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Deformable Crumpled Nano-ball Coatings with Adaptable Adhesion and Mechanical Energy Absorption for Lunar Dust Mitigation
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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Regolith Adherence Characterization, or RAC, is one of 10 science and technology instruments flying on NASA’s next Commercial Lunar Payload Services (CLPS) flight as part of the Blue Ghost Misison-1. Developed by Aegis Aerospace of Webster, Texas, RAC is designed to study how lunar dust reacts to more than a dozen different types of material samples, located on the payload’s wheels. Photo courtesy Firefly Aerospace The Moon may look like barren rock, but it’s actually covered in a layer of gravel, pebbles, and dust collectively known as “lunar regolith.” During the Apollo Moon missions, astronauts learned firsthand that the fine, powdery dust – electromagnetically charged due to constant bombardment by solar and cosmic particles – is extremely abrasive and clings to everything: gloves, boots, vehicles, and mechanical equipment. What challenges does that dust pose to future Artemis-era missions to establish long-term outposts on the lunar surface?
That’s the task of an innovative science instrument called RAC-1 (Regolith Adherence Characterization), one of 10 NASA payloads flying aboard the next delivery for the agency’s CLPS (Commercial Lunar Payload Services) initiative and set to be carried to the surface by Firefly Aerospace’s Blue Ghost 1 lunar lander.
Developed by Aegis Aerospace of Webster, Texas, RAC will expose 15 sample materials – fabrics, paint coatings, optical systems, sensors, solar cells, and more – to the lunar environment to determine how tenaciously the lunar dust sticks to each one. The instrument will measure accumulation rates during landing and subsequent routine lander operations, aiding identification of those materials which best repel or shed dust. The data will help NASA and its industry partners more effectively test, upgrade, and protect spacecraft, spacesuits, habitats, and equipment in preparation for continued exploration of the Moon under the Artemis campaign.
“Lunar regolith is a sticky challenge for long-duration expeditions to the surface,” said Dennis Harris, who manages the RAC payload for NASA’s CLPS initiative at the agency’s Marshall Space Flight Center in Huntsville, Alabama. “Dust gets into gears, sticks to spacesuits, and can block optical properties. RAC will help determine the best materials and fabrics with which to build, delivering more robust, durable hardware, products, and equipment.”
Under the CLPS model, NASA is investing in commercial delivery services to the Moon to enable industry growth and support long-term lunar exploration. As a primary customer for CLPS deliveries, NASA aims to be one of many customers on future flights. NASA’s Marshall Space Flight Center in Huntsville, Alabama, manages the development of seven of the 10 CLPS payloads carried on Firefly’s Blue Ghost lunar lander.
Learn more about. CLPS and Artemis at:
https://www.nasa.gov/clps
Alise Fisher
Headquarters, Washington
202-358-2546
Alise.m.fisher@nasa.gov
Headquarters, Washington
202-358-2546
Alise.m.fisher@nasa.gov
Corinne Beckinger
Marshall Space Flight Center, Huntsville, Ala.
256-544-0034
corinne.m.beckinger@nasa.gov
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Last Updated Dec 20, 2024 EditorBeth RidgewayContactCorinne M. Beckingercorinne.m.beckinger@nasa.govLocationMarshall Space Flight Center Related Terms
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By NASA
Download PDF: Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
Energy modulators (EM), also known as energy absorbers, are safety-critical components that are used to control shocks and impulses in a load path. EMs are textile devices typically manufactured out of nylon, Kevlar® and other materials, and control loads by breaking rows of stitches that bind a strong base webbing together as shown in Figure 1. A familiar EM application is a fall-protection harness used by workers to prevent injury from shock loads when the harness arrests a fall. EMs are also widely used in parachute systems to control shock loads experienced during the various stages of parachute system deployment.
Random forest is an innovative algorithm for data classification used in statistics and machine learning. It is an easy to use and highly flexible ensemble learning method. The random forest algorithm is capable of modeling both categorical and continuous data and can handle large datasets, making it applicable in many situations. It also makes it easy to evaluate the relative importance of variables and maintains accuracy even when a dataset has missing values.
Random forests model the relationship between a response variable and a set of predictor or independent variables by creating a collection of decision trees. Each decision tree is built from a random sample of the data. The individual trees are then combined through methods such as averaging or voting to determine the final prediction (Figure 2). A decision tree is a non-parametric supervised learning algorithm that partitions the data using a series of branching binary decisions. Decision trees inherently identify key features of the data and provide a ranking of the contribution of each feature based on when it becomes relevant. This capability can be used to determine the relative importance of the input variables (Figure 3). Decision trees are useful for exploring relationships but can have poor accuracy unless they are combined into random forests or other tree-based models.
The performance of a random forest can be evaluated using out-of-bag error and cross-validation techniques. Random forests often use random sampling with replacement from the original dataset to create each decision tree. This is also known as bootstrap sampling and forms a bootstrap forest. The data included in the bootstrap sample are referred to as in-the-bag, while the data not selected are out-of-bag. Since the out-of-bag data were not used to generate the decision tree, they can be used as an internal measure of the accuracy of the model. Cross-validation can be used to assess how well the results of a random forest model will generalize to an independent dataset. In this approach, the data are split into a training dataset used to generate the decision trees and build the model and a validation dataset used to evaluate the model’s performance. Evaluating the model on the independent validation dataset provides an estimate of how accurately the model will perform in practice and helps avoid problems such as overfitting or sampling bias. A good model performs well on
both the training data and the validation data.
The complex nature of the EM system made it difficult for the team to identify how various parameters influenced EM behavior. A bootstrap forest analysis was applied to the test dataset and was able to identify five key variables associated with higher probability of damage and/or anomalous behavior. The identified key variables provided a basis for further testing and redesign of the EM system. These results also provided essential insight to the investigation and aided in development of flight rationale for future use cases.
For information, contact Dr. Sara R. Wilson. sara.r.wilson@nasa.gov
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By NASA
5 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Seen at the center of this image, NASA’s retired InSight Mars lander was captured by the agency’s Mars Reconnaissance Orbiter using its High-Resolution Imagine Science Experiment (HiRISE) camera on Oct. 23, 2024.NASA/JPL-Caltech/University of Arizona New images taken from space show how dust on and around InSight is changing over time — information that can help scientists learn more about the Red Planet.
NASA’s Mars Reconnaissance Orbiter (MRO) caught a glimpse of the agency’s retired InSight lander recently, documenting the accumulation of dust on the spacecraft’s solar panels. In the new image taken Oct. 23 by MRO’s High-Resolution Imaging Science Experiment (HiRISE) camera, InSight’s solar panels have acquired the same reddish-brown hue as the rest of the planet.
After touching down in November 2018, the lander was the first to detect the Red Planet’s marsquakes, revealing details of the crust, mantle, and core in the process. Over the four years that the spacecraft collected science, engineers at NASA’s Jet Propulsion Laboratory in Southern California, which led the mission, used images from InSight’s cameras and MRO’s HiRISE to estimate how much dust was settling on the stationary lander’s solar panels, since dust affected its ability to generate power.
NASA retired InSight in December 2022, after the lander ran out of power and stopped communicating with Earth during its extended mission. But engineers continued listening for radio signals from the lander in case wind cleared enough dust from the spacecraft’s solar panels for its batteries to recharge. Having detected no changes over the past two years, NASA will stop listening for InSight at the end of this year.
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NASA’s InSight Mars lander acquires the same reddish-brown hue as the rest of the planet in a set of images from 2018 to 2024 that were captured by the agency’s Mars Reconnaissance Orbiter using its High-Resolution Imagine Science Experiment (HiRISE) camera.NASA/JPL-Caltech/University of Arizona Scientists requested the recent HiRISE image as a farewell to InSight, as well as to monitor how its landing site has changed over time.
“Even though we’re no longer hearing from InSight, it’s still teaching us about Mars,” said science team member Ingrid Daubar of Brown University in Providence, Rhode Island. “By monitoring how much dust collects on the surface — and how much gets vacuumed away by wind and dust devils — we learn more about the wind, dust cycle, and other processes that shape the planet.”
Dust Devils and Craters
Dust is a driving force across Mars, shaping both the atmosphere and landscape. Studying it helps scientists understand the planet and engineers prepare for future missions (solar-powered and otherwise), since dust can get into sensitive mechanical parts.
When InSight was still active, scientists matched MRO images of dust devil tracks winding across the landscape with data from the lander’s wind sensors, finding these whirling weather phenomena subside in the winter and pick up again in the summer.
The imagery also helped with the study of meteoroid impacts on the Martian surface. The more craters a region has, the older the surface there is. (This isn’t the case with Earth’s surface, which is constantly recycled as tectonic plates slide over one another.) The marks around these craters fade with time. Understanding how fast dust covers them helps to ascertain a crater’s age.
Another way to estimate how quickly craters fade has been studying the ring of blast marks left by InSight’s retrorocket thrusters during landing. Much more prominent in 2018, those dark marks are now returning to the red-brown color of the surrounding terrain.
HiRISE has captured many other spacecraft images, including those of NASA’s Perseverance and Curiosity rovers, which are still exploring Mars, as well as inactive missions, like the Spirit and Opportunity rovers and the Phoenix lander.
“It feels a little bittersweet to look at InSight now. It was a successful mission that produced lots of great science. Of course, it would have been nice if it kept going forever, but we knew that wouldn’t happen,” Daubar said.
More About MRO and InSight
The University of Arizona, in Tucson, operates HiRISE, which was built by Ball Aerospace & Technologies Corp., in Boulder, Colorado. A division of Caltech in Pasadena, California, JPL manages the MRO project and managed InSight for NASA’s Science Mission Directorate, Washington.
The InSight mission was part of NASA’s Discovery Program, managed by the agency’s Marshall Space Flight Center in Huntsville, Alabama. Lockheed Martin Space in Denver built the InSight spacecraft, including its cruise stage and lander, and supported spacecraft operations for the mission.
A number of European partners, including France’s Centre National d’Études Spatiales (CNES) and the German Aerospace Center (DLR), supported the InSight mission. CNES provided the Seismic Experiment for Interior Structure (SEIS) instrument to NASA, with the principal investigator at IPGP (Institut de Physique du Globe de Paris). Significant contributions for SEIS came from IPGP; the Max Planck Institute for Solar System Research (MPS) in Germany; the Swiss Federal Institute of Technology (ETH Zurich) in Switzerland; Imperial College London and Oxford University in the United Kingdom; and JPL. DLR provided the Heat Flow and Physical Properties Package (HP3) instrument, with significant contributions from the Space Research Center (CBK) of the Polish Academy of Sciences and Astronika in Poland. Spain’s Centro de Astrobiología (CAB) supplied the temperature and wind sensors.
For more about the missions:
https://science.nasa.gov/mission/insight
science.nasa.gov/mission/mars-reconnaissance-orbiter
News Media Contacts
Andrew Good
Jet Propulsion Laboratory, Pasadena, Calif.
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andrew.c.good@jpl.nasa.gov
Karen Fox / Molly Wasser
NASA Headquarters, Washington
202-358-1600
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Last Updated Dec 16, 2024 Related Terms
InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) Jet Propulsion Laboratory Mars Mars Reconnaissance Orbiter (MRO) Radioisotope Power Systems (RPS) Explore More
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By NASA
5 Min Read NASA Technologies Aim to Solve Housekeeping’s Biggest Issue – Dust
This artist rendering of Electrostatic Dust Lofting (EDL) examines the lofting of lunar dust when electrostatic charging occurs after exposure to ultraviolet light. If you thought the dust bunnies under your sofa were an issue, imagine trying to combat dust on the Moon. Dust is a significant challenge for astronauts living and working on the lunar surface. So, NASA is developing technologies that mitigate dust buildup enabling a safer, sustainable presence on the Moon.
A flight test aboard a suborbital rocket system that will simulate lunar gravity is the next step in understanding how dust mitigation technologies can successfully address this challenge. During the flight test with Blue Origin, seven technologies developed by NASA’s Game Changing Development program within the agency’s Space Technology Mission Directorate will study regolith mechanics and lunar dust transport in a simulated lunar gravity environment.
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The technologies featured in this animation are Electrostatic Dust Lofting (EDL), Electrodynamic Regolith Conveyor (ERC), Hermes Lunar-G, ISRU Pilot Excavator (IPEx), Clothbot, Duneflow, and Vertical Lunar Regolith Conveyor (VLRC). Each of these technology payloads will advance our understanding of regolith mechanics and lunar dust transport through flight testing in space with simulated lunar gravity.NASA / Advanced Concepts Lab Why Is Lunar Dust a Problem?
With essentially no atmosphere, dust gets lofted, or lifted by the surface, by a spacecraft’s plumes as it lands on the lunar surface. But it can also be lofted through electrostatic charges. Lunar dust is electrostatic and ferromagnetic, meaning it adheres to anything that carries a charge.
Kristen John, NASA’s Lunar Surface Innovation Initiative technical integration lead at Johnson Space Center said, “The fine grain nature of dust contains particles that are smaller than the human eye can see, which can make a contaminated surface appear to look clean.”
Although lunar dust can appear smooth with a powder like finish, its particles actually have a jagged shape. Lunar dust can scratch everything from a spacesuit to human lungs. Dust can also prevent hardware from surviving the lunar night when it accumulates on solar panels causing a reduction in available power. A buildup of dust coats thermal radiators, increasing the temperature of the equipment. Lunar dust can also accumulate on windows, camera lenses, and visors leading to obscured vision.
Dirty Moon? Clean It Up.
The projects being tested on the lunar gravity flight with Blue Origin include ClothBot, Electrostatic Dust Lofting (EDL), and Hermes Lunar-G.
ClothBot
When future astronauts perform extra-vehicular activities on the lunar surface they could bring dust into pressurized, habitable areas. The goal of the ClothBot experiment is to mimic and measure the transport of lunar dust as releases from a small patch of spacesuit fabric. When agitated by pre-programmed motions, the compact robot can simulate “doffing,” the movement that occurs when removing a spacesuit. A laser-illuminated imaging system will capture the dust flow in real-time, while sensors record the size and number of particles traveling through the space. This data will be used to understand dust generation rates inside a lander or airlock from extra-vehicular activity and refine models of lunar dust transport for future lunar and potential Martian missions.
Electrostatic Dust Lofting
This technology will examine the lofting of lunar dust when electrostatic charging occurs after exposure to ultraviolet light. The EDL’s camera with associated lights will record and illuminate for the duration of the flight. During the lunar gravity phase of the flight, a vacuum door containing the dust sample will release and the ultraviolet light source will illuminate the substance, charging the grains until they electrostatically repel one another and become lofted. The lofted dust will pass through a sheet laser as it rises up from the surface. When the lunar gravity phase ends, the ultraviolet light source disables, and the camera will continue recording until the end of the flight. This data will inform dust mitigation modeling efforts for future Moon missions.
Hermes Lunar-G
NASA partnered with Texas A&M and Texas Space Technology Applications and Research (T STAR) to develop Hermes Lunar-G, technology that utilizes flight-proven hardware to conduct experiments with regolith simulants. Hermes was previously a facility on the International Space Station. Hermes Lunar-G repurposed Hermes hardware to study lunar regolith simulants. The Hermes Lunar-G technology uses four canisters to compress the simulants during flight, takeoff, and landing. When the technology is in lunar gravity, it will decompress the contents of the canisters while high-speed imagery and sensors capture data. Results of this experiment will provide information on regolith mechanics that can be used in a variety of computational models. The results of Hermes Lunar-G will be compared to microgravity data from the space station as well as similar data acquired from parabolic flights for lunar and microgravity flight profiles.
The Future of Dust Mitigation
As a primary challenge of lunar exploration, dust mitigation influences several NASA technology developments. Capabilities from In-Situ Resource Utilization to surface power and mobility, rely on some form of dust mitigation, making it a cross-cutting area.
Learning some of the fundamental properties of how lunar dust behaves and how lunar dust impacts systems has implications far beyond dust mitigation and environments. Advancing our understanding of the behavior of lunar dust and advancing our dust mitigation technologies benefits most capabilities planned for use on the lunar surface."
Kristen John
NASA’s Lunar Surface Innovation Initiative Technical Integration Lead
Engineering teams perform a variety of tests to mitigate dust, ensuring it doesn’t cause damage to hardware that goes to the Moon. NASA’s Game Changing Development program, created a reference guide for lunar dust mitigation to help engineers build hardware destined for the lunar surface.
NASA’s Flight Opportunities program funded the Blue Origin flight test as well as the vehicle capability enhancements to enable the simulation of lunar gravity during suborbital rocket flight for the first time. The payloads are managed under NASA’s Game Changing Development program within the agency’s Space Technology Mission Directorate.
To learn more visit: https://www.nasa.gov/stmd-game-changing-development/
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Last Updated Dec 13, 2024 Related Terms
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By NASA
Congratulations to the selected teams and their schools who will participate in the Lunar Autonomy Challenge! 31 teams were selected for the qualifying round, engaging 229 students from colleges and universities in 15 states. Teams will now move on to a Qualifying Round where they will virtually explore and map the lunar surface using a digital twin of NASA’s lunar mobility robot, the ISRU Pilot Excavator (IPEx). Teams will develop software that can perform set actions without human intervention, navigating the digital IPEx in the harsh, low-light conditions of the Moon. The Qualifying Round will extend to February 28, when the top-scoring teams will proceed to the Final Round, with the winners announced in May 2025.
The Lunar Autonomy Challenge is a collaboration between NASA, The Johns Hopkins University (JHU) Applied Physics Laboratory (APL), Caterpillar Inc., and Embodied AI.
Learn more: https://lunar-autonomy-challenge.jhuapl.edu/
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