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By NASA
Download PDF: Contact Dynamics Predictions Utilizing theNESC Parameterless Contact Model
Modeling the capture of the Mars Sample Return (MSR) Orbiting Sample (OS) involves understanding complex dynamic behavior, which includes the OS making contact against the interior of the capture enclosure. The MSR Program required numerical verification of the contact dynamics’ predictions produced using their commercial software tools. This commercial software used “free” parameters to set up the contact modeling. Free parameters (also known as free variables) are not based on contact physics. The commercial contact model used by MSR
required seven free parameters including a Hertzian contact stiffness, surface penetration, stiffening exponent, penetration velocity, contact damping, maximum penetration depth for the contact damping value, and a smoothing function. An example of a parameter that is not free is coefficient of friction, which is a physics-based parameter. Consider the free parameter, contact stiffness. Contact stiffness is already present in the finite element model’s (FEM) stiffness matrix where the bodies come into contact, and surface penetration is disallowed in a physically realizable contact model, as FEM meshes should not penetrate one another during contact (i.e., the zero-contact limit penetration constraint condition).
As such, with each set of selected free parameters generating a different contact force signature, additional numerical verification is required to guide setting these parameters. Contact modeling is nonlinear. This means that the stiffness matrices of contacting bodies are continuously updated as the bodies come into contact, potentially recontact (due to vibrations), and disengage. The modal properties of contacting bodies continuously change with state transitions (e.g., stick-to-slip). Some contact models have been proposed and incorporated in commercial finite element analysis solvers, and most involve static loading. A relatively smaller number involve dynamics, which has historically proven challenging.
In 2005, NASA conducted a study testing several commercial contact solvers in predicting contact forces in transient dynamic environments. This was necessitated by the Space Shuttle Program (SSP)—after the February 2003 Columbia accident— deciding to include contact dynamics in the Space Shuttle transient coupled loads analysis (CLA) to capture the impact of contact nonlinearities. This rendered the entire CLA nonlinear. The study found major difficulties executing nonlinear CLAs in commercial software. A nonlinear solver developed by the NESC and Applied Structural Dynamics (ASD) that was able to produce physically realizable results was numerically verified by NASA and later experimentally validated as well. This nonlinear solver was subsequently utilized to execute all NASA SSP CLAs (i.e., crewed space flights) from 2005 to the final flight in 2011, as well as currently supporting the SLS Program.
The objective of the MSR contact verification work was to provide data that could be used by the MSR team to help define the free parameters listed above for the commercial tool contact model. The NESC/ASD solver was used to model contact between simple cantilever and free beams, deriving contact forces and relative displacements. These resulting data can be used to determine parameter values for more complex structures. Two of the modeled configurations, one for axial contact (Figure 1) and the other for stick/friction (Figure 2), and sample results from the NESC nonlinear dynamic analyses are presented in Figures 1 and 2.
For information, contact:
Dr. Dexter Johnson dexter.johnson@nasa.gov
Dr. Arya Majed arya.majed@nasa.gov
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By NASA
At Goddard Space Flight Center, the GSFC Data Science Group has completed the testing for their SatVision Top-of-Atmosphere (TOA) Foundation Model, a geospatial foundation model for coarse-resolution all-sky remote sensing imagery. The team, comprised of Mark Carroll, Caleb Spradlin, Jordan Caraballo-Vega, Jian Li, Jie Gong, and Paul Montesano, has now released their model for wide application in science investigations.
Foundation models can transform the landscape of remote sensing (RS) data analysis by enabling the pre-training of large computer-vision models on vast amounts of remote sensing data. These models can be fine-tuned with small amounts of labeled training and applied to various mapping and monitoring applications. Because most existing foundation models are trained solely on cloud-free satellite imagery, they are limited to applications of land surface or require atmospheric corrections. SatVision-TOA is trained on all-sky conditions which enables applications involving atmospheric variables (e.g., cloud or aerosol).
SatVision TOA is a 3 billion parameter model trained on 100 million images from Moderate Resolution Imaging Spectroradiometer (MODIS). This is, to our knowledge, the largest foundation model trained solely on satellite remote sensing imagery. By including “all-sky” conditions during pre-training, the team incorporated a range of cloud conditions often excluded in traditional modeling. This enables 3D cloud reconstruction and cloud modeling in support of Earth and climate science, offering significant enhancement for large-scale earth observation workflows.
With an adaptable and scalable model design, SatVision-TOA can unify diverse Earth observation datasets and reduce dependency on task-specific models. SatVision-TOA leverages one of the largest public datasets to capture global contexts and robust features. The model could have broad applications for investigating spectrometer data, including MODIS, VIIRS, and GOES-ABI. The team believes this will enable transformative advancements in atmospheric science, cloud structure analysis, and Earth system modeling.
The model architecture and model weights are available on GitHub and Hugging Face, respectively. For more information, including a detailed user guide, see the associated white paper: SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery.
Examples of image reconstruction by SatVision-TOA. Left: MOD021KM v6.1 cropped image chip using MODIS bands [1, 3, 2]. Middle: The same images with randomly applied 8×8 mask patches, masking 60% of the original image. Right: The reconstructed images produced by the model, along with their respective Structural Similarity Index Measure (SSIM) scores. These examples illustrate the model’s ability to preserve structural detail and reconstruct heterogeneous features, such as cloud textures and land-cover transitions, with high fidelity.NASAView the full article
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By Space Force
Chief Master Sgt. of the Space Force John Bentivegna introduces "The B-Line," a new quarterly memo that will serve as a direct line of communication between the Space Force senior leader and Guardians.
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By NASA
NASA, along with members of the FAA and commercial drone engineers, gathered in the Dallas area May 25, 2024, to view multiple delivery drones operating in a shared airspace beyond visual line of sight using an industry-developed, NASA-originated uncrewed aircraft system traffic management system.NASA NASA’s Uncrewed Aircraft Systems Traffic Management Beyond Visual Line of Sight (UTM BVLOS) subproject aims to support the growing demand for drone flights across the globe.
Uncrewed aircraft systems (UAS), or drones, offer an increasing number of services, from package delivery to critical public safety operations, like search and rescue missions. However, without special waivers, these flights are currently limited to visual line of sight – or only as far as the pilot can see – which is roughly no farther than one mile from the operator. As the FAA works to authorize flights beyond this point, NASA is working with industry and the Federal Aviation Administration (FAA) to operationalize an uncrewed traffic management system for these operations.
NASA’s UTM Legacy
NASA’s Uncrewed Aircraft Systems Traffic Management, or UTM, was first developed at NASA’s Ames Research Center in California’s Silicon Valley in 2013, and enables drones to safely and efficiently integrate into air traffic that is already flying in low-altitude airspace. UTM is based on digital sharing of each user’s planned flight details, ensuring each user has the same situational awareness of the airspace.
NASA performed a series of drone flight demonstrations using UTM concepts in rural areas and densely populated cities under the agency’s previous UTM project . And commercial drone companies have since utilized NASA’s UTM concepts and delivery operations in limited areas.
Several projects supporting NASA’s Advanced Air Mobility or AAM mission are working on different elements to help make AAM a reality and one of these research areas is automation.NASA / Graphics UTM Today
NASA research is a driving force in making routine drone deliveries a reality. The agency is supporting a series of commercial drone package deliveries beyond visual line of sight, some of which kicked off in August 2024 in Dallas, Texas. Commercial operators are using NASA’s UTM-based capabilities during these flights to share data and planned flight routes with other operators in the airspace, detect and avoid hazards, and maintain situational awareness. All of these capabilities allow operators to safely execute their operations in a shared airspace below 400 feet and away from crewed aircraft. These drone operations in Dallas are a collaboration between NASA, the FAA, industry drone operators, public safety operators, and others.
These initial flights will help validate UTM capabilities through successful flight operation evaluations and inform the FAA’s rulemaking for safely expanding drone operations beyond visual line of sight.
The agency will continue to work with industry and government partners on more complex drone operations in communities across the country. NASA is also working with partners to leverage UTM for other emerging operations, including remotely piloted air cargo delivery and air taxi flights. UTM infrastructure could also support high-altitude operations for expanded scientific research, improved disaster response, and more.
NASA UTM BVLOS
NASA’s UTM Beyond Visual Line of Site (UTM BVLOS) subproject is leading this effort, under the Air Traffic Management eXploration portfolio within the agency’s Aeronautics Research Mission Directorate. This work is in support of NASA’s Advanced Air Mobility Mission, which seeks to transform our communities by bringing the movement of people and goods off the ground, on demand, and into the sky.
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By NASA
4 min read
Expanded AI Model with Global Data Enhances Earth Science Applications
On June 22, 2013, the Operational Land Imager (OLI) on Landsat 8 captured this false-color image of the East Peak fire burning in southern Colorado near Trinidad. Burned areas appear dark red, while actively burning areas look orange. Dark green areas are forests; light green areas are grasslands. Data from Landsat 8 were used to train the Prithvi artificial intelligence model, which can help detect burn scars. NASA Earth Observatory NASA, IBM, and Forschungszentrum Jülich have released an expanded version of the open-source Prithvi Geospatial artificial intelligence (AI) foundation model to support a broader range of geographical applications. Now, with the inclusion of global data, the foundation model can support tracking changes in land use, monitoring disasters, and predicting crop yields worldwide.
The Prithvi Geospatial foundation model, first released in August 2023 by NASA and IBM, is pre-trained on NASA’s Harmonized Landsat and Sentinel-2 (HLS) dataset and learns by filling in masked information. The model is available on Hugging Face, a data science platform where machine learning developers openly build, train, deploy, and share models. Because NASA releases data, products, and research in the open, businesses and commercial entities can take these models and transform them into marketable products and services that generate economic value.
“We’re excited about the downstream applications that are made possible with the addition of global HLS data to the Prithvi Geospatial foundation model. We’ve embedded NASA’s scientific expertise directly into these foundation models, enabling them to quickly translate petabytes of data into actionable insights,” said Kevin Murphy, NASA chief science data officer. “It’s like having a powerful assistant that leverages NASA’s knowledge to help make faster, more informed decisions, leading to economic and societal benefits.”
AI foundation models are pre-trained on large datasets with self-supervised learning techniques, providing flexible base models that can be fine-tuned for domain-specific downstream tasks.
Crop classification prediction generated by NASA and IBM’s open-source Prithvi Geospatial artificial intelligence model. Focusing on diverse land use and ecosystems, researchers selected HLS satellite images that represented various landscapes while avoiding lower-quality data caused by clouds or gaps. Urban areas were emphasized to ensure better coverage, and strict quality controls were applied to create a large, well-balanced dataset. The final dataset is significantly larger than previous versions, offering improved global representation and reliability for environmental analysis. These methods created a robust and representative dataset, ideal for reliable model training and analysis.
The Prithvi Geospatial foundation model has already proven valuable in several applications, including post-disaster flood mapping and detecting burn scars caused by fires.
One application, the Multi-Temporal Cloud Gap Imputation, leverages the foundation model to reconstruct the gaps in satellite imagery caused by cloud cover, enabling a clearer view of Earth’s surface over time. This approach supports a variety of applications, including environmental monitoring and agricultural planning.
Another application, Multi-Temporal Crop Segmentation, uses satellite imagery to classify and map different crop types and land cover across the United States. By analyzing time-sequenced data and layering U.S. Department of Agriculture’s Crop Data, Prithvi Geospatial can accurately identify crop patterns, which in turn could improve agricultural monitoring and resource management on a large scale.
The flood mapping dataset can classify flood water and permanent water across diverse biomes and ecosystems, supporting flood management by training models to detect surface water.
Wildfire scar mapping combines satellite imagery with wildfire data to capture detailed views of wildfire scars shortly after fires occurred. This approach provides valuable data for training models to map fire-affected areas, aiding in wildfire management and recovery efforts.
Burn scar mapping generated by NASA and IBM’s open-source Prithvi Geospatial artificial intelligence model. This model has also been tested with additional downstream applications including estimation of gross primary productivity, above ground biomass estimation, landslide detection, and burn intensity estimations.
“The updates to this Prithvi Geospatial model have been driven by valuable feedback from users of the initial version,” said Rahul Ramachandran, AI foundation model for science lead and senior data science strategist at NASA’s Marshall Space Flight Center in Huntsville, Alabama. “This enhanced model has also undergone rigorous testing across a broader range of downstream use cases, ensuring improved versatility and performance, resulting in a version of the model that will empower diverse environmental monitoring applications, delivering significant societal benefits.”
The Prithvi Geospatial Foundation Model was developed as part of an initiative of NASA’s Office of the Chief Science Data Officer to unlock the value of NASA’s vast collection of science data using AI. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT), based at Marshall, IBM Research, and the Jülich Supercomputing Centre, Forschungszentrum, Jülich, designed the foundation model on the supercomputer Jülich Wizard for European Leadership Science (JUWELS), operated by Jülich Supercomputing Centre. This collaboration was facilitated by IEEE Geoscience and Remote Sensing Society.
For more information about NASA’s strategy of developing foundation models for science, visit https://science.nasa.gov/artificial-intelligence-science.
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