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Aerodynamic characteristics of large-scale model with lift fan mounted in 5 percent thick triangular wing
The ability of current methodologies to accurately predict the aerodynamic characteristics identified as uncertainties was evaluated for two aircraft configurations. The two wind tunnel models studied horizontal altitude takeoff and landing V/STOL fighter aircraft derivatives.
An evaluation of current prediction methodologies to estimate the aerodynamic uncertainties identified for the E205 configuration is presented. This evaluation was accomplished by comparing predicted and wind tunnel test data in three major categories: untrimmed longitudinal aerodynamics; trimmed longitudinal aerodynamics; and lateral-directional aerodynamic characteristics.
The aerodynamic characteristics of the components of the baseline E205 configuration is presented. Geometric variations from the baseline E205 configuration are also given including a matrix of conrad longitudinal locations and strake shapes.
The longitudinal and lateral-directional aerodynamic characteristics of the RALS R104 wind tunnel model are summarized. Configurations for the E205 are also presented for comparison.
The objectives are to demonstrate the change in mechanical properties of a thermoplastic polymer when the polymer is heated above the glass transition temperature. The instructor or student can perform this demonstration. In preparation for the demonstration, fill the flask or beaker 3/4 full with water and bring the water to a boil. First observe the initial stiffness of the toothbrush by slightly flexing it. Note that it is fairly stiff and retains its initial shape when the forces are removed. If you were to apply enough force, the toothbrush would break in a somewhat brittle fashion. Now place the toothbrush in the boiling water for 2 minutes. After the 2 minutes are up, remove the toothbrush from the boiling water with the washcloth. At this time, you will be able to bend the toothbrush easily. When it cools it will retain this bent shape. You've just demonstrated heating the polymer above the glass transition temperature, demonstrating thermoplastic softening. Instructor notes are provided.
This paper addresses the design considerations for Earth observation spacecraft bus and payload subsystems such that cost-effective spacecraft maintainability is enhanced through optimized reliability and the application of robotic on-orbit support. In the past, for most satellites, maintainability has been associated with the clever application of telemetry reconfiguration and the use of redundant systems as necessary over the life cycle of the spacecraft. This presentation addresses the opportunities and challenges of leveraging the extensive work already accomplished in the development of on-orbit servicing technologies.
Brief presentation highlighting the accomplishments of NASA upon its 50th anniversary. NASA's first manned space flight, voyage to the moon, planetary exploration, space station construction, international cooperation, space habitat construction and the deployment of multiple satellites including the Hubble Space Telescope, Gamma Ray Observatory, Magellan and Galileo. More recent efforts include the construction of the Ares transportation system and a return to human exploration beyond low-Earth orbit. The author also urges for continued space exploration via the National Space Policy through the authorization of Congress.
The Environmental Effects Branch at the Marshall Space Flight Center supports a myriad array of programs for NASA, DoD, and commercial space including human exploration, advanced space propulsion, improving life on Earth, and the study of the Sun, the Earth, and the solar system. The branch provides testing, evaluation, and qualification of materials for use on external spacecraft surfaces and in contamination-sensitive systems. Space environment capabilities include charged particle radiation, ultraviolet radiation, atomic oxygen, impact, plasma, and thermal vacuum, anchored by flight experiments and analysis of returned space hardware. These environmental components can be combined for solar wind or planetary surface environment studies or to evaluate synergistic effects. The Impact Testing Facility allows simulation of impacts ranging from sand and rain to micrometeoroids and orbital debris in order to evaluate materials and components for flight and ground-based systems. The Contamination Control Team is involved in the evaluation of environmentally-friendly replacements for HCFC-225 for use in propulsion oxygen systems, developing cleaning methods for additively manufactured hardware, and reducing risk for the Space Launch System.
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BACKGROUND The objective of this study was to evaluate the cerebral and ocular physiological and anatomical effects of head-down tilt bedrest (HDBR) with and without daily artificial gravity (AG) in healthy subjects to provide novel insights into possible countermeasures for the Spaceflight Associated Neuro-ocular Syndrome (SANS). Previous long-term studies using the spaceflight analog of strict 6° HDBR for 30 days revealed changes in ocular structure, including optic disc edema. The effects of an AG countermeasure using intermittent centrifugation to restore upright hydrostatic gradients and reduce the ocular and brain anatomical effects has not been explored. METHODS The effects of 60-day exposure to simulated microgravity (6° HDBR) on the cerebral, ocular and vestibular systems with and without the intervention of daily 30 minute exposure to AG (short-arm centrifugation) was evaluated in 24 healthy subjects. Non-invasive measurements of cerebral blood flow, intracranial blood volume (near infrared spectroscopy, NIRS), cerebral and ocular structure (MRI and optical coherence tomography), internal jugular vein area (ultrasound), lateral ventricular volumes (MRI), intraocular pressure, and sensorimotor/vestibular (i.e. balance and coordination) systems were made during various time-points during the bedrest study. Additional measures with ultrasound and NIRS were made during centrifugation. RESULTS The study began in Spring 2019 and all 24 subjects completed the testing by end of 2019. Data analysis continues. We report the following updated results for 2021. MRI showed significant decreases in carotid artery flow, increases in brain and CSF volumes, and aqueductal flow velocities in all subjects in HDT compared to baseline supine, but no effects of the short duration AG. All groups showed increased posterior globe flattening during bedrest compared to baseline, without protection from AG. As previously reported we observed chorioretinal folds for the first time in a bedrest analog, as well as optic disc edema, with no protection from the short-term AG. Data from the study is now being transmitted to the NASA LSDA which will allow for future research. CONCLUSIONS The 6 degree HDBR analog produced significant changes in the anatomical and physiological functions of the brain and eye in healthy subjects. Daily 30 minute exposure to AG with short-arm centrifugation had significant transient effects on physiology of the cerebrovascular system, but was not sufficient to prevent the anatomical effects of HDBR on the brain and eye.
In this article, we calculated global land evapotranspiration for 2003 to 2019 using a massbalance approach. To do this, we calculated evapotranspiration as the residual of the water balance, using an ensemble of datasets for precipitation, discharge and total water storage change. We made an error in calculating the global mean precipitation: we used arithmetic averaging to calculate the mean, instead of calculating a spatially weighted mean to account for the changing grid box size with latitude. As a result, the magnitudes of the global mean precipitation time series were underestimated. This impacted the subsequent calculation of global mean evapotranspiration, resulting in the mean evapotranspiration values being underestimated and altering some results. We are therefore retracting this article. We thank Ning Ma and others for bringing this error to our attention.
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ABSTRACT This paper provides a catalogue of stars, quasars, and galaxies for the Southern Photometric Local Universe Survey Data Release 2 (S-PLUS DR2) in the Stripe 82 region. We show that a 12-band filter system (5 Sloan-like and 7 narrow bands) allows better performance for object classification than the usual analysis based solely on broad bands (regardless of infrared information). Moreover, we show that our classification is robust against missing values. Using spectroscopically confirmed sources retrieved from the Sloan Digital Sky Survey DR16 and DR14Q, we train a random forest classifier with the 12 S-PLUS magnitudes + 4 morphological features. A second random forest classifier is trained with the addition of the W1 (3.4 $\mu\mathrm{m} $) and W2 (4.6 $\mu\mathrm{m} $) magnitudes from the Wide-field Infrared Survey Explorer (WISE). Forty-four per cent of our catalogue have WISE counterparts and are provided with classification from both models. We achieve 95.76 per cent (52.47 per cent) of quasar purity, 95.88 per cent (92.24 per cent) of quasar completeness, 99.44 per cent (98.17 per cent) of star purity, 98.22 per cent (78.56 per cent) of star completeness, 98.04 per cent (81.39 per cent) of galaxy purity, and 98.8 per cent (85.37 per cent) of galaxy completeness for the first (second) classifier, for which the metrics were calculated on objects with (without) WISE counterpart. A total of 2926 787 objects that are not in our spectroscopic sample were labelled, obtaining 335 956 quasars, 1347 340 stars, and 1243 391 galaxies. From those, 7.4 per cent, 76.0 per cent, and 58.4 per cent were classified with probabilities above 80 per cent. The catalogue with classification and probabilities for Stripe 82 S-PLUS DR2 is available for download.
We construct cosmic void catalogues with the dive void finder upon SDSS BOSS DR12 and eBOSS DR16 galaxy samples with BAO reconstruction, and perform a joint BAO analysis using different types of galaxies and the corresponding voids. The BAO peak is evident for the galaxy–galaxy, galaxy–void, and void–void correlation functions of all data sets, including the ones cross-correlating LRG and ELG samples. Two multitracer BAO fitting schemes are tested, one combining the galaxy and void correlation functions with a weight applied to voids, and the other using a single BAO dilation parameter for all clustering measurements. Both methods produce consistent results with mock catalogues, and on average ~10 per cent improvements of the BAO statistical uncertainties are observed for all samples, compared to the results from galaxies alone. By combining the clustering of galaxies and voids, the uncertainties of BAO measurements from the SDSS data are reduced by 5–15 per cent, yielding 0.9 per cent, 0.8 per cent, 1.1 per cent, 2.3 per cent, and 2.9 per cent constraints on the distance $D_{_{\rm V}}(z)$, at effective redshifts 0.38, 0.51, 0.70, 0.77, and 0.85, respectively. When combined with BAO measurements from SDSS MGS, QSO, and Lyα samples, as well as the BBN results, we obtain $H_0 = 67.58 \pm 0.91\, {\rm km}\, {\rm s}^{-1}\, {\rm Mpc}^{-1}$, Ω m = 0.290 ± 0.015, and $\Omega _\Lambda h^2 = 0.3241 \pm 0.0079$ in the flat-ΛCDM framework, where the 1σ uncertainties are around 6 per cent, 6 per cent, and 17 per cent smaller respectively, compared to constraints from the corresponding anisotropic BAO measurements without voids and LRG–ELG cross-correlations.
ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.
Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87’s well-studied GC system to implement supervised machine learning (ML) classification algorithms – specifically random forest and neural networks – to identify GCs from foreground stars and background galaxies, using ground-based photometry from the Canada–France–Hawaii Telescope (CFHT). We compare these two ML classification methods to studies of ‘human-selected’ GCs and find that the best-performing random forest model can reselect 61.2 per cent ± 8.0 per cent of GCs selected from HST data (ACSVCS) and the best-performing neural network model reselects 95.0 per cent ± 3.4 per cent. When compared to human-classified GCs and contaminants selected from CFHT data – independent of our training data – the best-performing random forest model can correctly classify 91.0 per cent ± 1.2 per cent and the best-performing neural network model can correctly classify 57.3 per cent ± 1.1 per cent. ML methods in astronomy have been receiving much interest as Vera C. Rubin Observatory prepares for first light. The observables in this study are selected to be directly comparable to early Rubin Observatory data and the prospects for running ML algorithms on the upcoming data set yields promising results.
We present a study on the incidence of major mergers and their impact on the triggering of nuclear activity in 47 type I and 236 type II optically selected AGN from the MaNGA DR15 sample. From an estimate of non-parametric image predictors (Gini, M_20, concentration (C), asymmetry (A), clumpiness (S), Sérsic index (n), and shape asymmetry ()) using the SDSS images, in combination with a Linear Discriminant Analysis Method, we identified major mergers and merger stages. We reinforced our results by looking for bright tidal features in our post-processed SDSS and DESI legacy images. We find a statistically significant higher incidence of major mergers of 29 per cent ± 3 per cent in our type I+II AGN sample compared to 22 per cent ± 0.8 per cent for a non-AGN sample matched in redshift, stellar mass, colour, and morphological type, finding also a prevalence of post-coalescence (51 per cent ± 5 per cent) over pre-coalescence (23 per cent ± 6 per cent) merger stages. The levels of AGN activity among our massive major mergers are similar to those reported in other works using [O iii] tracers. However, similar levels are produced by our AGN-galaxies hosting stellar bars, suggesting that major mergers are important promoters of nuclear activity but are not the main nor the only mechanism behind the AGN triggering. The tidal strength parameter Q was considered at various scales looking for environmental differences that could affect our results on the merger incidence, finding non-significant differences. Finally, the H-H β diagram could be used as an empirical predictor for the flux coming from an AGN source, useful to correct photometric quantities in large AGN samples emerging from surveys.