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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Hydrocode predictions of collisional outcomes: Effects of target size

Traditionally, laboratory impact experiments, designed to simulate asteroid collisions, attempted to establish a predictive capability for collisional outcomes given a particular set of initial conditions. Unfortunately, laboratory experiments are restricted to using targets considerably smaller than the modelled objects. It is therefore necessary to develop some methodology for extrapolating the extensive experimental results to the size regime of interest. Results are reported obtained through the use of two dimensional hydrocode based on 2-D SALE and modified to include strength effects and the fragmentation equations. The hydrocode was tested by comparing its predictions for post-impact fragment size distributions to those observed in laboratory impact experiments.

Ryan, Eileen V.↗
NASA NTRS1993

Impact penetration experiments in teflon targets of variable thickness

Approximately 20.4 sq m of Teflon thermal blankets on the nonspinning Long Duration Exposure Facility (LDEF) were exposed to the orbital debris and micrometeoroid environment in low-Earth orbit (LEO) for approximately 5.7 years. Each blanket consisted of an outer layer (approximately 125 micron thick) of FEP Teflon that was backed by a vapor-deposited metal mirror (Inconel; less than 1 micron thick). The inner surface consisted of organic binders and Chemglaze thermal protective paint (approximately 50 micron thick) resulting in a somewhat variable, total blanket thickness of approximately 180 to 200 microns. There was at least one of these blankets, each exposing approximately 1.2 sq m of surface area, on nine of LDEF's 12 principal pointing directions, the exceptions being Rows 3, 9, and 12. As a consequence, these blankets represent a significant opportunity for micrometeoroid and debris studies, in general, and specifically they provide an opportunity to address those issues that require information about pointing direction (i.e., spatial density of impact events as a function of instrument orientation). During deintegration of the LDEF spacecraft at KSC, all penetration holes greater than or equal to 300 micron in diameter were documented and were recently synthesized in terms of spatial density as a function of LDEF viewing direction by. The present report describes ongoing cratering and penetration experiments in pure Teflon targets, which are intended to establish the relationships between crater or penetration-hole diameters and the associated projectile dimensions at laboratory velocities (i.e., 6 km/s). The ultimate objective of these efforts is to extract reliable mass-frequencies and associated fluxes of hypervelocity particles in LEO.

Hoerz, F.↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS1993

Impact-induced tensional failure in rock

Planar impact experiments were employed to induce dynamic tensile failure in Bedford limestone. Rock disks were impacted with aluminum and polymethyl methacralate flyer plates at velocities of 10 to 25 m/s. This resulted in tensile stresses in the range of 11 to 160 MPa. Tensile stress durations of 0.5 and 1.3 microsec induced microcrack growth which in many experiments were insufficient to cause complete spalling of the samples. Velocity reduction, and by inference microcrack production, occurred in samples subjected to stresses above 35 MPa in the 1.3-microsec PMMA experiments and 60 MPa in the 0.5-microsec aluminum experiments. Apparent fracture toughnesses of 2.4 and 2.5 MPa m exp 1/2 are computed for the 1.3- and 0.5-microsec experiments. Three-dimensional impact experiments were conducted on 20 cm-sized blocks of Bedford limestone and San Marcos gabbro. Compressional wave velocity deficits up to 50-60 percent were observed in the vicinity of the crater. The damage decreases as about r exp -1.5 from the crater, indicating a dependence on the magnitude and duration of the tensile pulse.

Ahrens, Thomas J.↗
NASA NTRS1991

Space debris: Orbital microparticulates impacting LDEF experiments favour a natural extraterrestrial origin

The results of work carried out at the Unit for Space Sciences at the University of Kent at Canterbury, United Kingdom, on the micrometeoroid and space debris environment of near Earth space are described. The primary data for the research program is supplied by an examination of several types of exposed surface from the NASA Long Duration Exposure Facility (LDEF), including an experiment dedicated to the detection of micrometeoroids and space debris provided by the University.

Mcdonnell, Tony↗
NASA NTRS1993

Derivation of particulate directional information from analysis of elliptical impact craters on LDEF

The Long Duration Exposure Facility provided a gravity gradient stabilized platform which allowed limited directional information to be derived from particle impact experiments. The morphology of impact craters on semi-infinite materials contains information which may be used to determine the direction of impact much more accurately. We demonstrate the applicability of this technique and present preliminary results of measurements from LDEF and modelling of interplanetary dust and space debris.

Newman, P. J.↗
NASA NTRS1995

Experimental investigation of the relationship between impact crater morphology and impacting particle velocity and direction

Interpretation of the wealth of impact data available from the Long Duration Exposure Facility, in terms of the absolute and relative populations of space debris and natural micrometeoroids, requires three dimensional models of the distribution of impact directions, velocities and masses of such particles, as well as understanding of the impact processes. Although the stabilized orbit of LDEF provides limited directional information, it is possible to determine more accurate impact directions from detailed crater morphology. The applicability of this technique has already been demonstrated but the relationship between crater shape and impactor direction and velocity has not been derived in detail. We present the results of impact experiments and simulations: (1) impacts at micron dimensions using the Unit's 2MV Van de Graaff accelerator; (2) impacts at mm dimensions using a Light Gas Gun; and (3) computer simulations using AUTODYN-3D from which an empirical relationship between crater shape and impactor velocity, direction and particle properties we aim to derive. Such a relationship can be applied to any surface exposed to space debris or micrometeoroid particles for which a detailed pointing history is available.

Mackay, N. G.↗
DOE OSTI2026

Effect of macroscopic surface defects on dynamic damage: An experimental and numerical study

This study examines the impact of macroscopic surface defects on the dynamic ductile damage behavior of polycrystalline metals using plate-impact experiments. Defects of various shapes (flat, round, and point) were manufactured on the free or impact surfaces of annealed copper specimens. The experiments were diagnosed with photon Doppler velocimetry measurements and soft recovery techniques. The experimental results revealed that defect shape and location significantly affect velocity–time profiles and void distribution. In order to understand the dynamics of shock propagation and corresponding ductile damage evolution within the target specimens, the impact experiments were modeled with a calibrated Tepla model, a dynamic ductile damage model for polycrystalline metals [Nguyen et al., Int. J. Solids Struct. 329, 113833 (2026)]. Overall, our resulting simulated velocity showed good agreement with measured velocity, and our simulated porosity distributions qualitatively matched experimental data. Based on our simulation results, defects on the free surface were found to distort rarefaction waves and therefore the corresponding spall planes. On the other hand, defects on the impact surface generated a delayed shock when the flyer plate and the defected target area were in contact, leading to distortion of the spall plane. The distortion of the spall plane resulted in a non-uniform distribution of voids within the defected specimens. Lastly, we examined the role of local work hardening due to the defect manufacturing process on the velocity and porosity distribution, using Tepla simulations with a simplified representation of local hardening. This investigation highlights the importance of defect geometry, location, and local hardening associated with defect generation in dynamic ductile damage processes.

36 MATERIALS SCIENCE↗
NASA NTRS2001

Impact of Quikscat Data on Numerical Weather Prediction

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse ---mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific. The SeaWinds scatterometer on the QuikScat satellite was launched in July 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). The NASA Data Assimilation Office (DAO) was the first data assimilation center to assimilate QuikScat SeaWinds data and evaluate their impact on numerical weather prediction. Several data impact experiments have been performed, using systems from both the DAO (GEOS-3) and from NCEP (GDAS). In general, these experiments have shown a modest impact of SeaWinds data on numerical weather prediction, the magnitude of which appears to be comparable to the magnitude of the impact of AMI scatterometer data from the ERS satellites. Some of the main results from these experiments will be presented at the meeting.

Atlas, Robert↗
NASA NTRS1982

Experimental simulation of impact cratering on icy satellites

Cratering processes on icy satellites were simulated in a series of 102 laboratory impact experiments involving a wide range of target materials. For impacts into homogeneous clay slurries with impact energies ranging from five million to ten billion ergs, target yield strengths ranged from 100 to 38 Pa, and apparent viscosities ranged from 8 to 200 Pa s. Bowl-shaped craters, flat-floored craters, central peak craters with high or little relief, and craters with no relief were observed. Crater diameters increased steadily as energies were raised. A similar sequence was seen for experiment in which impact energy was held constant but target viscosity and strength progressively decreases. The experiments suggest that the physical properties of the target media relative to the gravitationally induced stresses determined the final crater morphology. Crater palimpsests could form by prompt collapse of large central peak craters formed in low target strength materials. Ages estimated from crater size-frequency distributions that include these large craters may give values that are too high.

Greeley, R.↗
DOE OSTI2026

Efficacy of Gadolinium Gallium Garnet (GGG) as a High-Impedance Optical Window for Shock Wave Experiments

We conducted a series of plate impact experiments to examine the efficacy of < 111>-oriented gadolinium gallium garnet (GGG) single crystals as high-impedance optical window for Photonic Doppler velocimetry (PDV) under shock and double-shock loading. At ~ 123 GPa, shocked GGG remains fully transparent to 1550 nm light for at least 250 ns without any signal degradation. Above 135 GPa, PDV data measured through GGG exhibit a gradual loss of fringe contrast following shock entrance, which eventually leads to transparency loss. The duration for which shocked GGG remains transparent decreases with increasing pressure, and at ~ 148 GPa, it becomes opaque within ~ 20–30 ns. This limits the use of GGG as an interferometry window between ~ 110–140 GPa under single shock loading. Within this pressure range, the refractive index of GGG increases linearly with density: n = 1.552 + 0.054ρ. In contrast to single shock loading, where GGG becomes opaque rapidly above ~ 140 GPa, double-shocked GGG remains optically transparent for over 100 ns when it is first shocked to ~ 123 GPa and then reshocked to significantly higher pressures (215–233 GPa). Our findings raise the exciting possibility of GGG being used as a high-impedance optical window in multi-shock and shock-ramp loading experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2017

Experimental Hypervelocity Dust Impact in Olivine: FIB/TEM Characterization of Micron-Scale Craters with Comparison to Natural and Laser-Simulated Small-Scale Impact Effects

The space weathering of regoliths on airless bodies and the formation of their exospheres is driven to a large extent by hypervelocity impacts from the high relative flux of micron to sub-micron meteoroids that comprise approximately 90 percent of the solar system meteoroid population. Laboratory hypervelocity impact experiments are crucial for quantifying how these small impact events drive space weathering through target shock, melting and vaporization. Simulating these small scale impacts experimentally is challenging because the natural impactors are both very small and many have velocities above the approximately 8 kilometers-per-second limit attainable by conventional chemical/light gas accelerator technology. Electrostatic "dust" accelerators, such as the one recently developed at the Colorado Center for Lunar Dust and Atmospheric Studies (CCLDAS), allow the experimental velocity regime to be extended up to tens of kilometers-per-second. Even at these velocities the region of latent target damage created by each impact, in the form of microcraters or pits, is still only about 0.1 to 10 micrometers in size. Both field-emission analytical scanning electron microscopy (FE-SEM) and advanced field-emission scanning transmission electron microscopy (FE-STEM) are uniquely suited for characterizing the individual dust impact sites in these experiments. In this study, we have used both techniques, along with focused ion beam (FIB) sample preparation, to characterize the micrometer to nanometer scale effects created by accelerated dust impacts into olivine single crystals. To our knowledge this work presents the first TEM-scale characterization of dust impacts into a key solar system silicate mineral using the CCLDAS facility. Our overarching goal for this work is to establish a basis to compare with our previous results on natural dust-impacted lunar olivine and laser-irradiated olivine.

Christoffersen, R.↗