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Report describes structure, preparation, characterization, and applications of films of amorphous-carbon. Amorphous-carbon films potentially useful as masks in x-ray lithography, layers for passivation of high-speed microelectronic circuits, hard films to protect magnetic recording media and optical components from degradation by chemical etching or wear, and radiation detectors.
Mass spectra of products emerging from identical samples of a C-13-enriched polyimide polymer (chemically equivalent to Kapton) under atomic oxygen bombardment in space and in the laboratory were collected. Reaction products unambiguously detected in space were CO-13, NO, (12)CO2, and (13)CO2. These reaction products and two others, H2O and CO-12, were detected in the laboratory, along with inelastically scattered atomic and molecular oxygen. Qualitative agreement was seen in the mass spectra taken in space and in the laboratory; the agreement may be improved by reducing the fraction of O2 in the laboratory molecular beam. Both laboratory and space data indicated that CO and CO2 products come preferentially from reaction with the imide component of the polymer chain, raising the possibility that the either component may degrade in part by the 'evaporation' of higher molecular weight fragments. Laboratory time-of-flight distributions showed: (1) incomplete energy accommodation of impinging O and O2 species that do not react with the surface; and (2) both hyperthermal and thermal CO and CO2 products, suggesting two distinct reaction mechanisms with the surface.
In zero or micro-gravity, type 1 muscle fibers atrophy and lose predominance, especially in slow-twitch muscles. No increase in mononuclear cells has been observed, just as in simple denervation, where both types 1 and 2 fibers atrophy, again without infiltration of cells, but with clear satellite cell proliferation. However, extracellular matrix (ECM) degradation takes place after denervation and if re-innervation is encouraged, functional recovery to near control levels may be achieved. No information is available concerning the ECM milieu, the activation of serine proteases, their efficacy in degrading ECM components and the production of locally-derived natural protease inhibitors (serpins) in effecting surface proteolytic control. In addition, no studies are available concerning the activation of these enzymes in micro- or zero gravity or their response to muscle injury on the ground and what alterations, if any, occur in space. These studies were the basis for the experiments in Cosmos 2044.
Polymer Matrix Composites (PMCs) offer lightweight and frequently low cost alternatives to other materials in many applications. High temperature PMCs are currently used in limited propulsion applications replacing metals. Yet in most cases, PMC propulsion applications are not in the direct engine flow path since particulate erosion degrades PMC component performance and therefore restricts their use in gas turbine engines. This paper compares two erosion resistant coatings (SANRES and SANPRES) on PMCs that are useful for both low and high temperature propulsion applications. Collaborating over a multi-year period, researchers at NASA Glenn Research Center, Allison Advanced Developed Company, and Rolls-Royce Corporation have optimized these coatings in terms of adhesion, surface roughness, and erosion resistance. Results are described for vigorous hot gas/particulate erosion rig and engine testing of uncoated and coated PMC fan bypass vanes from the AE 3007 regional jet gas turbine engine. Moreover, the structural durability of these coatings is described in long-term high cycle fatigue tests. Overall, both coatings performed well in all tests and will be considered for applications in both commercial and defense propulsion applications.
Higher operating temperatures increase the efficiency of aircraft gas turbine engines, but can also degrade internal components. High-pressure turbine blades just downstream of the combustor are particularly susceptible to overheating. Computational fluid dynamics (CFD) computer programs can predict the flow around the blades so that potential hot spots can be identified and appropriate cooling schemes can be designed. Various blade and cooling schemes can be examined computationally before any hardware is built, thus saving time and effort. Often though, the accuracy of these programs has been found to be inadequate for predicting heat transfer. Code and model developers need highly detailed aerodynamic and heat transfer data to validate and improve their analyses. The Transonic Turbine Blade Cascade was built at the NASA Glenn Research Center at Lewis Field to help satisfy the need for this type of data.
Metal oxide semiconductor (MOS) circuits for measuring spatially varying intensities of beams of low-energy charged particles have been developed. These circuits are intended especially for use in measuring fluxes of ions with spatial resolution along the focal planes of mass spectrometers. Unlike prior mass spectrometer focal-plane detectors, these MOS circuits would not be based on ion-induced generation of electrons, and photons; instead, they would be based on direct detection of the electric charges of the ions. Hence, there would be no need for microchannel plates (for ion-to-electron conversion), phosphors (for electron-to-photon conversion), and photodetectors (for final detection) -- components that degrade spatial resolution and contribute to complexity and size. The developmental circuits are based on linear arrays of charge-coupled devices (CCDs) with associated readout circuitry (see figure). They resemble linear CCD photodetector arrays, except that instead of a photodetector, each pixel contains a capacitive charge sensor. The capacitor in each sensor comprises two electrodes (typically made of aluminum) separated by a layer of insulating material. The exposed electrode captures ions and accumulates their electric charges during signal-integration periods.
Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace, demands the need of robust risk monitoring framework for assessing the safety of airspace, ground-structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV's predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV's trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.
Enabling operations of unmanned aerial vehicles (UAVs) in low-altitude airspace demands the need of robust risk monitoring framework for assessing the safety of airspace, ground structures and people. As widespread applications emerge, the need of risk assessment becomes increasingly important for UAV flights beyond visual line-of-sight, especially subjected to off-nominal conditions introduced by component failures, degraded controllability or environmental disturbances such as wind gusts in an urban canyon. From a safety perspective, collision with obstacles can be detrimental not only to the vehicle and payload, but also to the structure and people on ground. Although it is safe to assume that approved UAVs would be equipped with collision avoidance systems, risk of collision which can be predicted for a flight trajectory even before the UAV encounters any obstacle is beneficial to resolve contingencies in its decision-making under off-nominal conditions. In this paper, a framework is presented for computing the risk of collision with obstacle based on a UAV’s predicted trajectory, proximity to static and dynamic obstacles, sub-system state-of-health and external wind conditions. The conditional probability of trajectory deviation is generated using a Bayesian Belief Network (BBN) based on on-board sensor measurements. Further, a kinematic 3-DOF model is implemented to compute deviation in UAV’s trajectory subjected to one case study of off-nominal condition i.e. wind gusts. Finally, the integrated risk factor is demonstrated on real data from experimental flights of an octocopter at NASA Langley Research Center, in presence of simulated obstacles and wind conditions. The proposed approach would enable risk-informed decision making process for timely mitigation of current and future unsafe events.
Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The risk is represented in terms of a matrix with rows corresponding to the likelihood of occurrence of collision and columns representing severity of collision to the vehicle and surrounding structures. Risk likelihood is generated using a Bayesian Belief Network (BBN) that compiles knowledge from related Failure Modes and Effects Analysis (FMEAs) and Subject Matter Experts (SMEs) to determine the probability of collision based on on-board sensor measurements indicative of vehicle health and controllability. Risk severity is computed utilizing a point-mass 3D kinematic model of the vehicle in presence of wind. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.
Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk likelihood of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The deviation in the planned trajectory caused due to wind is computed utilizing a point-mass 3D kinematic simulation model of the vehicle. Likelihood of risk for the flight plan is then analyzed based on generating the probability of collision for each point in the trajectory. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, distance from obstacles, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.
For incorporation of unmanned aerial vehicles into the National Airspace, ensuring safety of the airspace including the vehicles, people, and property on the ground is of utmost importance. One of the safety-critical factors for unmanned aviation flights is the risk of deviating from a planned trajectory resulting in a variety of hazards, including potential loss of separation between vehicle and obstacles or unexpected battery energy consumption. Off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts can lead to unacceptable unexpected deviations from the flight trajectory. It is essential to accurately model such effects on the flight trajectory while computing safety thresholds such as minimum separation from surrounding obstacles, available battery resource to complete the mission or determining delay in the expected time of arrival of flights. In this paper, a tool is presented based on Gaussian Process Regression for wind representation over a pre-defined trajectory for fast, yet approximated, in-time evaluation of possible trajectory deviations caused by wind gusts. The deviation in the planned trajectory caused by wind is further simulated utilizing a 6 degrees-of-freedom (DOF) UAV trajectory simulator comprising of a rotorcraft lumped-mass model with LQRI controller. Both steady-state wind and wind gust effects are investigated. The probability of collision with obstacle is computed and demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in the presence of simulated obstacles and wind conditions. Effect of varying wind conditions and varying UAV airspeed is further demonstrated on experimental flights in the presence of wind measured by ground based weather service stations. The proposed approach would eventually benefit timely mitigation of current and future safety-critical events in autonomous systems by enabling risk-informed decision making.
In recent years, we have received requests for meteorite sample sections to be prepared without using epoxy impregnation or mounting. The reasoning for these requests ranges from potential contamination of analyses from epoxy chemical components to degradation of instrumental vacuum from epoxy outgassing (e.g., SEM, SIMS, EPMA, etc.). An alternative approach that we employ involves sawing off a 1-5 mm thick wafer of the sample. The wafer is ground using silica carbide lapping media wetted with 190 proof ethyl alcohol to make both sides flat and parallel. The sample is then polished on one or both sides using diamond paste or dry diamond powder on a rotating lap wheel until the desired finish is obtained. The sample is cleaned in between steps using 200 proof ethyl alcohol in an ultrasonic cleaner. Friable and altered samples need to have higher thickness in order for polishing to be attempted. Without epoxy to hold the sample together or a glass slide for backing, the sample needs additional material to provide the necessary support structure. The machining steps of this process need to be undertaken at a much slower speed than in traditional thin or thick section preparation.
The aviation industry has long recognized the potential benefits of predictive maintenance, a maintenance strategy that leverages sensor and operational data to predict the future degradation of components. Prescriptive maintenance takes this a step further and considers the entire aviation ecosystem to schedule maintenance actions optimally. With the ability to reduce maintenance costs by up to 30%, as reported by the Department of Energy, these maintenance strategies have been identified to be an important investment to reduce a airline costs. However, despite great interest and technological advances in areas such as diagnostics, prognostics, sensing, computation, and machine learning, the adoption of predictive and prescriptive maintenance has not been widely applied in aviation. To shed light on this issue, we conducted an analysis of the barriers preventing or limiting the adoption of predictive and prescriptive maintenance in aviation. Through discussions with subject matter experts across industry, academia, standards bodies, and government, we identified five key challenges: complexity of prediction; validation, safety assurance, and regulatory challenges; cost of adoption; difficulty in quantifying impact and informing decisions; and data availability, quality, and ownership challenges. This study provides a detailed overview of these barriers and areas where stakeholders could invest to overcome them, aiming to support the scaled adoption of predictive and prescriptive maintenance in aviation.
In solid oxide cells (SOCs), the choice between galvanostatic (constant current) and potentiostatic (constant voltage) modes does not significantly affect the performance of SOCs as long as the cell components remain unchanged and intact. However, the degradation of cell components, which leads to changes in electrochemical and physical properties of the cell, elevates the importance of the selected operating mode. This paper aims to investigate the effects of galvanostatic and potentiostatic operating modes on the evolving properties of SOCs and their subsequent influence on performance durability. Employing non-equilibrium thermodynamic analysis, a crucial approach for understanding the degradation phenomena within an active electrochemical system, this study aims to provide in-depth insights into how these operating modes affect the longevity and efficacy of SOCs. Key findings include: In cases where oxygen electrode (OE) degradation is accelerated by higher partial pressure of oxygen ( p O 2 ), operating under constant voltage electrolysis can mitigate the high p O 2 at the OE|electrolyte (OE|EL) interface. Conversely, if OE degradation occurs more rapidly under a lower p O 2 , constant current electrolysis is more effective in suppressing degradation by achieving a high p O 2 at the OE|EL interface. For degradation of the fuel electrode (FE) due to higher p O 2 , constant current electrolysis is beneficial for more stable performance, which helps maintain low p O 2 at the FE|EL interface. When FE degradation is accelerated by lower p O 2 , constant voltage electrolysis can avert low p O 2 at the FE|EL interface. In practical scenarios, more complex degradation mechanisms come into play, especially when p O 2 significantly deviates from initial conditions. Degradation in one electrode can influence p O 2 in the other electrode, a phenomenon more pronounced in potentiostatic than in galvanostatic electrolysis.
Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active
Lunar environment effects on Surveyor I components, particularly white paint degradation as function of time
A many times heard mantra of solvent degradation management in amine-based post combustion capture is “keep the solvent clean” to minimize solvent consumption. It is assumed that the amine losses would decrease by the removal of metals, degradation products, and reactive trace components which are captured from the flue gas, like NO 2 (as potentially driving components of the amine degradation besides dissolved O 2 ). However, this theoretical hypothesis – based on results from laboratory experiments typically generated with fresh amines – disregards the complexity of the solvent matrix, interaction of potential metal catalysts with degradation products and oxidizing agents, and specific chemical requirements which must be fulfilled before a degradation mechanism can proceed. Degradation of the solvent CESAR1 (aqueous solution of 3.0 molar 2-amino-2-methylpropan-1-ol (AMP) and 1.5 molar piperazine (PZ)) is investigated in a unique long-time test campaign (testing time up to now 40 months; 24/7 operation) without replacement of the solvent inventory at the capture pilot plant at the lignite-fired power plant in Niederaussem. Three solvent management strategies with different effect mechanisms are investigated and evaluated: (a) removal of only anionic compounds and trace elements (within 75 days solvent inventory treated two times) and anionic as well as cationic compounds and trace elements (114 days, inventory treated four times) from the solvent by ion exchange, (b) adsorptive removal of trace elements from the solvent by active carbon in 35% of the operating time, and (c) removal of >80% NO 2 by flue gas pretreatment with thiosulfate/sulfite solution (dosing for 2,000 h). The results of the testing program clearly show that “solvent cleanliness” is not a well-defined parameter and that results from laboratory tests, tests without fully representative industrial flue gasses, and short-term testing of monoethanolamine cannot be generalized for other solvents and industrial application. Furthermore, these results showcase that specific degradation management considering solvent, capture plant and flue gas quality is reasonable. Overshooting efforts for solvent management are contra-productive and produce unnecessary waste streams, efficiency losses and costs.
With growing renewables penetration, there is increased interest in flexible power operation for nuclear reactors. For multi-unit SMRs, in particular the NuScale SMR, which is an integral pressurized water reactor, there are opportunities to optimize flexible power operation across multiple units to limit the degradation of structural and control components. Here, we focus on degradation of in-core components, specifically the control rods and reactor pressure vessel. To perform these studies a high-fidelity, multi-cycle representation of the NuScale SMR is required, with a detailed representation of the structural and control components. To this end, the NuScale SMR has been modelled using the Virtual Environment for Reactor Applications (VERA) software. The entire transition to equilibrium is simulated, from Cycle 1 through to the equilibrium cycle. The equilibrium cycle model shows a good agreement with the NuScale design certification application (DCA) results, with differences attributable to a combination of using public domain data for the present study, and methodological differences. K-effective, power distributions, reactivity coefficients, and boron letdown curves are compared and all found to closely match. This shows that the VERA model is suitable for further studies. (authors)