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At least 199 records · Page 11

Characterization of Modern Spacecraft Materials under Space-simulated Environment

External spacecraft materials play an important role in satellite protection from the harsh space environment. Research has shown that the physical, chemical, and optical properties of matter change continuously as a result of exposure to solar radiation and aggressive chemical species produced in Earth’s upper atmosphere. Thorough knowledge of the material properties evolution throughout a planned mission lifetime helps to improve the reliability of spacecraft. Moreover, the establishment of correlation factors between true space exposure and accelerated space weather experiments at ground facilities enables accurate prediction of on-orbit material performance based on laboratory-based testing. The presented work aims to evaluate the radiation effects of low Earth orbit (LEO) environment, namely, exposure to the high-energy electrons, atomic oxygen (AO), and vacuum ultraviolet (VUV), of several modern spacecraft materials. The studied materials represent the “flight duplicates” of samples that will be launched as a part of the Materials International Space Station Experiment Flight Facility (MISSE-FF) mission in 2022. MISSE-FF flight sample collection comprises different classes of polymers, including polyimides from the Kapton family, manufactured by E.I du Pont de Nemours and Co., Polyethylene terephthalate (PET) materials, liquid crystal polymers, PI/Polyhedral Oligomeric Silsesquioxanes (POSS), and carbon and glass fiber reinforced polymers. A sequential exposure approach was undertaken to allow monitoring of degradation induced by each environmental component (electrons, AO, and VUV) separately. Surface morphology, optical, and charge transport properties of selected materials were characterized using different techniques, namely, atomic force and scanning electron microscopy, ultraviolet visible (UV/Vis) transmission, reflectance, Bidirectional Reflectance Distribution Function (BRDF), and surface potential decay measurements.

Elena Plis↗

Spacecraft Materials Degradation Under Space-Simulated Low Earth Orbit (LEO) Environment

External spacecraft materials play an important role in satellite protection from the harsh space environment. Research has shown that the physical, chemical, and optical properties of matter change continuously as a result of exposure to solar radiation and aggressive chemical species produced in Earth’s upper atmosphere. Thorough knowledge of the material properties evolution throughout a planned mission lifetime helps to improve the reliability of spacecraft. Moreover, the establishment of correlation factors between true space exposure and accelerated space weather experiments at ground facilities enables accurate prediction of on-orbit material performance based on laboratory-based testing. The presented work aims to evaluate the radiation effects of low Earth orbit (LEO) environment, namely, exposure to the high-energy electrons, atomic oxygen (AO), and vacuum ultraviolet (VUV), of several modern spacecraft materials. The studied materials represent the “flight duplicates” of samples that are launched as a part of the 16th Materials International Space Station Experiment Flight Facility (MISSE-FF) mission in 2022.

Elena A Plis↗

Spacecraft Materials Degradation Under Space-Simulated Low Earth Orbit (LEO) Environment

External spacecraft materials play an important role in satellite protection from the harsh space environment. Research has shown that the physical, chemical, and optical properties of matter change continuously as a result of exposure to solar radiation and aggressive chemical species produced in Earth’s upper atmosphere. Thorough knowledge of the material properties evolution throughout a planned mission lifetime helps to improve the reliability of spacecraft. Moreover, the establishment of correlation factors between true space exposure and accelerated space weather experiments at ground facilities enables accurate prediction of on-orbit material performance based on laboratory-based testing. The presented work aims to evaluate the radiation effects of low Earth orbit (LEO) environment, namely, exposure to the high-energy electrons, atomic oxygen (AO), and vacuum ultraviolet (VUV), of several modern spacecraft materials. The studied materials represent the “flight duplicates” of samples that are launched as a part of the 16th Materials International Space Station Experiment Flight Facility (MISSE-FF) mission in 2022.

Elena Plis↗

Acousto-ultrasonic characterization of fiber reinforced composites

The acousto-ultrasonic technique combines advantageous aspects of acoustic emission and ultrasonic methodologies. Acousto-ultrasonics operates by introducing a repeating series of ultrasonic pulses into a material. The waves introduced simulate the spontaneous stress waves that would arise if the material were put under stress as in the case of acoustic emission measurements. These benign stress waves are detected by an acoustic emission sensor. The physical arrangement of the ultrasonic (input) transducer and acoustic emission (output) sensor is such that the resultant waveform carries an imprint of morphological factors that govern or contribute to material performance. The output waveform is complex, but it can be quantitized in terms of a 'stress wave factor.' The stress wave factor, which can be defined in a number of ways, is a relative measure of the efficiency of energy dissipation in a material. If flaws or other material anomalies exist in the volume being examined, their combined effect appears in the stress wave factor.

Vary, A.↗

Effects of Space Environments: Techniques and Lessons from Selected Flight Experiments

Data describing long-term materials performance under exposure to space environments has accumulated gradually over the past four decades. The authors present here selected results from several previous flight experiments and operational spacecraft, and a few examples from a current flight experiment. As examples of environmental effects on materials, analysis of Gortex samples from the Passive Optical Sample Assembly II (POSA II) experiment on the MIR-Shuttle docking module will be described. Results from a previous evaluation of radiation on silverized Teflon from over ten individual satellites will be summarized. Several examples of contamination effects on materials properties will be presented. These include outgassing of solar arrays onto nearby surfaces of the POSA I experiment and contamination of certain surfaces of the Long Duration Exposure Facility (LDEF). Results from experiments with silicone contamination on satellites in geosynchronous orbit will be compared with measurements on the Solar Maximum satellite and the LDEF. A number of techniques are being attempted to extend the range of exposure conditions present on selected experiments. Use of focusing concentrators on the Effect of Space Environments on Materials (ESEM), POSA, and Materials International Space Station Experiment (MISSE) will be described. A technique for obtaining time-resolved data from a passive materials experiment will be described. The status of an on-going materials flight experiment, MISSE, will be reported. Finally, the authors will draw some conclusions about the current state of knowledge relating to materials chosen for spacecraft applications. Understanding of degradation mechanisms, state of predictive models, and the need to strengthen model inputs will be discussed.

Golden, J. L.↗

Acousto-ultrasonic characterization of fiber reinforced composites

The acousto-ultrasonic technique combines advantageous aspects of acoustic emission and ultrasonic methodologies. Acousto-ultrasonics operates by introducing a repeating series of ultrasonic pulses into a material. The waves introduced simulate the spontaneous stress waves that would arise if the material were put under stress as in the case of acoustic emission measurements. These benign stress waves are detected by an acoustic emission sensor. The physical arrangement of the ultrasonic (input) transducer and acoustic emission (output) sensor is such that the resultant waveform carries an imprint of morphological factors that govern or contribute to material performance. The output waveform is quite complex, but it can be quantitized in terms of a 'stress wave factor'. The stress wave factor, which can be defined in a number of ways, is essentially a relative measure of the efficiency of energy dissipation in a material. If flaws or other material anomalies exist in the volume being examined, their combined effect will appear in the stress wave factor.

Vary, A.↗

Acousto-ultrasonic characterization of fiber reinforced composites

The advantageous aspects of acoustic emission and ultrasonic methodologies are combined in a technique which operates by introducing a repeating series of ultrasonic pulses into a material. The waves introduced simulate the spontaneous stress waves that would arise if the material were put under stress as in the case of acoustic emission measurements. These benign stress waves are detected by an acoustic emission sensor. The physical arrangement of the ultrasonic (input) transducer and acoustic emission (output) sensor is such that the resultant waveform carries an imprint of morphological factors that govern or contribute to material performance. The output waveform is quite complex, but it can be quantitized in terms of a "stress wave factor". The stress wave factor, which can be defined in a number of ways, is essentially a relative measure of the efficiency of energy dissipation in a material. If flaws or other material anomalies exist in the volume being examined, their combined effect will appear in the stress wave factor.

Vary, A.↗

Microstructural Evolution and Mechanical Properties of LP-DED NASA HR-1 – A Hydrogen Resistant AM Superalloy for Space Propulsion Applications

The National Aeronautics and Space Administration (NASA) has actively pursued metal additive manufacturing (AM) technologies for spaceflight applications since the late 2000s. AM offers transformative advantages in cost, schedule, part consolidation, and design flexibility. Among the various AM techniques, laser powder directed energy deposition (LP-DED) is particularly well suited for fabricating complex geometries with fine feature resolution. In propulsion systems that utilize high-pressure gaseous hydrogen—such as liquid hydrogen rocket engines—hydrogen environment embrittlement (HEE) presents a serious threat to material performance 1,2 . Mechanical property degradation under these conditions can compromise component reliability, especially under cyclic loading. To address this challenge, NASA developed NASA HR-1 (Hydrogen Resistant-1) as a solution for liquid rocket engine components operating in hydrogen-rich environments, using the LP-DED technique 3-9 . A key component in a liquid rocket engine is the exhaust nozzle, which is typically regeneratively cooled (regen) due to the high heat flux. NASA HR-1 was specifically developed for regen nozzle applications using hydrogen as a propellant, providing resistance to HEE, a critical issue for many materials. The AM version of NASA HR-1 was also formulated to achieve high ultimate tensile strength, along with high yield strength and ductility in this environment 5,6 . Low-cycle fatigue (LCF) is another important consideration in nozzle design, as components are expected to endure multiple starts and missions. Additionally, the LP-DED version of the alloy exhibits improved thermal conductivity compared to its wrought counterpart, which benefits nozzle cooling. Overall, NASA HR-1 offers an excellent balance of high strength, HEE resistance, LCF performance, thermal conductivity, and ductility to meet the demanding requirements of channel-cooled nozzles and other components used with hydrogen and other propellants. The LP-DED–processed NASA HR-1 requires several post-processing heat treatment steps to achieve the material properties desirable for its intended application 6 . These steps include stress relief, homogenization, solution annealing, and aging for precipitation hardening. The stress relief treatment mitigates residual stresses accumulated during the LP-DED process and minimizes the potential for distortion. Homogenization, a common step for AM materials, reduces elemental segregation and promotes recrystallization to develop a more equiaxed grain structure. The subsequent solution annealing treatment heats the part to a solid solution temperature to dissolve the undesirable η-phase that forms during cooling from homogenization, followed by rapid cooling to retain an η-phase–free microstructure. Finally, aging promotes precipitation of the strengthening γ′ phase in the alloy. The integration of compositional design and optimized thermal processing enables high-quality LP-DED NASA HR-1 components with excellent microstructural and mechanical stability. Improved chemical and microstructure homogeneity enhances ductility and fatigue resistance—both critical for safe and reliable operation in high-pressure hydrogen environments. NASA has successfully fabricated and hot-fire tested multiple subscale and full-scale channel wall nozzles using LP-DED NASA HR-1 5,6, 9-14 . These efforts included process refinements to support thin-wall construction and various channel geometries. Throughout development, several key observations emerged. After homogenization, the as-built columnar grain structure transforms into a fully equiaxed microstructure. However, subsequent treatments—such as solution annealing and aging—result in changes that are more difficult to track. The grain structure remains largely unchanged, and the γ′ precipitates, typically 5–10 nm in diameter, are beyond the resolution of scanning electron microscopy (SEM). While transmission electron microscopy (TEM) can resolve these fine precipitates, TEM sample preparation is time-consuming and difficult for LP-DED material. As an alternative, differential scanning calorimetry (DSC) offers a useful, qualitative approach to monitor precipitate evolution throughout different stages of heat treatment. The overall goal is to improve the understanding of how heat treatment affects the microstructure and mechanical performance of LP-DED NASA HR-1. This paper presents heat treatment design considerations, microstructural characterization, mechanical testing – including tensile and LCF testing in both air and hydrogen environments.

Superalloy↗

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗

Material Compatibility Testing of Green Hydrazine

Green Hydrazine Propellant Blend (GHPB) is a new green propellant developed by Aerojet Rocketdyne and selected for continued testing at NASA’s Goddard Space Flight Center (GSFC). It primarily differs from previous "green” propellants by maintaining traditional hydrazine as the base constituent, along with additives that are intended to increase handling safety by lowering the concentration of hydrazine in the vapor. GSFC has performed material compatibility testing. The scope of this campaign included materials that are common in spacecraft or ground support equipment (GSE), and also included typical safety materials (e.g. permeation rates through gloves). The conclusion in a broad sense is that GHPB is less reactive than hydrazine, including the absorption of metals into the propellant, as examined by Inductively-Coupled Plasma (ICP) analysis of metals uptake.

Eric H Cardiff↗

Damage Mechanisms and Controlled Crack Propagation in a Hot Pressed Silicon Nitride Ceramic

The subcritical growth of cracks from pre-existing flaws in ceramics can severely affect the structural reliability of a material. The ability to directly observe subcritical crack growth and rigorously analyze its influence on fracture behavior is important for an accurate assessment of material performance. A Mode I fracture specimen and loading method has been developed which permits the observation of stable, subcritical crack extension in monolithic and toughened ceramics. The test specimen and procedure has demonstrated its ability to generate and stably propagate sharp, through-thickness cracks in brittle high modulus materials. Crack growth for an aluminum oxide ceramic was observed to be continuously stable throughout testing. Conversely, the fracture behavior of a silicon nitride ceramic exhibited crack growth as a series of subcritical extensions which are interrupted by dynamic propagation. Dynamic initiation and arrest fracture resistance measurements for the silicon nitride averaged 67 and 48 J/sq m, respectively. The dynamic initiation event was observed to be sudden and explosive. Increments of subcritical crack growth contributed to a 40 percent increase in fracture resistance before dynamic initiation. Subcritical crack growth visibly marked the fracture surface with an increase in surface roughness. Increments of subcritical crack growth loosen ceramic material near the fracture surface and the fracture debris is easily removed by a replication technique. Fracture debris is viewed as evidence that both crack bridging and subsurface microcracking may be some of the mechanisms contributing to the increase in fracture resistance. A Statistical Fracture Mechanics model specifically developed to address subcritical crack growth and fracture reliability is used together with a damaged zone of material at the crack tip to model experimental results. A Monte Carlo simulation of the actual experiments was used to establish a set of modeling input parameters. It was demonstrated that a single critical parameter does not characterize the conditions required for dynamic initiation. Experimental measurements for critical crack lengths, and the energy release rates exhibit significant scatter. The resulting output of the model produces good agreement with both the average values and scatter of experimental measurements.

Calomino, Anthony Martin↗

Cryogenic Moisture Apparatus

The Cryogenic Moisture Apparatus (CMA) is designed for quantifying the amount of moisture from the surrounding air that is taken up by cryogenic-tank-insulating material specimens while under typical conditions of use. More specifically, the CMA holds one face of the specimen at a desired low temperature (e.g., the typical liquid-nitrogen temperature of 77 K) while the opposite face remains exposed to humid air at ambient or near-ambient temperature. The specimen is weighed before and after exposure in the CMA. The difference between the "after" and "before" weights is determined to be the weight of moisture absorbed by the specimen. Notwithstanding the term "cryogenic," the CMA is not limited to cryogenic applications: the low test temperature can be any temperature below ambient, and the specimen can be made of any material affected by moisture in air. The CMA is especially well suited for testing a variety of foam insulating materials, including those on the space-shuttle external cryogenic tanks, on other cryogenic vessels, and in refrigerators used for transporting foods, medicines, and other perishables. Testing is important because absorbed moisture not only adds weight but also, in combination with thermal cycling, can contribute to damage that degrades insulating performance. Materials are changed internally when subjected to large sub-ambient temperature gradients.

Fesmire, James↗