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The NASA Science Mission Directorate (SMD) instituted the Entrepreneurs Challenge to identify innovative ideas and technologies from small business start-ups with the potential to advance the agency’s science goals. Geolabe—a prize winner in the latest Entrepreneurs Challenge—has developed a way to use artificial intelligence to identify global methane emissions. Methane is a greenhouse gas that significantly contributes to global warming, and this promising new technology could provide data to help decision makers develop strategies to mitigate climate change.
SMD sponsored Entrepreneurs Challenge events in 2020, 2021, and 2023. Challenge winners were awarded prize money—in 2023 the total Entrepreneurs Challenge prize value was $1M. To help leverage external funding sources for the development of innovative technologies of interest to NASA, SMD involved the venture capital community in Entrepreneurs Challenge events. Numerous challenge winners have subsequently received funding from both NASA and external sources (e.g., other government agencies or the venture capital community) to further develop their technologies.
Each Entrepreneurs Challenge solicited submissions in specific focus areas such as mass spectrometry technology, quantum sensors, metamaterials-based sensor technologies, and more. The focus areas of the latest 2023 challenge included lunar surface payloads and climate science.
A recent Entrepreneurs Challenge success story involves 2023 challenge winner Geolabe—a startup founded by Dr. Claudia Hulbert and Dr. Bertrand Rouet-Leduc in 2020 in Los Alamos, New Mexico. The Geolabe team developed a method that uses artificial intelligence (AI) to automatically detect methane emissions on a global scale.
This image taken from a NASA visualization shows the complex patterns of methane emissions around the globe in 2018, based on data from satellites, inventories of human activities, and NASA global computer models. Credit: NASA’s Scientific Visualization Studio As global temperatures rise to record highs, the pressure to curb greenhouse gas emissions has intensified. Limiting methane emissions is particularly important since methane is the second largest contributor to global warming, and is estimated to account for approximately a third of global warming to date. Moreover, because methane stays in the atmosphere for a shorter amount of time compared to CO2, curbing methane emissions is widely considered to be one of the fastest ways to slow down the rate of global warming.
However, monitoring methane emissions and determining their quantities has been challenging due to the limitations of existing detection methods. Methane plumes are invisible and odorless, so they are typically detected with specialized equipment such as infrared cameras. The difficulty in finding these leaks from space is akin to finding a needle in a haystack. Leaks are distributed around the globe, and most of the methane plumes are relatively small, making them easy to miss in satellite data.
Multispectral satellite imagery has emerged as a viable methane detection tool in recent years, enabling routine measurements of methane plumes at a global scale every few days. However, with respect to methane, these measurements suffer from very poor signal to noise ratio, which has thus far allowed detection of only very large emissions (2-3 tons/hour) using manual methods.
This landscape of “mountains” and “valleys” speckled with glittering stars is actually the edge of a nearby, young, star-forming region called NGC 3324 in the Carina Nebula. Captured in infrared light by NASA’s new James Webb Space Telescope, this image reveals for the first time previously invisible areas of star birth. Credit: NASA, ESA, CSA, and STScI The Geolabe team has developed a deep learning architecture that automatically identifies methane signatures in existing open-source spectral satellite data and deconvolves the signal from the noise. This AI method enables automatic detection of methane leaks at 200kg/hour and above, which account for over 85% of the methane emissions in well-studied, large oil and gas basins. Information gained using this new technique could help inform efforts to mitigate methane emissions on Earth and automatically validate their effects. This Geolabe project was featured in Nature Communications on May 14, 2024.
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NASA Science Mission Directorate
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Last Updated Aug 20, 2024 Related Terms
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By NASA
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Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Johnson Space Center: ORDEM represents the state of the art in orbital debris models intended for engineering analysis. It is a data-driven model, relying on large quantities of radar, optical, in situ, and laboratory measurement data. When released, it was the first software code to include a model for different orbital debris material densities, population models from low Earth orbit (LEO) all the way to Geosynchronous orbit (GEO), and uncertainties in each debris population.
ORDEM allows users to compute the orbital debris flux on any satellite in Earth orbit. This allows satellite designers to mitigate possible orbital debris damage to a spacecraft and its instruments using shielding and design choices, thereby extending the useful life of the mission and its experiments. The model also has a mode that simulates debris telescope/radar observations from the ground. Both it and the spacecraft flux mode can be used to design experiments to measure the meteoroid and orbital debris environments.
ORDEM is used heavily in the hypervelocity protection community, those that design, build, and test shielding for spacecraft and rocket upper stages. The fidelity of the ORDEM model allows for the optimization of shielding to balance mission success criteria, risk posture, and cost considerations.
As both government and civilian actors continue to exploit the space environment for security, science, and the economy, it is important that we track the debris risks in increasingly crowded orbits, in order to minimize damage to these space assets to make sure these missions continue to operate safely. ORDEM is NASA’s primary tool for computing and mitigating these risks.
ORDEM is used by NASA, the Department of Defense, and other U.S. government agencies, directly or indirectly (via the Debris Assessment Software, MSC-26690-1) to evaluate collision risk for large trackable objects, as well as other mission-ending risks associated with small debris (such as tank ruptures or wiring cuts). In addition to the use as an engineering tool, ORDEM has been used by NASA and other missions in the conceptual design phase to analyze the frequency of orbital debris impacts on potential in situ sensors that could detect debris too small to be detected from ground-based assets.
Commercial and academic users of ORDEM include Boeing, SpaceX, Northrop Grumman, the University of Colorado, California Polytechnic State University, among many others. These end users, similar to the government users discussed above, use the software to (1) directly determine potential hazards to spaceflight resulting from flying through the debris environment, and (2) research how the debris environment varies over time to better understand what behaviors may be able to mitigate the growth of the environment.
The quality and quantity of data available to the NASA Orbital Debris Program Office (ODPO) for the building, verification, and validation of the ORDEM model is greater than for any other entity that performs similar research. Many of the models used by other research and engineering organizations are derived from the models that ODPO has published after developing them for use in ORDEM.
ORDEM Team
Alyssa Manis Andrew B, Vavrin Brent A. Buckalew Christopher L. Ostrom Heather Cowardin Jer-chyi Liou John H, Seago John Nicolaus Opiela Mark J. Matney, Ph.D. Matthew Horstman Phillip D. Anz-Meador, Ph.D. Quanette Juarez Paula H. Krisko, Ph.D. Yu-Lin Xu, Ph.D. Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Ames Research Center: ProgPy is an open-source Python package supporting research and development of prognostics, health management, and predictive maintenance tools.
Prognostics is the science of prediction, and the field of Prognostics and Health Management (PHM) aims at estimating the current physical health of a system (e.g., motor, battery, etc.) and predicting how the system will degrade with use. The results of prognostics are used across industries to prevent failure, preserve safety, and reduce maintenance costs.
Prognostics, and prediction in general, is a very difficult and complex undertaking. Accurate prediction requires a model of the performance and degradation of complex systems as a function of time and use, estimation and management of uncertainty, representation of system use profiles, and ability to represent impact of neighboring systems and the environment. Any small discrepancy between the model and the actual system is compounded repeatedly, resulting in a large variation in the resulting prediction. For this reason, prognostics requires complex and capable algorithms, models, and software systems.
The ProgPy architecture can be thought of as three innovations: the Prognostic Models, the Prognostic Engine, Prognostic Support Tools.
The first part of the ProgPy innovation is the Prognostic Models. The model describes the prognostic behavior of the specific system of interest. ProgPy’s architecture includes a spectrum of modeling methodologies, ranging from physics-based models to entirely data-driven or hybrid techniques. Most users develop their own physics-based model, train one of the ProgPy data-driven models (e.g., Neural-Network models), or some hybrid of the two. A set of mature models for systems like batteries, electric motors, pumps, and valves are distributed in ProgPy. For these parameterized models, users tune the model to their specific system using the model tuning tools. The Prognostics Engine and Support Tools are built on top of these models, meaning a user that creates a new model will immediately be able to take advantage of the other features of ProgPy.
The Prognostic Engine is the most important part of ProgPy and forms the backbone of the software. The Prognostics Engine uses a Prognostics Model to perform the key functions of prognostics and health state estimation. The value in this design is that the Prognostics Engine can use any ProgPy model, whether it be a model distributed with ProgPy or a custom model created by users, to perform health state estimation and prognostics in a configurable way. The components of the Prognostics Engine are extendable, allowing users to implement their own state estimation or prediction algorithm for use with ProgPy models or use one distributed with ProgPy. Given the Prognostics Engine and a model, users can start performing prognostics for their application. This flexible and extendable framework for performing prognostics is truly novel and enables the widespread impact of ProgPy in the prognostic community.
The Prognostic Support Tools are a set of features that aid with the development, tuning, benchmarking, evaluation, and visualization of prognostic models and Prognostics Engine results (i.e., predictions). Like the Prognostic Engine, the support tools work equally with models distributed with ProgPy or custom models created by users. A user creating a model immediately has access to a wide array of tools to help them with their task.
Detailed documentation, examples, and tutorials of all these features are available to help users learn and use the software tools.
These three innovations of ProgPy implement architectures and widely used prognostics and health management functionality, supporting both researchers and practitioners. ProgPy combines technologies from across NASA projects and mission directorates, and external partners into a single package to support NASA missions and U.S. industries. Its innovative framework makes it applicable to a wide range of applications, providing enhanced capabilities not available in other, more limited, state-of-the-art software packages.
ProgPy offers unique features and a breadth and depth of unmatched capabilities when compared to other software in the field. It is novel in that it equips users with the tools necessary to do prognostics in their applications as-is, eliminating the need to adapt their use case to comply with the software available. This feature of ProgPy is an improvement upon the current state-of-the-art, as other prognostics software are often developed for specific use cases or based on a singular modeling method (Dadfarina and Drozdov, 2013; Davidson-Pilon, 2022; Schreiber, 2017). ProgPy’s unique approach opens a world of possibilities for researchers, practitioners, and developers in the field of prognostics and health management, as well as NASA missions and U.S. industries.
ProgPy Team:
Adam J Sweet, Aditya Tummala, Chetan Shrikant Kulkarni Christopher Allen Teubert Jason Watkins Kateyn Jarvis Griffith Matteo Corbetta Matthew John Daigle Miryam Stautkalns Portia Banerjee Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA Marshall Space Flight Center: A thrust chamber assembly (TCA) is the critical and central component in a rocket engine that provides thrust to propel a launch vehicle into space. Since the 1960s, while small improvements in TCA performance have been made, little has been done to reduce weight, improve development timelines, and reduce manufacturing cost. This invention makes dramatic improvements in all three areas.
This Thrust Chamber Liner and Fabrication Method technology eliminates complex, bolted joints by using 3D printing and large-scale additive manufacturing (AM) to fabricate a one-piece TCA. This creates a combined combustion chamber and nozzle. A novel composite overwrap provides support with an overall mass reduction of >40%. The TCA is the heaviest component on the rocket engine, so every pound eliminated allows for additional payload. The benefits include significantly better performance of launch vehicles, consolidation of parts, and a simplified fabrication that reduces cost and lead time.
A liquid rocket engine provides thrust through the injection of a fuel and oxidizer into a combustion chamber then expanding the hot gases through a nozzle. The engine’s core component is the TCA, which comprises an injector, a combustion chamber, and a nozzle. To prevent the TCA’s wall material from reaching melting temperatures, a regenerative cooling system is employed. Small internal channels circulate either fuel or oxidizer as a coolant before it’s injected into the combustion chamber for the combustion process.
The TCA must withstand a wide range of challenges, including extreme temperatures (from cryogenic temperatures below -290 °F and up to +6,000°F), high pressures (up to 6,000 psi), demanding duty cycles that impact fatigue life, engine dynamics, and the reactive thrust loads. This necessitates the use of a variety of materials and involves intricate manufacturing and joining processes while maintaining exceptionally tight tolerances. The walls can be as thin as a few sheets of paper, measuring approximately 0.02 inch, increasing the complexity of the technological challenge.
The design and construction of the combined combustion chamber and nozzle has several novel features: (1) A NASA-developed alloy, Copper-Chrome-Niobium (GRCop-42) was matured for the combustion chamber resulting in a 45% increase in wall temperatures. (2) The integral channel design supports effective cooling, manifolds, and a range of features that facilitate an integrated coupled nozzle and composite overwrap. (3) The chamber and its internal structures are produced using a NASA-developed (and later commercialized) process known as laser powder bed fusion (L-PBF). This uses minimal exterior material, allowing the composite overwrap to effectively contain the high pressure and various engine loads. (4) Stock material and integral features build the chamber nozzle onto the aft end using a different alloy, optimizing the overall strength-to-weight ratio. (5) Traditionally, AM requires a build plate onto which parts are fabricated, but this innovation can use the chamber itself as the build plate. (6) A large-scale AM process called laser powder directed energy deposition (LP-DED) was developed with a new NASA alloy for hydrogen environments, called NASA HR-1 (HR = hydrogen resistant). The AM employed to integrate the chamber and nozzle involves the use of two distinct AM processes and alloys, using GRCop-42 for the chamber and NASA HR-1 for the nozzle.
A composite overwrap significantly reduces weight and provides adequate strength to sustain required pressures and loads. Various filament winding techniques and fiber orientations, guided by modeling simulations effectively counteract the (barrel) static pressure, startup, and shutdown loads, thrust, and gimbal loads. The unique locking features designed into the chamber include turn-around regions (referred to as “humps”) to eliminate complex tooling.
Traditional TCA design incorporates multiple manifolds, adding unnecessary weight and bolted or welded joints. These joints necessitate exceedingly tight tolerances, polished surface finishes, and intricate sealing mechanisms to prevent leakage. Maintaining precise concentricity among the components and ancillary features, such as shear-lips to avoid hot gas circulation and joint separation, is imperative. The risk of potential leakage can lead to the catastrophic failure of the engine or the entire vehicle. The tragic explosion of the Space Shuttle Challenger serves as a stark reminder of how joint failure, albeit in a solid rocket motor in that case, can have dire consequences. By contrast, this design eliminates these vulnerabilities by employing integrated AM processes to create a one-piece TCA, dramatically improving safety and efficiency.
Thrust Chamber Liner Team
Paul R. Gradl Christopher Stephen Protz Cory Ryan Medina Justin R. Jackson Omar Roberto Mireles Sandra Elam Greene William C. C. Brandsmeier Share
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Last Updated Jul 31, 2024 EditorBill Keeter Related Terms
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By NASA
Curiosity Navigation Curiosity Home Mission Overview Where is Curiosity? Mission Updates Science Overview Instruments Highlights Exploration Goals News and Features Multimedia Curiosity Raw Images Images Videos Audio More Resources Mars Missions Mars Sample Return Mars Perseverance Rover Mars Curiosity Rover MAVEN Mars Reconnaissance Orbiter Mars Odyssey More Mars Missions The Solar System The Sun Mercury Venus Earth The Moon Mars Jupiter Saturn Uranus Neptune Pluto & Dwarf Planets Asteroids, Comets & Meteors The Kuiper Belt The Oort Cloud 3 min read
Sols 4257-4258: A Little Nudge on Kings Canyon
This image was taken by Left Navigation Camera onboard NASA’s Mars rover Curiosity on Sol 4255 (2024-07-26 05:09:58 UTC). NASA/JPL-Caltech Earth planning date: Friday, July 26, 2024
Today’s 2-sol weekend plan is our first taste of a new location for a potential sampling campaign. We call today’s plan type: Drill Sol 1 – triage contact science.
We arrived this morning to a lovely new workspace. The science team has been eagerly observing these lighter-toned rocks first from orbital data, then from our drive direction imaging as we approached them, and now they are right in front of us! Because the science team had been contemplating the possibility of sampling these rocks, the drive that we planned on Wednesday ended in just the right orientation in case the team does decide to drill here. Variables that matter are the rover roll and pitch – so that we can both drill the rock safely but also then deliver sample to our two internal instruments, CheMin and SAM. Additionally, the rover heading needs to be just right so that we can communicate clearly with Earth – perhaps for several weeks if we remain for a campaign! We have specially certified Rover Planners called Sampling Campaign Rover Planners (SCaRPs) and they go into action on Drill Sol 1 to confirm that all those special considerations for drilling are met including finding the actual target on the ground that we want to assess.
This morning, the SCaRPs swiftly found a great target and we named it “Kings Canyon.” Kings Canyon National Park is in the southern Sierra Nevada range in California. Kings Canyon itself is a glacially carved canyon more than a mile deep! The national park also contains some of the world’s largest stands of Giant Sequoia trees.
The Drill Sol 1 plan has two purposes – first to determine if our target, Kings Canyon, meets the science teams criteria for sampling – for example, is it compositionally interesting? The second objective is to determine if the rock and specific target, are safe to drill; can it handle the forces from the drill, for example. We call this activity a “drill preload test.” Therefore, the primary activities in today’s plan are the preload test and contact science on Kings Canyon – we will first brush the target to remove surface dust and then take close-up imaging with our MAHLI instrument and compositional data with our APXS instrument.
In anticipation of a notional full drill on Monday, today’s team was very conservative with the amount of power we used. This meant limiting our remote sensing observations to only those that the team thought were most important to get down timely to support a drill campaign. We’ll use our ChemCam instrument to also study Kings Canyon, ChemCam provides complementary compositional data to the APXS observations. Together these observations will help inform the science about whether they want to proceed with sampling. Today’s plan also includes our typical environmental monitoring observations that we take at a regular cadence.
Hope your weekend is as busy and fun as Curiosity’s!
Written by Elena Amador-French, Science Operations Coordinator at NASA’s Jet Propulsion Laboratory
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Last Updated Jul 29, 2024 Related Terms
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