is the fact that we foresee large assemblies in space which will require active control to damp vibrations and/or hold a desired shape. Lightweight structures will be very flexible, with many elastic modes having very low frequencies. In order to control these modes well, the control system will likely require many sensors and many actuators, probably distributed over much of the structure. The combination of a large number of control system components with long operational periods virtually guarantees that these systems will suffer control system component failures during operation. The control system must be designed to tolerate failures of some sensors and actuators, and still be able to continue to perform its function.
Two important aspects of the control of large space structures are studied: the modeling of deployed or erected structures including nonlinear joint characteristics; and the detection and isolation of failures of the components of control systems for large space structures. The emphasis in the first task is on efficient representation of the dynamics of large and complex structures having a great many joints. The initial emphasis in the second task is on experimental evaluation of FDI methodologies using ground-based facilities in place at NASA Langley Research Center and Marshall Space Flight Center. The progress to date on both research tasks is summarized.
Scientific contributions made in developing new methods to isolate and map vector magnetic anomalies from measurements made by Magsat are described. In addition to the objective of the proposal, the isolation and mapping of equatorial vector lithospheric Magsat anomalies, isolation of polar ionospheric fields during the period were also studied. Significant progress was also made in isolation of polar delta(Z) component and scalar anomalies as well as integration and synthesis of various techniques of removing equatorial and polar ionospheric effects. The significant contributions of this research are: (1) development of empirical/analytical techniques in modeling ionospheric fields in Magsat data and their removal from uncorrected anomalies to obtain better estimates of lithospheric anomalies (this task was accomplished for equatorial delta(X), delta(Z), and delta(B) component and polar delta(Z) and delta(B) component measurements; (2) integration of important processing techniques developed during the last decade with the newly developed technologies of ionospheric field modeling into an optimum processing scheme; and (3) implementation of the above processing scheme to map the most robust magnetic anomalies of the lithosphere (components as well as scalar).
Recent breakthroughs in remote-sensing technology have led to the development of high spectral resolution imaging sensors for observation of earth surface features. This research was conducted to evaluate the effects of organic matter content and composition on narrowband soil reflectance across the visible and reflective infrared spectral ranges. Organic matter from four Indiana agricultural soils, ranging in organic C content from 0.99 to 1.72 percent, was extracted, fractionated, and purified. Six components of each soil were isolated and prepared for spectral analysis. Reflectance was measured in 210 narrow bands in the 400- to 2500-nm wavelength range. Statistical analysis of reflectance values indicated the potential of high dimensional reflectance data in specific visible, near-infrared, and middle-infrared bands to provide information about soil organic C content, but not organic matter composition. These bands also responded significantly to Fe- and Mn-oxide content.
Robert (1989) reported the presence of unusually enriched hydrogen in the insoluble HF-HCl residue extracted from two chert samples of Eocene and Pliocene ages. Since the presence of heavy hydrogen might be due to the incorporation of extraterrestrial materials, we desired to reexamine the same samples to isolate the D-rich components. Our experiments did not reveal any D-rich components, but the hydrogen isotope composition of the insoluble residue of the two chert samples was well within the range expected for terrestrial organic matter. We also describe a protocol that needs to be followed in the hydrogen isotope analysis of any insoluble organic matter.
This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.
Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.
Astronauts experience a microgravity environment during spaceflight, which results in a central reinterpretation of both vestibular and body axial-loading information by the sensorimotor system. Subjects in bed rest studies lie at 6deg head-down in strict bed rest to simulate the fluid shift and gravity-unloading of the microgravity environment. However, bed rest subjects still sense gravity in the vestibular organs. Therefore, bed rest isolates the axial-unloading component, thus allowing for the direct study of its effects. The Tandem Walk is a standard sensorimotor test of dynamic postural stability. In a previous abstract, we compared performance on a Tandem Walk test between bed rest control subjects, and short- and long-duration astronauts both before and after flight/bed rest using a composite index of performance, called the Tandem Walk Parameter (TWP), that takes into account speed, accuracy, and balance control. This new study extends the previous data set to include bed rest subjects who performed exercise countermeasures. The purpose of this study was to compare performance during the Tandem Walk test between bed rest subjects (with and without exercise), short-duration (Space Shuttle) crewmembers, and long-duration International Space Station (ISS) crewmembers at various time points during their recovery from bed rest or spaceflight.
Electrified aircraft powertrains contain multiple interacting subsystems, making them much more complex than traditional aircraft propulsion systems in terms of integration and control. Electrification enables aircraft to have distributed thrust-producing fans that the flight control system can leverage for enhanced maneuverability, further increasing the control complexity. A NASA concept aircraft, the SUbsonic Single Aft eNgine (SUSAN) Electrofan, is such a vehicle. SUSAN is a series/parallel partial hybrid-electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed and implemented. This paper describes the development of some of these algorithms for system health management applied to the powertrain of the SUSAN concept aircraft.
Electrified aircraft powertrains contain multiple interacting subsystems, making them much more complex than traditional aircraft propulsion systems in terms of integration and control. Electrification enables aircraft to have distributed thrust-producing fans that the flight control system can leverage for enhanced maneuverability, further increasing the control complexity. A NASA concept aircraft, the SUbsonic Single Aft eNgine (SUSAN) Electrofan, is such a vehicle. SUSAN is a series/parallel partial hybrid-electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed and implemented. This paper describes the development of some of these algorithms for system health management applied to the powertrain of the SUSAN concept aircraft.
Electrified aircraft powertrains contain multiple interacting subsystems, making them much more complex than traditional aircraft propulsion systems in terms of integration and control. Electrification enables aircraft to have distributed thrust-producing fans that the flight control system can leverage for enhanced maneuverability, further increasing the control complexity. A NASA concept aircraft, the SUbsonic Single Aft eNgine (SUSAN) Electrofan, is such a vehicle. SUSAN is a series/parallel partial hybrid-electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed and implemented. This paper describes the development of some of these algorithms for system health management applied to the powertrain of the SUSAN concept aircraft.
Electrified aircraft powertrains contain multiple tightly coupled subsystems, making them much more complex than traditional aircraft propulsion systems, both in terms of integration and control. Electrification enables aircraft to have multiple distributed thrust-producing fans that the flight control system can utilize for enhanced maneuverability, further increasing the control complexity. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept aircraft that leverages this technology. SUSAN is a series/parallel partial hybrid electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed, tested, validated, and implemented. This paper describes a piloted evaluation of such an algorithm in scenarios with multiple fan failures, performed in a flight simulator, demonstrating failure recovery and continued safe operation up to the limits of the powertrain. These scenarios are subsequently related to certification requirements.
Electrified aircraft powertrains contain multiple tightly coupled subsystems, making them much more complex than traditional aircraft propulsion systems, both in terms of integration and control. Electrification enables aircraft to have multiple distributed thrust-producing fans that the flight control system can utilize for enhanced maneuverability, further increasing the control complexity. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept aircraft that leverages this technology. SUSAN is a series/parallel partial hybrid electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed, tested, validated, and implemented. This paper describes a piloted evaluation of such an algorithm in scenarios with multiple fan failures, performed in a flight simulator, demonstrating failure recovery and continued safe operation up to the limits of the powertrain. These scenarios are subsequently related to certification requirements.
Introduction: Automated biological experiments on small spacecraft missions have gained prominence over the past decade due to their simplicity, accessibility, and small mass, volume, and power needs. Most recently, the BioSensor microfluidic CubeSat payload aboard BioSentinel used an automated microfluidic cell culture system to study the effects of environmental stressors like deep space radiation and microgravity on yeast growth and metabolism. BioSentinel’s successor, the Lunar Explorer Instrument for space biology Applications (LEIA), will study the effects of lunar gravity and radiation using an improved version of the BioSensor microfluidic platform. The BioSensor payload has great adaptability to host a diverse range of biological experiments with single- and multi-celled organisms in both crewed and uncrewed missions, making it a compelling candidate for future space biology studies in a lunar surface environment. BioSensor Instrumentation on BioSentinel: The first spaceflight mission with the BioSensor, BioSentinel’s biology experiments occurred at three locations -- deep space, ISS and ground. The payload contained 18 microfluidic cards, each featuring 16 growth wells (a total of 288 growth wells). Each well was loaded before launch with desiccated yeast. In space, liquid culture medium (nutrients) was automatically introduced to batches of wells at a time to initiate a series of biology experiments. Temperature was maintained by thin film heaters on both sides of each card. Each well was equipped with three LEDs emitting at 570 nm, 630 nm, and 850 nm, paired with photodetectors to measure cell concentration and the alamarBlue (metabolic indicator dye) color transition from blue to pink. Phenotypic parameters like cell viability, metabolic rate, and generation time can be derived from these measurements. The sequence and timing of fluid fills, optical measurements, and thermal control were stored onboard, but could be updated asynchronously via ground communication. LEIA: LEIA is slated for launch no earlier than 2026 on a CLPS lander. BioSentinel’s BioSensor has been modified for use in LEIA. These improvements include: (a) storage for multiple culture medium types, (b) additional LED color (465 nm) for a new biological assay for antioxidant (carotenoid) production, (c) housing modifications for later biology load before launch, (d) improved isolation between electronic and fluidic components, and (e) improved humidity control for prolonged organism viability in case of post-load launch delay. Future Prospects: The consistent and successful demonstration of complex fluidics platforms alongside reliable instrument operations in a space environment is poised to create strong momentum for BioSensor-based biological experiment payloads. Planned future developments with the BioSensor include extending compatibility to a broader range of organisms and assays. Preliminary work has already demonstrated successful growth of Arabidopsis seedlings in fluidic cards. With a few modifications to the optical assembly, the setup could easily measure photosynthetic traits in plants and cyanobacteria. The addition of fluorescence measurements and generation of novel luminescent assays will elevate BioSensor’s functionality further. Beyond the BioSensor’s potential uses on free-flyer missions, ISS and Gateway, and CLPS landers, deploying the BioSensor to the lunar surface or in an artificial habitat on crewed missions could enable pioneering research on both how life responds to lunar conditions and future bioproduction capabilities making the BioSensor an indispensable tool for future space biology research.