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By NASA
Inside of the Electrostatics and Surface Physics Laboratory at NASA’s Kennedy Space Center in Florida, an electrodynamic dust shield (EDS) is in view on Jan. 18, 2023. The dust shield is one of the payloads that will fly aboard Firefly Aerospace’s Blue Ghost lunar lander as part of NASA’s Commercial Lunar Payload Services (CLPS) initiative. NASA/Cory Huston Defeating dust may be a small concern for most people on Earth, but for astronauts and spacecraft destined for the Moon or Mars, it is a significant hazard that must be mitigated. That’s why researchers at NASA’s Kennedy Space Center in Florida are seeking innovative ways to use the Electrodynamic Dust Shield (EDS) technology.
The EDS technology is headed to the Moon as part of the agency’s Artemis campaign. This innovative technology will be demonstrated on the lunar surface, where it will use electrical forces to lift and remove lunar regolith, or dirt, from various surfaces.
This dust-mitigating technology is one of 10 payloads aboard the next lunar delivery through NASA’s CLPS (Commercial Lunar Payload Services) initiative, set to launch from the agency’s Kennedy Space Center in Florida Wednesday, Jan. 15, with Firefly Aerospace’s Blue Ghost Lander.
Using transparent electrodes and electric fields, EDS technology can lift and remove dust from a variety of surfaces for space applications ranging from thermal radiators, solar panels, and camera lenses to spacesuits, boots, and helmet visors. Controlling and removing the charged dust will be critical to the success of Moon missions under the agency’s CLPS initiative and Artemis campaign.
“For these CLPS and Artemis missions, dust exposure is a concern because the lunar surface is far different than what we’re used to here,” said Dr. Charles Buhler, lead research scientist at the Electrostatics and Surface Physics Laboratory at Kennedy. “Lunar regolith dust can get into gaskets and seals, into hatches, and even into habitats, which can pose a lot of issues for spacecraft and astronauts.”
Unlike dust particles on Earth, dust on the Moon’s surface is sharp and abrasive – like tiny shards of glass – because it hasn’t been exposed to weathering and elements like water and oxygen.
“Simply brushing lunar regolith across surfaces can make the problem worse because it’s also very electrostatically charged and highly insulating,” Buhler said.
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. EDS was funded by the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCD).
Learn more about. CLPS and Artemis at https://www.nasa.gov/clps.
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By NASA
Illustration of the main asteroid belt, orbiting the Sun between Mars and JupiterNASA NASA’s powerful James Webb Space Telescope includes asteroids on its list of objects studied and secrets revealed.
A team led by researchers at the Massachusetts Institute of Technology (MIT) in Cambridge repurposed Webb’s observations of a distant star to reveal a population of small asteroids — smaller than astronomers had ever detected orbiting the Sun in the main asteroid belt between Mars and Jupiter.
The 138 new asteroids range from the size of a bus to the size of a stadium — a size range in the main belt that has not been observable with ground-based telescopes. Knowing how many main belt asteroids are in different size ranges can tell us something about how asteroids have been changed over time by collisions. That process is related to how some of them have escaped the main belt over the solar system’s history, and even how meteorites end up on Earth.
“We now understand more about how small objects in the asteroid belt are formed and how many there could be,” said Tom Greene, an astrophysicist at NASA’s Ames Research Center in California’s Silicon Valley and co-author on the paper presenting the results. “Asteroids this size likely formed from collisions between larger ones in the main belt and are likely to drift towards the vicinity of Earth and the Sun.”
Insights from this research could inform the work of the Asteroid Threat Assessment Project at Ames. ATAP works across disciplines to support NASA’s Planetary Defense Coordination Office by studying what would happen in the case of an Earth impact and modeling the associated risks.
“It’s exciting that Webb’s capabilities can be used to glean insights into asteroids,” said Jessie Dotson, an astrophysicist at Ames and member of ATAP. “Understanding the sizes, numbers, and evolutionary history of smaller main belt asteroids provides important background about the near-Earth asteroids we study for planetary defense.”
Illustration of the James Webb Space TelescopeNASA The team that made the asteroid detections, led by research scientist Artem Burdanov and professor of planetary science Julien de Wit, both of MIT, developed a method to analyze existing Webb images for the presence of asteroids that may have been inadvertently “caught on film” as they passed in front of the telescope. Using the new image processing technique, they studied more than 10,000 images of the star TRAPPIST-1, originally taken to search for atmospheres around planets orbiting the star, in the search for life beyond Earth.
Asteroids shine more brightly in infrared light, the wavelength Webb is tuned to detect, than in visible light, helping reveal the population of main belt asteroids that had gone unnoticed until now. NASA will also take advantage of that infrared glow with an upcoming mission, the Near-Earth Object (NEO) Surveyor. NEO Surveyor is the first space telescope specifically designed to hunt for near-Earth asteroids and comets that may be potential hazards to Earth.
The paper presenting this research, “Detections of decameter main-belt asteroids with JWST,” was published Dec. 9 in Nature.
The James Webb Space Telescope is the world’s premier space science observatory. Webb is solving mysteries in our solar system, looking beyond to distant worlds around other stars, and probing the mysterious structures and origins of our universe and our place in it. Webb is an international program led by NASA with its partners, ESA (European Space Agency) and CSA (Canadian Space Agency).
For news media:
Members of the news media interested in covering this topic should reach out to the NASA Ames newsroom.
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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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