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Turning astronauts into Moon explorers
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By NASA
Humans are returning to the Moon—this time, to stay. Because our presence will be more permanent, NASA has selected a location that maximizes line-of-sight communication with Earth, solar visibility, and access to water ice: the Lunar South Pole (LSP). While the Sun is in the lunar sky more consistently at the poles, it never rises more than a few degrees above the horizon; in the target landing regions, the highest possible elevation is 7°. This presents a harsh lighting environment never experienced during the Apollo missions, or in fact, in any human spaceflight experience. The ambient lighting will severely affect the crews’ ability to see hazards and to perform simple work. This is because the human vision system, which despite having a high-dynamic range, cannot see well into bright light and cannot adapt quickly from bright to dark or vice versa. Functional vision is required to perform a variety of tasks, from simple tasks (e.g., walking, operating simple tools) through managing complex machines (e.g., lander elevator, rovers). Thus, the environment presents an engineering challenge to the Agency: one that must be widely understood before it can be effectively addressed.
In past NASA missions and programs, design of lighting and functional vision support systems for extravehicular activity (EVA) or rover operations have been managed at the lowest program level. This worked well for Apollo and low Earth orbit because the Sun angle was managed by mission planning and astronaut self-positioning; helmet design alone addressed all vision challenges. The Artemis campaign presents new challenges to functional vision, because astronauts will be unable to avoid having the sun in their eyes much of the time they are on the lunar surface. This, combined with the need for artificial lighting in the extensive shadowing at the LSP, means that new functional vision support systems must be developed across projects and programs. The design of helmets, windows, and lighting systems must work in a complementary fashion, within and across programs, to achieve a system of lighting and vision support that enables crews to see into darkness while their eyes are light-adapted, in bright light while still dark-adapted, and protects their eyes from injury.
Many of the findings of the assessment were focused on the lack of specific requirements to prevent functional vision impairment by the Sun’s brilliance (which is different from preventing eye injury), while enabling astronauts to see well enough to perform specific tasks. Specifically, tasks expected of astronauts at the LSP were not incorporated into system design requirements to enable system development that ensures functional vision in the expected lighting environment. Consequently, the spacesuit, for example, has flexibility requirements for allowing the astronauts to walk but not for ensuring they can see well enough to walk from brilliant Sun into a dark shadow and back without the risk of tripping or falling. Importantly, gaps were identified in allocation of requirements across programs to ensure that the role of the various programs is for each to understand functional vision. NESC recommendations were offered that made enabling functional vision in the harsh lighting environment a specific and new requirement for the system designers. The recommendations also included that lighting, window, and visor designs be integrated.
The assessment team recommended that a wide variety of simulation techniques, physical and virtual, need to be developed, each with different and well-stated capabilities with respect to functional vision. Some would address the blinding effects of sunlight at the LSP (not easily achieved through virtual approaches) to evaluate performance of helmet shields and artificial lighting in the context of the environment and adaptation times. Other simulations would add terrain features to identify the threats in simple (e.g., walking, collection of samples) and complex (e.g., maintenance and operation of equipment) tasks. Since different facilities have different strengths, they also have different weaknesses. These strengths and limitations must be characterized to enable verification of technical solutions and crew training.
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By NASA
Through NASA’s Artemis campaign, astronauts will land on the lunar surface and use a new generation of spacesuits and rovers as they live, work, and conduct science in the Moon’s South Pole region, exploring more of the lunar surface than ever before. Recently, the agency completed the first round of testing on three commercially owned and developed LTVs (Lunar Terrain Vehicle) from Intuitive Machines, Lunar Outpost, and Venturi Astrolab at NASA’s Johnson Space Center in Houston.NASA/Bill Stafford Venturi Astrolab’s FLEX, Intuitive Machines’ Moon RACER, and Lunar Outpost’s Eagle lunar terrain vehicle – three commercially owned and developed LTVs (Lunar Terrain Vehicle) – are pictured at NASA’s Johnson Space Center in Houston in this photo from Nov. 21, 2024.
As part of an ongoing year-long feasibility study, each company delivered a static mockup of their vehicle to Johnson at the end of September, initiated rover testing in October and completed the first round of testing in December inside the Active Response Gravity Offload System (ARGOS) test facility. Lunar surface gravity is one-sixth of what we experience here on Earth, so to mimic this, ARGOS offers an analog environment that can offload pressurized suited subjects for various reduced gravity simulations.
See how these LTVs were tested.
Image credit: NASA/Bill Stafford
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By NASA
NASA’s Dawn spacecraft captured this image of Vesta as it left the giant asteroid’s orbit in 2012. The framing camera was looking down at the north pole, which is in the middle of the image.NASA/JPL-Caltech/UCLA/MPS/DLR/IDA Known as flow formations, these channels could be etched on bodies that would seem inhospitable to liquid because they are exposed to the extreme vacuum conditions of space.
Pocked with craters, the surfaces of many celestial bodies in our solar system provide clear evidence of a 4.6-billion-year battering by meteoroids and other space debris. But on some worlds, including the giant asteroid Vesta that NASA’s Dawn mission explored, the surfaces also contain deep channels, or gullies, whose origins are not fully understood.
A prime hypothesis holds that they formed from dry debris flows driven by geophysical processes, such as meteoroid impacts, and changes in temperature due to Sun exposure. A recent NASA-funded study, however, provides some evidence that impacts on Vesta may have triggered a less-obvious geologic process: sudden and brief flows of water that carved gullies and deposited fans of sediment. By using lab equipment to mimic conditions on Vesta, the study, which appeared in Planetary Science Journal, detailed for the first time what the liquid could be made of and how long it would flow before freezing.
Although the existence of frozen brine deposits on Vesta is unconfirmed, scientists have previously hypothesized that meteoroid impacts could have exposed and melted ice that lay under the surface of worlds like Vesta. In that scenario, flows resulting from this process could have etched gullies and other surface features that resemble those on Earth.
To explore potential explanations for deep channels, or gullies, seen on Vesta, scientists used JPL’s Dirty Under-vacuum Simulation Testbed for Icy Environments, or DUSTIE, to simulate conditions on the giant asteroid that would occur after meteoroids strike the surface.NASA/JPL-Caltech But how could airless worlds — celestial bodies without atmospheres and exposed to the intense vacuum of space — host liquids on the surface long enough for them to flow? Such a process would run contrary to the understanding that liquids quickly destabilize in a vacuum, changing to a gas when the pressure drops.
“Not only do impacts trigger a flow of liquid on the surface, the liquids are active long enough to create specific surface features,” said project leader and planetary scientist Jennifer Scully of NASA’s Jet Propulsion Laboratory in Southern California, where the experiments were conducted. “But for how long? Most liquids become unstable quickly on these airless bodies, where the vacuum of space is unyielding.”
The critical component turns out to be sodium chloride — table salt. The experiments found that in conditions like those on Vesta, pure water froze almost instantly, while briny liquids stayed fluid for at least an hour. “That’s long enough to form the flow-associated features identified on Vesta, which were estimated to require up to a half-hour,” said lead author Michael J. Poston of the Southwest Research Institute in San Antonio.
Launched in 2007, the Dawn spacecraft traveled to the main asteroid belt between Mars and Jupiter to orbit Vesta for 14 months and Ceres for almost four years. Before ending in 2018, the mission uncovered evidence that Ceres had been home to a subsurface reservoir of brine and may still be transferring brines from its interior to the surface. The recent research offers insights into processes on Ceres but focuses on Vesta, where ice and salts may produce briny liquid when heated by an impact, scientists said.
Re-creating Vesta
To re-create Vesta-like conditions that would occur after a meteoroid impact, the scientists relied on a test chamber at JPL called the Dirty Under-vacuum Simulation Testbed for Icy Environments, or DUSTIE. By rapidly reducing the air pressure surrounding samples of liquid, they mimicked the environment around fluid that comes to the surface. Exposed to vacuum conditions, pure water froze instantly. But salty fluids hung around longer, continuing to flow before freezing.
The brines they experimented with were a little over an inch (a few centimeters) deep; scientists concluded the flows on Vesta that are yards to tens of yards deep would take even longer to refreeze.
The researchers were also able to re-create the “lids” of frozen material thought to form on brines. Essentially a frozen top layer, the lids stabilize the liquid beneath them, protecting it from being exposed to the vacuum of space — or, in this case the vacuum of the DUSTIE chamber — and helping the liquid flow longer before freezing again.
This phenomenon is similar to how on Earth lava flows farther in lava tubes than when exposed to cool surface temperatures. It also matches up with modeling research conducted around potential mud volcanoes on Mars and volcanoes that may have spewed icy material from volcanoes on Jupiter’s moon Europa.
“Our results contribute to a growing body of work that uses lab experiments to understand how long liquids last on a variety of worlds,” Scully said.
Find more information about NASA’s Dawn mission here:
https://science.nasa.gov/mission/dawn/
News Media Contacts
Gretchen McCartney
Jet Propulsion Laboratory, Pasadena, Calif.
818-287-4115
gretchen.p.mccartney@jpl.nasa.gov
Karen Fox / Molly Wasser
NASA Headquarters, Washington
202-358-1600
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov
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Last Updated Dec 20, 2024 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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