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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 NTRS2005

Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and inform manual maneuver decisions. Our multi-modal display of this information provides speech output to issue control surface failure warnings to a lesser-used communication channel and provides graphical displays with pilot-selectable !eve!s of details to issues additional information about the failure. We also describe a potential presentation for flight envelope reduction that can be viewed separately or integrated with an existing attitude indicator instrument. Preliminary results suggest that the inductive approach is capable of detecting that a control surface has failed and determining the type of fault. Furthermore, preliminary evaluations suggest that the interface discloses a concise summary of this information to the pilot.

Spikovska, Lilly↗
NASA NTRS1995

Lunar soil evolution processes and Apollo 16 core 60013/60014

Soils of the 62-cm deep Apollo 16 double drive tube 60013/14 are mature at the top and submature at the bottom. Modal analyses of 5529 grains from the 90-150 micrometers and the 500-1000 micrometers fractions from 12 levels of the core show that, in general, agglutinate abundance increases somewhat monotonically to the top and mimics the Is/FeO profile. There is a general decrease in the modal abundance of monomineralic fragments towards the top, suggesting that agglutinates were formed in part at the expense of monomineralic grains, especially feldspars, which are by far the most abundant mineral in these soils. In detail, the top 27 cm of the core differs from the bottom 21 cm, and the middle 14 cm is intermediate in its properties. In the upper segment, variations in the abundances of feldspars correspond with those of feldspathic fragmental breccias and cataclastic anorthosites; in the bottom segment, a similar but weak correspondence between feldspars and crystalline matrix breccias is observed. Mixing of the comminuted products of these three rock types likely produced the bulk of the core material. Many single feldspars in all size fractions are remarkably fresh, show no damage from shock, and are similar in appearance to the large feldspars in anorthosites and feldspathic fragmental breccias, which we consider to be the primary sources of single feldspars in this core. Major (Na, Al, Si, K, Ca) and minor (Fe, Ba) element analyses of 198 single feldspar grains indicate the presence of only one population of feldspars, which is consistent with our interpretation of feldspar provenance. Classification of 890 monomineralic feldspar, olivine, pyroxene, and glass spherules on the basis of the presence or absence of thin brownish coating--related to reworking at the surface--shows that coated grains are much more abundant in the top segment than in the bottom segment. A comparison with the mixing and maturation model (McKay et al., 1977) of soils in the core 60009/10, some 60 m away from 60013/14, shows that mixtures of an immature, nearly pure plagioclase soil (dominant in 60009/10) and another immature, crystalline breccia-rich soil (dominant in 60013/14) may have matured through in situ reworking to produce the soils under investigation. We conclude that the soils in this core are products of mixing along soil evolution Path 2 of McKay et al. (1974). Superimposed on that soil column is the reworking of the upper part, which has evolved more recently along Path 1. This core thus represents a consanguineous column of the lunar regolith with an upper reworked segment.

short duration↗
DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗