2024 Software of the Year Co-Winner – Orbital Debris Engineering Model (ORDEM)

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. 

Powered by WPeMatico

Get The Details…
Bill Keeter

2024 Software of the Year Award Co-Winner -Prognostics Python Packages (ProgPy)

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  

Powered by WPeMatico

Get The Details…
Bill Keeter

2024 Invention of the Year Winner – Thrust Chamber Liner and Fabrication Method

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 

Powered by WPeMatico

Get The Details…
Bill Keeter

Ranger 7 Snaps the Moon

On July 31, 1964, the Ranger 7 spacecraft took this photo, the first image of the Moon taken by a United States spacecraft. 17 minutes later, it crashed into the Moon on the northern rim of the Sea of Clouds as intended. The 4,316 images sent back helped identify safe Moon landing sites for Apollo astronauts.

Until 1964, no closeup photographs of the lunar surface existed. Ranger 7 returned the first high resolution close-up photographs of the lunar surface. The mission marked a turning point in America’s lunar exploration program, taking the country one step closer to a human Moon landing.

Learn more about Ranger 7.

Image credit: NASA/JPL

Powered by WPeMatico

Get The Details…
Monika Luabeya

Tech Today: Remote Sensing Technology Fights Forest Fires

It’s not easy to predict the path of forest fires—a lot depends on constantly changing factors like wind. But it is crucial to be as accurate as possible because the lives, homes, and businesses of the tens of thousands of people living and working in fire-prone areas depend on the reliability of these predictions. Sensors mounted on airplanes or drones that provide a picture of the fire from above are an important tool, and that’s where NASA comes in. 

In partnership with the U.S. Forest Service, local and state firefighting agencies, and the Bureau of Land Management, NASA plays a pivotal role in battling infernos. The agency’s extensive experience and technical expertise in remote sensing technology have significantly improved the speed and accuracy of information relayed to firefighting decision-makers.

According to Don Sullivan, who specialized in information technology design at the time, the Airborne Science Program at NASA’s Ames Research Center in Silicon Valley, California, was integral to that effort.

In the 1990s, NASA began a project to adapt uncrewed aircraft for environmental research. The researchers at Ames wanted to ensure the technology would be useful to the broadest possible spectrum of potential end users. One concept tested during the project was sending data in real-time to the ground via communications links installed on the aircraft.

That link sent data faster and to multiple recipients at once—not just the team on the fire front line, but also the commanders organizing the teams and decision makers looking at the big picture across the entire region throughout the fire season, explained Sullivan.

For the Forest Service, this was a much-needed upgrade to the original system on their crewed jets: rolling up a printout and later thumb drives with thermal sensor data placed into a plastic tube attached to a parachute and dropped out of the airplane. NASA’s remotely piloted aircraft called Ikhana tested the technology, and it’s still used by the agency to collect data on wildfires.

Since the introduction of this technology, wildfires have gotten bigger, burn hotter, and set new records every year. But in California in 2008, this technology helped fight what was then the worst fire season on record. A NASA test flight using a data downlink system provided updated information to the incident managers that was crucial in determining where to send firefighting resources and whether a full evacuation of the town of Paradise was needed.

Without that timely information, said Sullivan, “there likely would have been injuries and certainly property damage that was worse than it turned out to be.”

Explore More

5 min read

NASA Public Engagement Specialist Loves to Inspire Kids with STEM

Article


7 hours ago

3 min read

NASA’s First-Ever Quantum Memory Made at Glenn Research Center

Article


9 hours ago

8 min read

Overview for NASA’s Northrop Grumman 21st Commercial Resupply Mission

NASA, Northrop Grumman, and SpaceX are targeting no earlier than 11:29 a.m. EDT on Saturday,…

Article


1 day ago

Keep Exploring

Discover Related Topics

Earth Observations

Fire and Air Quality

Climate Change

Drones & You

Powered by WPeMatico

Get The Details…
Andrew Wagner