Micrometeoroid Impact Research Articles

Numerical Analysis of Debris Cloud Formation in UHMWPE Wavy Plates during Hypervelocity Impact

This paper presents and analyses the numerical results of hypervelocity impact against ultra-high molecular weight polyethylene (UHMWPE) plates with four different surface wave profiles. UHMWPE wavy plates (WP) are intended to be used in Whipple Shield bumper plate, which is of paramount importance for space vehicles against micro-meteorite and orbital debris (MMOD) impact protection. Numerical work was carried out as a hybrid combination of smoothed particle hydrodynamics (SPH) and finite element modelling (FEM). Circular plates were subjected to hypervelocity impact of a spherical aluminium projectile travelling at 3000 m/s. The outcomes of the simulations were analysed in terms of debris cloud generation, projectile fragmentation, and impact energy dissipation performance of wavy plates, and compared with a conventional flat counterpart. Results of this study indicate that surface wave profile has a clear positive influence in terms of hypervelocity impact protection performance.

Shock melt in the Cold Bokkeveld CM2 carbonaceous chondrite and the response of C‐complex asteroids to hypervelocity impacts

AbstractMany of the CM carbonaceous chondrites are regolith breccias and so should have abundant evidence for collisional processing. The constituent clasts of these fragmental rocks frequently display compactional petrofabrics; yet, olivine microstructures show that most CMs are unshocked. To better understand the reasons for this contradiction, we have sought other evidence for hypervelocity impact processing of CM chondrites using the Cold Bokkeveld meteorite. We find that this regolith breccia contains rare particles of vesicular shock melt that are close in chemical composition to bulk CM chondrite. Transmission electron microscopy of a melt bead shows that it is composed of silicate glass with inclusions of pentlandite, pyrrhotite, and wüstite. Characterization of shards of another bead by atom probe tomography reveals nanoscale clusters of sulfur that represent sulfide inclusions arrested at an early stage of growth. These glass particles are mineralogically comparable to micrometeoroid impact melt described from the Cb‐type asteroid Ryugu and melt that has been experimentally produced by pulsed laser irradiation of CM targets. The glass could have formed by in situ shock‐melting, but petrographic evidence is more consistent with an origin as ballistic ejecta from a distal impact. The scarcity of melt in this meteorite, and CM chondrites more broadly, is consistent with the explosive fragmentation of hydrous asteroids following energetic collisions. Cold Bokkeveld's parent body is likely to be a second‐generation asteroid that was constructed from the debris of one or more earlier bodies, and only a small proportion of the reaccreted material had been highly shocked and melted.

Lunar soil record of atmosphere loss over eons.

The Moon has a tenuous atmosphere produced by space weathering. The short-lived nature of the atoms surrounding the Moon necessitates continuous replenishment from lunar regolith through mechanisms such as micrometeorite impacts, ion sputtering, and photon-stimulated desorption. Despite advances, previous remote sensing and space mission data have not conclusively disentangled the contributions of these processes. Using high-precision potassium (K) and rubidium (Rb) isotopic analyses of lunar soils from the Apollo missions, our study sheds light on the lunar surface-atmosphere evolution over billions of years. The observed correlation between K and Rb isotopic ratios (δ 87Rb = 0.17 δ 41K) indicates that, over long timescales, micrometeorite impact vaporization is the primary source of atoms in the lunar atmosphere.

Sensor Fault Detection in Smart Extraterrestrial Habitats Using Unsupervised Learning

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature–pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed.

Durable and impact-resistant thermoset polymers for the extreme environment of low Earth orbit

Polymers degrade rapidly in low-Earth orbit (LEO) due to extreme thermal cycling, solar radiation, micrometeoroid and orbital micro-debris impact, and the synergistic erosive effects of atomic oxygen (AO) with UV radiation. In this work, we investigate the synergistic effect of (AO+UV)-induced erosion and high-velocity impact damage on a tough thermoset, polydicyclopentadiene (pDCPD), via erosion yield measurements after a long-term LEO exposure in the ram orientation aboard the International Space Station (ISS) followed by ground-based hypervelocity impact tests. The incorporation of SiO2 nanoparticles reduces the AO-erosion rate by an order of magnitude, while also mitigating the associated degradation of surface mechanical properties. Moreover, the resistance of pDCPD to AO-erosion increases with crosslink density. Hypervelocity impact tests using sub-millimeter laser-driven flyer plates launched at 4.5 km s−1 reveal that pDCPD has higher impact resistance than epoxies with comparable erosion resistance. Interestingly, both epoxy and pDCPD exhibit a statistically insignificant change in impact resistance following surface AO-erosion during direct exposure to space vehicle ram conditions (7.8 km s−1) in LEO.

The application of machine learning in micrometeoroid and orbital debris impact protection and risk assessment for spacecraft

Current spacecraft micrometeoroid and orbital debris impact risk assessments utilize semi-empirical equations to describe the protection afforded by a spacecraft component (e.g., pressure hull, critical component, etc.). These equations demand fundamentally limiting assumptions, for example of projectile shape and material, to reduce the complexity of the mechanics and material response under such extreme conditions. Machine learning (ML) approaches, however, are well suited to such high dimensionality problems and have previously been shown to provide comparable classification accuracy to state-of-the-art empirical techniques in this domain. We demonstrate that ML models can readily incorporate additional complexity beyond that currently achievable with semi-empirical models, such as the effect of thermal insulation blankets, non-aluminium projectiles, and non-spherical projectiles on failure thresholds without any notable loss of performance, compared with baseline conditions. For future micrometeoroid and orbital debris (MMOD) risk assessment codes such ML models offer the potential to incorporate the true characteristics of the MMOD environment more accurately than existing approaches.

Evaluating UHMWPE-stuffed aluminium foam sandwich panels for protecting spacecraft against micrometeoroid and orbital debris impact

Robotic spacecraft shielding for protection against micrometeoroid impact is typically provided by existing structural panels, thermal insulation blankets, or reinforced component containers. Here the performance of an open cell aluminium foam core sandwich panel with intermediate ultra-high molecular weight polyethylene (UHMWPE) stuffing under hypervelocity impact of spherical aluminium projectiles was evaluated. Providing comparable structural and thermal performance as honeycomb sandwich panels, foam core sandwich panels have demonstrated improved impact shielding behaviour in existing studies. The inclusion of UHMWPE stuffing is found, through experimental testing and a comprehensive numerical study, to increase the shielding capability of foam core sandwich panels (40–80% increase in critical projectile diameter) for a modest weight penalty (25%). Compared to conventional aluminium honeycomb sandwich panels with equivalent weight, the improvement in shielding performance is >100%.

Design, fabrication, and hypervelocity impact testing of screen-printed flexible micrometeoroid and orbital debris impact sensors for long-duration spacecraft health monitoring

Micrometeoroid and orbital debris (MMOD) are a key risk for spacecraft damage that could compromise missions. Detecting and evaluating MMOD damage is therefore a crucial component in the health monitoring of spacecraft, especially for long duration, deep space expeditions. In this work, we developed a passive sensor system fabricated from conductive metal ink screen-printed on flexible Kapton, using roll-to-roll manufacturing suitable for low-cost fabrication of large areas of sensors, as a MMOD sensor for a spacecraft shield. The sensor is integrated into a low density, two-wall Whipple shield comprising of thin aluminum sheets sandwiching a polyimide foam. The shield with the sensors were tested with hypervelocity impacts at approximately 7 km/s using different particle diameters. Data collected from the sensors were successfully used to determine the impact size, impact location, and predict the impact energy of the damage.

Design of an Explosive Micro-particle Accelerator to Simulate Micrometeoroid Impacts in Space

Design of an Explosive Micro-particle Accelerator to Simulate Micrometeoroid Impacts in Space

COPING WITH SPACE ENVIRONMENT: TESTING SOLID PROPELLANTS FOR IN-ORBIT USE

The results of a risk reduction study are presented, proving the suitability of solid rocket motors (SRMs) to de-orbit satellites at the end of their lifetime. In a risk reduction program, three basic unexplored questions regarding this technological approach were investigated: (1) what are the effects of the size and type of particles ejected during the operation of SRMs; (2) what is the effect of the micrometeoroid impact on the solid propellant; and (3) what are the effects of prolonged exposure to space radiation during the 20-year expected satellite lifetime of satellites? Using a series of sophisticated and newly developed research approaches, it was shown that solid rocket propellants can withstand harsh space conditions over an extensive period of time. All of the tests were performed using a well-established, metal-free high-performance ammonium perchlorate/hydroxyl-terminated polybutadiene propellant fully qualified by military standards.

Heat transfer in granular media with weakly interacting particles

We study the heat transfer in weakly interacting particle systems in vacuum. The particles have surface roughness with self-affine fractal properties, as expected for mineral particles produced by fracture, e.g., by crunching brittle materials in a mortar, or from thermal fatigue or the impact of micrometeorites on asteroids. We show that the propagating electromagnetic (EM) waves give the dominant heat transfer for large particles, while for small particles both the evanescent EM-waves and the phononic contribution from the area of real contact are important. As an application, we discuss the heat transfer in rubble pile asteroids.

The Influence of Surface Binding Energy on Sputtering in Models of the Sodium Exosphere of Mercury

We have simulated the sodium (Na) exosphere of Mercury to show how the exosphere is affected by the assumed surface binding energy (SBE) of Na in the sputtered component. We constrained ion precipitation onto the surface using distributions for the cusp regions that are consistent with measurements by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging Fast Imaging Plasma Spectrometer instrument. We have simulated sputtering with SBEs of 0.27, 2.6, 4.4, and 7.9 eV, with the lowest value commonly used in exosphere models and the highest from recent molecular dynamics calculations for the Na-bearing feldspar end-member, albite. A gradual change in the exosphere is seen as the yield decreases and the ejecta energy increases with increasing SBE. We describe the corresponding exosphere source functions for ion sputtering (IS), as well as for the previously studied processes of micrometeoroid impact vaporization and photon-stimulated desorption (PSD), along with their release energy distributions and spatial distributions. We have summed the contributions of the various source processes to explain how and when the different sources can be distinguished by observations. The modeled exosphere scale heights range from 72 km for PSD to over 1000 km for IS using a SBE of 7.9 eV. We find that the processes responsible for generating Mercury's Na exosphere are separable by measuring line-of-sight column densities tangent to the planet at various altitudes and positions around the planet. Our initial results are consistent with the Na being sputtered from a high-SBE material such as feldspar, which has been predicted to be abundant on the Mercury's surface.

Molecular Dynamics Simulations of Water Formation and Retention by Micrometeoroid Impact on Lunar Surface

AbstractReactive molecular dynamics simulations are carried out to study water formation and retention during impacts by nanometer sized micrometeoroids on lunar surface at the atomic‐scale. Results show that water molecules are generated and lost simultaneously during an impact. For a hydroxylated surface under average solar wind condition, the water molecules produced by a nanometer sized cosmic dust with an impact velocity of 8 km/s to 20 km/s ranges from about 44% to 275% of that existed before impact. However, the increase in water content at the impact site is only from 5% to 73% due to ejections caused by impact. While micrometeoroid impact may generate a substantial amount of new water molecules, the amount of water lost to space also increases significantly at higher impact velocities. Hence, the increase in local lunar water content is strongly affected by the impact velocity.

ARTEMIS Observations of Lunar Nightside Surface Potentials in the Magnetotail Lobes: Evidence for Micrometeoroid Impact Charging

AbstractThe Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moon's Interaction with the Sun mission observes anomalously low lunar nightside surface potentials while in the terrestrial magnetotail lobes. Observed potential magnitudes are between 15% and 40% that expected from ambient tail‐plasma charging, are highly concentrated on the lunar dawn hemisphere, and are correlated with low ambient plasma densities. These characteristics suggest an additional, highly asymmetric source of cold current to the lunar surface. Given these characteristics, we identify micrometeoroid impact‐generated plasmas as the likely source of this additional current. Using laboratory measurements of impact charge yields and models of the micrometeoroid flux to the Moon constrained by in situ measurements, we show that currents due to micrometeoroid impact plasmas have the necessary magnitude and spatial distribution to explain the observed surface potential measurements. Micrometeoroid impact‐generated currents may contribute to surface charging at airless bodies with intense micrometeoroid bombardment and/or low ambient plasma densities.

Micrometeoroid and Orbital Debris Impact Detection and Location Based on FBG Sensor Network Using Combined Artificial Neural Network and Mahalanobis Distance Method

Micrometeoroid and orbital debris (MMOD) can cause serious impact damage to long-term on-orbit spacecraft. MMOD impact detection and location are essential to improve the safety and reliability of the on-orbit spacecraft. In this article, a combined artificial neural network (ANN) and Mahalanobis distance method for MMOD impact detection and location based on fiber Bragg grating (FBG) sensor network is proposed. The inputs of the proposed ANN are the Mahalanobis distances determined by the wavelength changes of the FBG sensors at impact points and reference points. A finite element model is developed to explore the relationship between the Mahalanobis distance and the actual distance, which proved to be nonlinear based on the finite element simulation results of MMOD impacts. Four FBG sensors were installed on the back of the test board and sampled at a frequency of 100 Hz using a four-channel FBG demodulator. A total of 360 impact experiments were carried out on this experiment system. The average location error of the proposed method is 0.89 cm, which is 39% and 31% lower compared with the Mahalanobis distance discriminant analysis method and the ANN method, respectively. The combined ANN and Mahalanobis distance method reduces the location errors and has higher accuracy of position prediction.

Impact force identification in structures using time-domain spectral finite elements

During the launch and subsequent life of a spacecraft, there are various transient loads due to stage separation and micro-meteorite impact which a spacecraft structure must be designed to withstand. However, the force histories for these transients at these locations are not available to design the structure due to practical considerations like mounting of instrumentation. To address this problem, a new algorithm for performing impact force identification based on time-domain responses measured at various locations is proposed. The time-domain spectral finite element model is adopted as it requires a fewer number of measured responses which clearly has an advantage with respect to conventional finite element method. Hence, we use time-domain spectral finite element method, to generate the mass, stiffness, and damping matrices, which are required to perform force identification. Experiments are performed to obtain the time-domain responses on the beam and portal frame structures on which force identification is performed. The efficiency of the new algorithm is demonstrated using a variety of responses and different one-dimensional structures. However, the proposed algorithm is general in nature and can be used for one-, two-, and three-dimensional structures and with conventional generalized finite element model. The results from the proposed algorithm show an excellent match between the reconstructed force histories and the experimentally measured force. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature.

Multifunctional inkjet printed sensors for MMOD impact detection

The sensitivity of the electronic properties of carbon nanotubes to gases, chemicals, temperature, and mechanical strain enables their use as fillers in nanocomposites for sensing applications. In this paper, the authors develop a low-cost and scalable process based on inkjet printing technology to fabricate printed flexible sensors used for strain and damage detection. A well-dispersed conductive water-based ink is fabricated with functionalized multiwall carbon nanotubes (MWCNT) and deposited onto paper and Kapton substrates to obtain a sheet resistance as low as 500 Ω sq−1 with about 30 printed layers. The number of printed layers, the direction of the electrical resistance measurement, and the type of substrate have clear effects on the sensor’s electrical performances related to the detection of mechanical strain and impact damage. This work demonstrates the effectiveness of the printed sensors for micrometeoroid and orbital debris (MMOD) impact damage detection through hypervelocity testing.

Does gist drive NASA experts’ design decisions?

AbstractAs engineers retire from practice, they must transfer their expertise to new recruits. Typically, this is accomplished using decision‐support systems that communicate precise probabilities. However, Fuzzy‐Trace Theory (FTT) predicts that the most experts prefer to rely on “gist” representations of risk over “verbatim” representations. We conducted a survey of 41 NASA employees (whose mathematical abilities are a prerequisite for their jobs) and 233 nonexperts. We tested whether experts designing space missions under the micrometeoroid and orbital debris (MMOD) impact – rely more on qualitative or quantitative risk representations. We tested three hypotheses: gist and verbatim representations of MMOD risk are distinct for both experts and nonexperts; gist representations are more predictive of decisions than are verbatim representations; and providing nonexperts with a bottom‐line meaning change their gists more than verbatim information does. Results support FTT's predictions: gist and verbatim representations were distinct, and gist representations were associated with decisions for both experts and nonexperts. We did not observe an association between quantitative risk estimates and decisions for either experts or nonexperts. We observed that exposing a nonexpert to an expert's gist modified that nonexpert's gist yet exposing quantitative risk information did not. Implications for expertise transfer are discussed.

Deep neural networks for analysis of Mercury’s planetary exosphere

The emergence of Artificial Intelligence’s Deep Neural Networks (DNNs) as a method for analysis of spatial and temporal data gives us a new avenue for research of the processes inherent to planetary exospheres and their interaction with the planetary environment. Hereby, it will be presented a particular study on how in-situ observations of the elemental composition of Mercury’s exosphere may serve as an indication of the surface regolith mineral composition below, through predictions given by pre-trained DNNs. In this case the main driver, considered to generate the exosphere, is the micrometeoroid impact vaporization (MIV), which is seen as the dominant process in the night-side hemisphere of the planet. The training datasets will be constructed from randomly generated virtual surfaces, which will aim to generalize the training of the AI models to various types and compositions of the regolith. Different Neural Network models, which include fully-connected networks and Convolutional Neural Networks, will be compared both in giving supervised classification through multivariate regression predictions, and in reconstructing the regolith mineralogy maps. Furthermore, ways to expand the Deep Neural Networks to pattern recognition and knowledge discovery will be explored beyond the surface-exosphere interaction, as well as the possibilities for transfer learning and online learning with the acquisition of real planetary data. The development of such data analysis algorithms will be shown especially in view of the upcoming arrival of the ESA/JAXA’s BepiColombo mission to Mercury, the innermost planet, in 2025, with its variety of instruments able to capture the dynamics of the tenuous Hermean environment. Ultimately, would be targeted the implementation of such AI algorithms within the Ground Segment pipeline software architecture of the experiment BepiColombo/MPO/SERENA, composed by four ion and neutral particle detectors.

Non-catastrophic perforation of a composite overwrapped pressure vessel

Most spacecraft have a pressurized tank on board, such as a liquid propellant tank or a breathable air supply tank. Because of the serious damage that might result following an on-orbit micro-meteoroid or orbital debris (MMOD) particle impact, a primary design consideration of such (often highly) pressurized tanks is the mitigation of the damage that might occur in the event of such an impact. While catastrophic failure (i.e. rupture) following a high-speed MMOD particle impact would be disastrous, a puncture and the resulting thrust caused by the expulsion of fluids or gas from the perforated tank could result in the de-stabilization of the spacecraft's orbit which could, in turn, also lead to loss of the spacecraft. As such, in the event that catastrophic failure does not occur, risk assessments must still consider whether or not a perforation of a pressurized tank might occur and, in the event of a perforation, what size hole is created by the impact. This paper presents the development of a ballistic limit equation for highly pressurized COPVs that would differentiate between combinations of impact parameters and operating conditions that would result in a hole (without rupture) in the front side of the impacted pressure vessel from those that would not. Empirical models for the hole-out area in the composite overwrap and in the interior liner material in the event of a front side perforation are also developed and discussed.