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At least 73 records · Page 4

Characterizing Detailed Grain Shape and Size Distribution Properties of Lunar Regolith

Introduction: As the nation prepares to return to the Moon, there is an increasing need for testing tools, instruments, and equipment in simulated environments on Earth to ensure successful operations during lunar missions. Regolith will affect all aspects of future lunar missions, from plume interactions during landing to space suit and tool design [1]. Because of this, it is important to understand the grain shape and size properties of lunar regolith and how those influence regolith behavior in order to prepare for these missions. This knowledge is also vital to create more accurate lunar regolith simulants for testing equipment in a lunar environment. While particle size analyses have been performed on most Apollo soils using simple sieving, shape has only been crudely addressed [2]. This work analyzes 4 lunar regolith samples to provide a better understanding of these size and shape parameters and will provide a more accurate baseline of data to create high-fidelity lunar regolith simulants. Methods: New technologies exist today that are capable of measuring size and shape simultaneously for hundreds of thousands of particles in a single measurement. We conducted a rigorous analysis of the particle size distribution (PSD) as well as the size-dependent 2D and 3D shape parameters of lunar regolith samples of different compositions and maturity levels. This analysis was done using a Microtrac SYNC which provides a unique combination of tri-laser diffraction and Dynamic Image Analysis (DIA). Sample Selection. Four regolith samples were selected for analysis based on maturity level and lunar terrain type: • 10084 – Mature high-Ti mare regolith • 15601 – Immature low-Ti mare regolith • 64501 – Mature highland regolith • 67461 – Immature highland regolith Sample Analysis. After receiving the samples, each sample was imaged with an optical microscope (Figure 1). To obtain 2D and 3D particle size and shape, 0.1 g of each sample was then added to the SYNC for analysis, which outputs more than 30 size and shape parameters for each individual grain as well as the complete size distribution from 0.01-2000 μm by blending laser diffraction and DIA together. For the 0.1 g sample masses requested, ~100,000 grains per sample were captured by DIA. Results: From the PSD analysis, it can be seen that the average particle size of 10084 is ~24.5 µm which is much smaller than the average particle size of the Apollo sample collection (~72 µm) [3], while the average particle size of samples 15601, 64501, and 67461 are larger than the Apollo sample average at ~106 µm, ~103 µm, and ~118.5 µm respectively. The size and shape measurements for the samples output ~30 parameters, four of which were focused on for this study: sphericity, aspect ratio, roundness, and concavity (Figure 3). However, after analyzing the plots it was found that only sphericity and aspect ratio showed differences between the samples. These two parameters are measured on a scale from 0 to 1, with 1 being a perfect sphere with equal dimensions. The results show that the sphericity values are slightly high-er for the mature samples 10084 and 64501 (~0.96) than they are for samples 15601 and 67461 (~0.95) (Figure 4). The aspect ratio values are slightly lower for samples 64501 (~0.7) and 67461 (~0.74) than they are for samples 10084 (~0.76) and 15601 (~0.8) (Figure 5). Discussion: Mature regoliths are those that have been exposed to micrometeorite and solar wind bombardment for long periods of time, breaking up particles and causing them to become more rounded [4]. Therefore, the smaller particle sizes and higher sphericity values for samples 10084 and 64501 are expected due to their higher maturity level compared to 15601 and 67461. However, the aspect ratio values are not dependent on maturity and are instead dependent on terrain type. The lower aspect ratio values for the high-land samples is potentially due to the higher plagioclase content, which occurs in elongated particles and does not break down as easily as the pyroxenes or olivines that are present in the mare. Conclusion and Future Work: The results of this work provide a baseline of high-quality data that will contribute to the creation of high-fidelity lunar simulants and will greatly benefit NASA’s efforts of establishing a human presence on the Moon. Future work includes performing unsupervised image classification on the ~105 particle images per sample in order to identify different classes of grains. These grain classes can then be linked to detailed shape properties, and the relative abundance of each class in the samples can be compared. Acknowledgments: We would like to thank the Extraterrestrial Materials Analysis Group (ExMAG) and the Astromaterials Allocation Review Board (AARB) for allocating lunar regolith samples 10084,9010, 15601,365, 64501,249, and 67461,171 for this work. References: [1] Taylor, L. A. et al. (2005) AIAA #2510. [2] Katagiri et al. (2015) ASCE. [3] Carrier III, W. D. (2005) Lunar Geotech Institute Tech Report. [4] McKay, D. S. et al. (1991) The Lunar Sourcebook, Chapter 7.

S R Deitrick↗

Processing and analysis of radiometer measurements for airborne reconnaissance

This paper describes selected results of airborne, radiometric imaging measurements at 90 GHz and 140 GHz relevant for the application in reconnaissance. Using a temperature resolution below 0.5 K and an angular resolution of about 1-degree high-quality images show the capability of discriminating between many brightness temperature classes within our natural environment and man-made objects. Measurement examples are given for cloud and fog penetration at 90 GHz, for the detection of vehicles on roads, and for the detection and classification of airports and airplanes. The application of different contour enhancement methods (Marr-Hildreth and Canny) shows the possibility of extracting lines and shapes precisely in order to improve automatic target recognition. The registration of the passive images with corresponding X-band synthetic aperture images from the same area is carried out and the high degree of correlation is discussed.

Suess, Helmut↗

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.

Statistics↗

Distribution and evolution of asteroid rotation rates

Data on the rotational characteristics of more than 300 asteroids are currently available, and it is now clear that the distribution of the rotation rates is nonrandom. A plot of rotation rate against asteroid diameter shows large dispersion but is distinctly V-shaped. The minimum of this curve at about 120 km may separate primordial asteroids from their collision products. There is also evidence that rotation rate depends on type classification, and weak evidence that it may also depend on family membership. Recent bias-free observations suggest that the marked rise of rotation rate with decreasing diameter D for those asteroids with D less than 120 km cannot be completely accounted for by observational-selection effects. A significantly large subset of the small asteroids have exceptionally long rotation periods suggestive of either a different nature and origin or a peculiar history. Models that have been proposed to account for these results are discussed.

Dermott, S. F.↗

Infusing Statistical Thinking into the NASA Quesst Community Test Campaign

Statistical thinking permeates many important decisions as NASA plans its Quesst mission, which will culminate in a series of community overflights using the X-59 aircraft to demonstrate low-noise supersonic flight. Month-long longitudinal surveys will be deployed to assess human perception and annoyance to this new acoustic phenomenon. NASA works with a large contractor team to develop systems and methodologies to estimate noise doses, to test and field socio-acoustic surveys, and to study the relationship between the two quantities, dose and response, through appropriate choices of statistical models. This latter dose-response relationship will serve as an important tool as national and international noise regulators debate whether overland supersonic flights could be permitted once again within permissible noise limits. In this presentation we highlight several areas where statistical thinking has come into play, including issues of sampling, classification and data fusion, and analysis of longitudinal survey data that are subject to rare events and the consequences of measurement error. We note several operational constraints that shape the appeal or feasibility of some decisions on statistical approaches, and we identify several important remaining questions to be addressed.

Bayesian model↗

Classification of the LCVF AVIRIS test site with a Kohonen artificial neural network

We present a classification of an AVIRIS spectral image of the Lunar Crater Volcanic Field (LCVF). Geologic mapping from such data is made possible by distinctive mineral signatures: absorption features and the shape of the spectral continuum. The subtle spectral shape differences between some of the geological units in this scene along with the high dimensionality of the spectral presents a challenging pattern recognition task. We found an artificial neural network powerful in separating 13 geological units based on the full spectral resolution. The LCVF, in northern Nye County, Nevada, was the primary focus of the NASA-sponsored Geologic Remote Sensing Field Experiment in the summer of 1989. It consists of over 100 square miles of Quaternary basaltic pyroclastic and flow deposits. These deposits lie atop ignimbrites and silicic lava flows of Tertiary age and in turn are overlain by Quaternary alluvial and playa deposits. This AVIRIS image was collected on September 29, 1989 at 11:44 at 11:44 PDT. The 256-by-256 pixel subsection in this study contains oxidized basaltic cinder deposits, the southern half of the Lunar Lake playa, and outcrops of the Rhyollite of Big Sand Spring Valley. Vegetation in LCVF is sparse, but locally abundant within washes and near springs.

Merenyi, Erzsebet↗

Fast Neutron Spectroscopy with Organic Scintillation Detectors in a High-Radiation Environment

Organic scintillators with pulse shape discrimination capability are widely used in both research and practical applications of neutron detection. The neutron and gamma-ray identification performance of the detector depends on the classification algorithms, noise filters and pileup rejection criteria in a high-flux bremsstrahlung radiation environment. In this paper, a technique has been developed and implemented for the neutron detection with multiple filter and discrimination steps, which to a high confidence level eliminates counting of gamma-ray pulses. Such a technique is merited when making measurements in a high flux bremsstrahlung and secondary fluorescence environment. The EJ-309 and stilbene detectors coupled to the digital data acquisition system were used for the calibration assessments with standard gamma-ray and neutron sources such as 137Cs, 60Co, 252Cf, and Am-Be. The MCNPX-PoliMi and GEANT4 toolkits were used to simulate the light output and the optical photon transport in the scintillators and create detector response functions for each type of detector. The neutron spectrum unfolding algorithm, GRAVELW, was used to recreate and calibrate with the Am-Be as final step before applying the neutron detection system to extract fusion neutron spectra generated in an intense bremsstrahlung radiation environment. This new technique described offers the user the ability to measure neutron spectra in a high-flux gamma-ray field and tune the parameters to meet required filtering needs.

Neutron Detector, Organic Scintillator, PulseShape↗

Determine precipitation rates from visible and infrared satellite images of clouds by pattern recognition technique

A more advanced cloud pattern analysis algorithm was subsequently developed to take the shape and brightness of the various clouds into account in a manner that is more consistent with the human analyst's perception of GOES cloud imagery. The results of that classification scheme were compared with precipitation probabilities observed from ships of opportunity off the U.S. east coast to derive empirical regressions between cloud types and precipitation probability. The cloud morphology was then quantitatively and objectively used to map precipitation probabilities during two winter months during which severe cold air outbreaks were observed over the northwest Atlantic. Precipitation probabilities associated with various cloud types are summarized. Maps of precipitation probability derived from the cloud morphology analysis program for two months and the precipitation probability derived from thirty years of ship observation were observed.

Weinman, James A.↗

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rai, Man Mohan↗

Hybrid Neural Network and Support Vector Machine Method for Optimization

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rai, Man Mohan↗

Signatures of bulge triaxiality from kinematics in Baade's window

We study a sample of 62 Baade's Window, (l,b) = (1, -4)deg, K giants that have published proper motions, radial velocity, and metallicity. Using R(sub 0) = 8 kpc, we construct the velocity ellipsoids, namely the 3x3 velocity dispersion tensors, for the metal rich stars ((Fe/H) greater than or equal to 0) and metal poor stars ((Fe/H) less than or equal to -0.2). After diagonalizing the tensor, we find a vertex deviation characteristic of a nonaxisymmetric system. Eigenvalues for the two velocity ellipsoids (sigma(sub 1), sigma(sub 2), sigma(sub 3)) are (126, 89, 65) +/- 13 km/s for the metal rich sample and (154, 77, 83) +/- 25 km/s for the metal poor sample with their long axes pointing to two nearly perpendicular directions (l(sub v), b(sub v)) = (-65 +/- 9 deg, +14 +/- 9 deg) and (l(sub v), b(sub v)) = (25 +/- 14 deg, -11 +/- 14 deg), respectively. The vertex deviations of the velocity ellipsoids cannot be consistently explained by any oblate model. We are able to reject the hypothesis that the metal poor and metal rich populations are drawn from the same distribution at better than the 97% confidence level. We populate orbits in a realistic bar potential with a Gaussian velocity distribution, allowing us to simulate and interpret observations. We conclude that the data are consistent with a triaxial bulge pointing towards (l,b) with l less than 0 deg and b = 0 deg as suggested by earlier work on gas dynamics and the observed light distribution. We also predict that low latitude (absolute value of b less than or equal to 4 deg) bulge fields should show the vertex deviation more strongly and would therefore be the best locations for future proper motion studies. In the classification scheme of Athanassoula et al. (1983) the metal rich stars appear to occupy the B-family orbits which rotate in the prograde sense in the rest frame and have boxy shapes that are aligned with and supporting the bar. The metal poor stars in the sample lag behind the metal rich bulge and appear to occupy R-family orbits which rotate in the retrograde sense in the rest frame. They have nearly round loop shapes and are aligned perpendicularly to the bar, hence limit the triaxiality of the bar potential. The correlations between the metallicity and the orbit families can develop if the bulge forms dissipatively on a sufficiently long time scale. However, it is difficult to explain such correlations if most stars in the inner Galaxy form during the violent relaxation phase.

Zhao, Hongsheng↗

JANNAF 17th Propulsion Systems Hazards Subcommittee Meeting

Volume 1, the first of two volumes is a compilation of 16 unclassified/unlimited technical papers presented at the 17th meeting of the Joint Army-Navy-NASA-Air Force (JANNAF) Propulsion Systems Hazards Subcommittee (PSHS) held jointly with the 35th Combustion Subcommittee (CS) and Airbreathing Propulsion Subcommittee (APS). The meeting was held on 7 - 11 December 1998 at Raytheon Systems Company and the Marriott Hotel, Tucson, AZ. Topics covered include projectile and shaped charge jet impact vulnerability of munitions; thermal decomposition and cookoff behavior of energetic materials; damage and hot spot initiation mechanisms with energetic materials; detonation phenomena of solid energetic materials; and hazard classification, insensitive munitions, and propulsion systems safety.

Cocchiaro, James E.↗

Automated Fiber Placement Defects: Automated Inspection and Characterization

Automated Fiber Placement (AFP) is an additive composite manufacturing technique, and a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time consuming, which led to the motivation to develop a rapid automatic method of inspection. This paper suggests a new automated inspection system based on convolutional neural networks and image segmentation tasks. This creates a pixel by pixel classification of the defects of the whole part scan. This process will allow for greater defect information extraction and faster processing times over previous systems, motivating rapid part inspection and analysis. Fine shape, height, and boundary detail can be generated through our system as opposed to a more coarse resolution demonstrated in other techniques. These scans are analyzed for defects, and then each defect is stored for export, or correlated to machine parameters or part design. The network is further improved through novel optimization techniques. New training instances can also be created with every new part scan by including the machine operator as a post inspection check on the accuracy of the system. Having a continuously adapting inspection system will increase accuracy for automated inspections, cutting down on false readings.

Sacco, Christopher↗

Characteristic variations in reflectance of surface soils

Surface soil samples from a wide range of naturally occurring soils were obtained for the purpose of studying the characteristic variations in soil reflectance as these variations relate to other soil properties and soil classification. A total 485 soil samples from the U.S. and Brazil representing 30 suborders of the 10 orders of 'Soil Taxonomy' was examined. The spectral bidirectional reflectance factor was measured on uniformly moist soils over the 0.52 to 2.32 micron wavelength range with a spectroradiometer adapted for indoor use. Five distinct soil spectral reflectance curve forms were identified according to curve shape, the presence or absence of absorption bands, and the predominance of soil organic matter and iron oxide composition. These curve forms were further characterized according to generically homogeneous soil properties in a manner similar to the subdivisions at the suborder level of 'Soil Taxonomy'. Results indicate that spectroradiometric measurements of soil spectral bidirectional reflectance factor can be used to characterize soil reflectance in terms that are meaningful to soil classification, genesis, and survey.

Stoner, E. R.↗

Lunar mare domes - Classification and modes of origin

In this paper, lunar mare domes (LMDs) are classified according to morphologic and morphometric (primarily diameter) characteristics, and consideration is given to their origin and role in lunar surface processes. In general, they occur either as low, flat, circular structures with convex shapes, slopes less than about 5 deg, and display summit craters, or as irregular structures often adjacent to highland regions and rarely containing summit craters. It is found that LMDs originate through extrusion of lavas through vents to produce low lava shields, or through flooding and draping of preexisting topography to produce kipukas and irregular domes. Smooth, vent-related mare domes range from about 3-17 km in diameter and up to several hundred meters in elevation; they are similar in morphology to small terrestrial lava shields.

Head, J. W.↗

A concentrated outbreak of tornadoes, downbursts and microbursts, and implications regarding vortex classification

A remarkable case of severe weather occurred near Springfield, Illinois on 6 August 1977. Aerial and ground surveys revealed that 17 cyclonic vortices, an anticyclonic vortex, 10 downbursts and 19 microbursts occurred in a limited (20 km x 40 km) area, associated with a bow-shaped radar echo. About half of the vortices appeared to have occurred along a gust front. Some of the others appear to have occurred within the circulation of a mesocyclone accompanying the bow echo, but these vortices seem to have developed specifically in response to localized boundary-layer vorticity generation associated with horizontal and vertical wind shears on the periphery of microbursts. Some of these vortices, and other destructive vortices in the literature, do not qualify as tornadoes as defined in the Glossary of Meteorology. A more pragmatic definition of a tornado is suggested.

Forbes, G. S.↗

A new method for recognizing quadric surfaces from range data and its application to telerobotics and automation

The problem of recognizing and positioning of objects in three-dimensional space is important for robotics and navigation applications. In recent years, digital range data, also referred to as range images or depth maps, have been available for the analysis of three-dimensional objects owing to the development of several active range finding techniques. The distinct advantage of range images is the explicitness of the surface information available. Many industrial and navigational robotics tasks will be more easily accomplished if such explicit information can be efficiently interpreted. In this research, a new technique based on analytic geometry for the recognition and description of three-dimensional quadric surfaces from range images is presented. Beginning with the explicit representation of quadrics, a set of ten coefficients are determined for various three-dimensional surfaces. For each quadric surface, a unique set of two-dimensional curves which serve as a feature set is obtained from the various angles at which the object is intersected with a plane. Based on a discriminant method, each of the curves is classified as a parabola, circle, ellipse, hyperbola, or a line. Each quadric surface is shown to be uniquely characterized by a set of these two-dimensional curves, thus allowing discrimination from the others. Before the recognition process can be implemented, the range data have to undergo a set of pre-processing operations, thereby making it more presentable to classification algorithms. One such pre-processing step is to study the effect of median filtering on raw range images. Utilizing a variety of surface curvature techniques, reliable sets of image data that approximate the shape of a quadric surface are determined. Since the initial orientation of the surfaces is unknown, a new technique is developed wherein all the rotation parameters are determined and subsequently eliminated. This approach enables us to position the quadric surfaces in a desired coordinate system. Experiments were conducted on raw range images of spheres, cylinders, and cones. Experiments were also performed on simulated data for surfaces such as hyperboloids of one and two sheets, elliptical and hyperbolic paraboloids, elliptical and hyperbolic cylinders, ellipsoids and the quadric cones. Both the real and simulated data yielded excellent results. Our approach is found to be more accurate and computationally inexpensive as compared to traditional approaches, such as the three-dimensional discriminant approach which involves evaluation of the rank of a matrix. Finally, we have proposed one other new approach, which involves the formulation of a mapping between the explicit and implicit forms of representing quadric surfaces. This approach, when fully realized, will yield a three-dimensional discriminant, which will recognize quadric surfaces based upon their component surfaces patches. This approach is faster than prior approaches and at the same time is invariant to pose and orientation of the surfaces in three-dimensional space.

Alvertos, Nicolas↗

The Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Cesana, Gregory V.↗