Engineering topics

Dana Mcneill Lear

Publications and source records attributed to Dana Mcneill Lear.

3 records

NASA NTRS2021

BUMPER: A Tool for Analyzing Spacecraft Micrometeoroid and Orbital Debris Risk

“Bumper” is a computer program for analyzing spacecraft micrometeoroid and orbital debris (MMOD) risk. Bumper was developed in the late-1980s and has been continuously maintained and used since. The user base has grown from a few government entities now include numerous commercial entities as well. The National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) group is responsible for all aspects of the Bumper software. Bumper has been used to characterize MMOD risk on many spacecraft. All of the International Space Station (ISS) modules, visiting vehicles and numerous external components and systems have been analyzed. Bumper was used to analyze the Space Shuttle, Orion, and many space probes, telescopes and satellites. Bumper is also being used to analyze future spacecraft such as the Deep Space Gateway (DSG) and Mars Sample Return (MSR) missions. The Bumper Configuration Control Board (CCB) ensures that all changes to the code are approved, reviewed, and documented. The current Bumper version – “Bumper 3” – is a Fortran executable that utilizes a 64-bit architecture. Bumper has numerous features that make it a powerful tool for analyzing spacecraft MMOD risk. Bumper uses the latest orbital debris and meteoroidenvironment models. Bumper also has a large library of ballistic limit “damage” equations available that can be used for a wide variety of MMOD shielding configurations. Bumper can also handle large spacecraft finite elementmodels (FEMs) and conducts checks of the model. This paper introduces Bumper and the MMOD risk analysis process using a simplified cube-shaped spacecraft model

hypervelocity↗

NASA NTRSDate not supplied

Alternative MMOD Shielding Concepts

The main types of meteoroid and orbital debris (MMOD) shields are single-layer “monolithic” shields, dual-wall “Whipple” shields, and multi-wall shields (“Stuffed Whipple” and “multi-shock” are common types). Aluminum alloys are typically used for the outer bumper layer and for the rear wall of the dual- and multi-wall shields, although carbon-composites are increasingly employed in MMOD shields given their low-mass and high-strength. Ceramic (NextelTM) and KevlarTM fabrics are commonly used for the intermediate layers of Stuffed Whipple shields. The NASA Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) group is continuously working to improve NASA spacecraft MMOD shielding by evaluating new materials and shielding concepts by test and analysis. HVIT has performed many hypervelocity impact tests over several years to evaluate alternative MMOD shield materials and concepts. This paper will provide results of this work in the following areas: (1) Material substitutions to improve radiation and MMOD protection within Stuffed Whipple shields, (2) Metallic and ceramic foam bumper and intermediate layer materials, (3) Self-sealing materials. In the first two areas listed above, the candidate shield materials were tested under similar test conditions and with fixed shield standoff and mass. Damage to the rear wall was quantified and compared to determine the best performing shield materials. In the self-sealing material evaluations, the test objective was to gauge the ability of different materials and techniques to stop leaks in a rear wall with a 1-atmosphere (air) delta-pressure across the wall. This paper provides results of the investigations and describes forward work to continue the development of the most promising MMOD shield alternatives. KevlarTM is a trademark of DuPont de Nemours, Inc. NextelTM is a trademark of 3M Corporation Note, Trade names and trademarks are used in this report for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration.

Hypervelocity↗

NASA NTRSDate not supplied

The Application of Artificial Intelligence Deep Learning to Visually Identify Micrometeoroid and Orbital Debris Impacts

Recent advances in Artificial Intelligence (AI) are changing the World. Novel approaches to training AI systems have led to dramatic reductions in the amount of time required. Training an AI system could take years and teams of people using traditional methods, but with the advancements of Deep Learning (DL) models this training can now be accomplished by an individual in a matter of minutes. The development of “fast AI” libraries has delivered AI to essentially everyone. Democratization of AI power has inspired many to revisit past problems that will benefit from DL approaches. For example, the application of AI has improved detection of breast cancer by 20% compared to traditional detection methods. Computer vision and machine learning are being used to identify soil deficiencies and provide planting recommendations to farmers. Success stories like these and many others have provided inspiration to see if AI can help improve one of our needed capabilities – that of visually identifying micrometeoroid and orbital debris (MMOD) impact damage to spacecraft from images of the spacecraft exterior. The need to visually locate and characterize spacecraft MMOD impact damage has been present since the early days of space travel. This is often done by either having a crew member take photographs of the spacecraft through a window using a hand-held camera or ground personnel directing externally-mounted cameras. The photographs are then transmitted back to Earth for visual analysis. This method of MMOD damage inspection works well and has been used on various spacecraft including the Space Shuttle and the International Space Station (ISS). One of the issues with the current method that we believe AI could improve is the speed and possibly the accuracy in identifying MMOD impacts. Note that detecting MMOD impacts in images can be very difficult. The visual appearance of an MMOD impact can change dramatically with lighting conditions, size of impact, depth of penetration, material types, surface waviness, fabric coverings, camera & lens, distance to surface, spacecraft orientation, analyst experience, and many other factors. Currently, this takes a team of highly-experienced specialists in both the fields of Image Analysis and MMOD impacts. This paper documents our initial research in training an AI DL model using the fast-AI library to identify actual and simulated MMOD impacts and perforations into exposed flat surfaces. While we recognize that this initial goal seems modest, it must be noted that what we have done would have taken teams of individuals and years of training just ten years ago. Our long-term goal is to add complexity and use-cases to the DL model being trained to expand the capabilities of this model so that it can be used to identify MMOD impacts on all types of spacecraft surfaces.

Cameron M Collins↗