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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Global Response of the Space Shuttle External Tank with the Presence of Intertank Stringer Cracks and Radius Blocks

After propellant was loaded into the external tank (ET), the November 5, 2010 launch of Space Shuttle mission STS-133 was scrubbed due to a gaseous hydrogen leak located in a vent line near the ground umbilical and ET connection. Subsequent visual inspections identified cracks in the sprayed-on foam insulation in the forward end of the ET intertank segment, adjacent to the liquid oxygen (LOX) tank, as shown in Figure 1. These cracks necessitated repair of the foam due to debris concerns that violated launch constraints. As part of the repair process, the affected foam was removed to reveal cracks in the underlying external hat stiffeners on the intertank, as shown in Figure 2. Ultimately, five stiffeners were discovered to be cracked adjacent to the LOX tank. As the managing center for the ET Project, NASA Marshall Space Flight Center (MSFC) coordinated failure investigation and repair activities among multiple organizations, which included the ET prime contractor (Lockheed Martin Space Systems Michoud Operations), the Space Shuttle Program Office at the NASA Johnson Space Center (JSC), the NASA Kennedy Space Center (KSC), and the NASA Engineering and Safety Center (NESC). STS-133 utilized the external tank designated as ET-137. Many aspects of the investigation have been reported previously in Refs. 1-7, which focus on the root cause of the failures, the flight readiness rationale and the local analyses of the stringer failures and repair. This paper summarizes the global analyses that were conducted on ET-137 as part of the NESC effort during the investigation, which was conducted primarily to determine if the repairs that were introduced to the stringers would alter the global response of the ET. In the process of the investigation, a new STAGS tabular input capability was developed to more easily introduce the aerodynamic pressure loads using a method that could easily be extended to incorporate finite element property data such as skin and stiffener thicknesses and beam cross-sectional properties.

Lovejoy, Andrew E.↗
NASA NTRS2008

Hail Disrometer Array for Launch Systems Support

Prior to launch, the space shuttle might be described as a very large thermos bottle containing substantial quantities of cryogenic fuels. Because thermal insulation is a critical design requirement, the external wall of the launch vehicle fuel tank is covered with an insulating foam layer. This foam is fragile and can be damaged by very minor impacts, such as that from small- to medium-size hail, which may go unnoticed. In May 1999, hail damage to the top of the External Tank (ET) of STS-96 required a rollback from the launch pad to the Vehicle Assembly Building (VAB) for repair of the insulating foam. Because of the potential for hail damage to the ET while exposed to the weather, a vigilant hail sentry system using impact transducers was developed as a hail damage warning system and to record and quantify hail events. The Kennedy Space Center (KSC) Hail Monitor System, a joint effort of the NASA and University Affiliated Spaceport Technology Development Contract (USTDC) Physics Labs, was first deployed for operational testing in the fall of 2006. Volunteers from the Community Collaborative Rain. Hail, and Snow Network (CoCoRaHS) in conjunction with Colorado State University were and continue to be active in testing duplicate hail monitor systems at sites in the hail prone high plains of Colorado. The KSC Hail Monitor System (HMS), consisting of three stations positioned approximately 500 ft from the launch pad and forming an approximate equilateral triangle (see Figure 1), was deployed to Pad 39B for support of STS-115. Two months later, the HMS was deployed to Pad 39A for support of STS-116. During support of STS-117 in late February 2007, an unusual hail event occurred in the immediate vicinity of the exposed space shuttle and launch pad. Hail data of this event was collected by the HMS and analyzed. Support of STS-118 revealed another important application of the hail monitor system. Ground Instrumentation personnel check the hail monitors daily when a vehicle is on the launch pad, with special attention after any storm suspected of containing hail. If no hail is recorded by the HMS, the vehicle and pad inspection team has no need to conduct a thorough inspection of the vehicle immediately following a storm. On the afternoon of July 13, 2007, hail on the ground was reported by observers at the VAB, about three miles west of Pad 39A, as well as at several other locations around Kennedy Space Center. The HMS showed no impact detections, indicating that the shuttle had not been damaged by any of the numerous hail events which occurred that day.

Lane, John E.↗
NASA NTRS2013

Conducting nanotubes or nanostructures based composites, method of making them and applications

An electromagnetic interference (EMI) shielding material includes a matrix of a dielectric or partially conducting polymer, such as foamed polystyrene, with carbon nanotubes or other nanostructures dispersed therein in sufficient concentration to make the material electrically conducting. The composite is formed by dispersing the nanotube material in a solvent in which the dielectric or partially conducting polymer is soluble and mixing the resulting suspension with the dielectric or partially conducting polymer. A foaming agent can be added to produce a lightweight foamed material. An organometallic compound can be added to enhance the conductivity further by decomposition into a metal phase.

Gupta, Mool C.↗
NASA NTRS2016

After Math - Foamology and Flight Rationale

The Space Shuttle was developed by NASA to be a largely reusable launch system which could provide frequent access to low earth orbit. Like all previous launch systems, safe reentry for the crew and payload required the use of a thermal protection system (TPS). Unlike previous spacecraft though, the Shuttle's TPS was exposed from launch, making it sensitive to debris which could be generated by the vehicle on ascent. The most likely and potentially destructive source of debris was considered to be ice, which could build-up anywhere on the External Tank (ET) where there was exposed metal. Ice could form during ground operations after the cryogenic propellants had been loaded and then be knocked loose on ascent. In order to prevent both ice build-up and boil-off of the propellants, the entire ET and all protuberances (orbiter attach points, pressurization lines, propellant feed lines, etc.) were covered with a spray on foam insulation (SOFI) type TPS. Unfortunately the foam was also susceptible to liberation during ascent, and posed a debris risk of its own. During the early years of the Shuttle Program engineers spent a good deal of effort characterizing the amount of foam that was shed.

Steva, Thomas↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2024

Development of Weighout Process and Evaluations for Underwater Partial Gravity EVA Simulations

For the upcoming Artemis lunar missions, astronauts will needto train in a spacesuit where partial gravity can be simulated such as NASA Neutral Buoyancy Lab (NBL). At the NBL, dive weights and foam can be added around the spacesuit to attaina satisfactory center of buoyancy (CB) and center of gravity (CG) location to simulate the lunar gravity (1/6thG) effects. If CG and CB are not co-located properly, incorrect righting moments can be introduced, and both simulation quality and EVA task performance can be impaired. Based on the findings from the initial testingusing xEMU spacesuits, it was observed that the weigh-out method (i.e., determination of the weights and foam quantities and position) needed further development to improve the simulation quality, especially for the subjects who experienced excessive instability. This paper aims to present the on-going effort to improve the weigh-out process for enabling NBL lunar EVA simulations. For this effort, a human-suit model was created to use suit CAD and 3D human body scansto estimate both CB and CG location for each suited subject. NBL weigh-out testing was performed to characterize the effects of CG and CB positioning, in which the 3D human-suit model was used to determine optimal weigh-out combinations of weights and foam. Postural, balance, and subjective feedback were gathered for each weigh-out configuration. The results indicated that, as the CB was shifted higher and the CB and CG located closer to each other, the subject tended to be more stable and their EVA performance improved. A high CB location was then prioritized across 4 additional subjects in both small and large size spacesuits. When compared to the initial xEMU test series, improved performance was observed across all subjects as the CB moved higher and aligned closer to the system CG.

Seyed Pouyan Sabahi↗
NASA NTRS2024

Development of Weigh-out Process and Evaluations for Underwater Partial Gravity Simulations

For the upcoming Artemis lunar missions, astronauts will need to train in a spacesuit where partial gravity can be simulated such as the NASA Neutral Buoyancy Lab (NBL). At the NBL, dive weights and foam can be added around the spacesuit to attain a satisfactory center of buoyancy (CB) and center of gravity (CG) location to simulate the lunar gravity (1/6thG) effects. If CG and CB are not co-located properly, incorrect righting moments can be introduced, and both simulation quality and EVA task performance can be impaired. Based on the findings from the initial testing using xEMU spacesuits, it was observed that the weigh-out method (i.e., determination of the weights and foam quantities and position) needed further development to improve the simulation quality, especially for the subjects who experienced excessive instability. This paper aims to present the on-going effort to improve the weigh-out process for enabling NBL lunar EVA simulations. For this effort, a human-suit model was created to use suit CAD and 3D human body scans to estimate both CB and CG location for each suited subject. NBL weigh-out testing was performed to characterize the effects of CG and CB positioning, in which the 3D human-suit model was used to determine optimal weigh-out combinations of weights and foam. Postural, balance, and subjective feedback were gathered for each weigh-out configuration. The results indicated that, as the CB was shifted higher and the CB and CG located closer to each other, the subject tended to be more stable and their EVA performance improved. A high CB location was then prioritized across 4 additional subjects in both small and large size spacesuits. When compared to the initial xEMU test series, improved performance was observed across all subjects as the CB moved higher and aligned closer to the system CG.

Pouyan Sabahi↗
NASA NTRS2024

Development of Weigh-out Process and Evaluations for Underwater Partial Gravity Simulations

For the upcoming Artemis lunar missions, astronauts will need to train in a spacesuit where partial gravity can be simulated such as the NASA Neutral Buoyancy Lab (NBL). At the NBL, dive weights and foam can be added around the spacesuit to attain a satisfactory center of buoyancy (CB) and center of gravity (CG) location to simulate the lunar gravity (1/6thG) effects. If CG and CB are not co-located properly, incorrect righting moments can be introduced, and both simulation quality and EVA task performance can be impaired. Based on the findings from the initial testing using xEMU spacesuits, it was observed that the weigh-out method (i.e., determination of the weights and foam quantities and position) needed further development to improve the simulation quality, especially for the subjects who experienced excessive instability. This paper aims to present the on-going effort to improve the weigh-out process for enabling NBL lunar EVA simulations. For this effort, a human-suit model was created to use suit CAD and 3D human body scans to estimate both CB and CG location for each suited subject. NBL weigh-out testing was performed to characterize the effects of CG and CB positioning, in which the 3D human-suit model was used to determine optimal weigh-out combinations of weights and foam. Postural, balance, and subjective feedback were gathered for each weigh-out configuration. The results indicated that, as the CB was shifted higher and the CB and CG located closer to each other, the subject tended to be more stable and their EVA performance improved. A high CB location was then prioritized across 4 additional subjects in both small and large size spacesuits. When compared to the initial xEMU test series, improved performance was observed across all subjects as the CB moved higher and aligned closer to the system CG.

Pouyan Sabahi↗
NASA NTRSDate not supplied

Development of Weigh-Out Process and Evaluations for Underwater Partial Gravity Simulations

For the upcoming Artemis lunar missions, astronauts will need to train in a spacesuit where partial gravity can be simulated such as the NASA Neutral Buoyancy Lab (NBL). At the NBL, dive weights and foam can be added around the spacesuit to attain a satisfactory center of buoyancy (CB) and center of gravity (CG) location to simulate the lunar gravity (1/6thG) effects. If CG and CB are not co-located properly, incorrect righting moments can be introduced, and both simulation quality and EVA task performance can be impaired. Based on the findings from the initial testing using xEMU spacesuits, it was observed that the weigh-out method (i.e., determination of the weights and foam quantities and position) needed further development to improve the simulation quality, especially for the subjects who experienced excessive instability. This paper aims to present the on-going effort to improve the weigh-out process for enabling NBL lunar EVA simulations. For this effort, a human-suit model was created to use suit CAD and 3D human body scans to estimate both CB and CG location for each suited subject. NBL weigh-out testing was performed to characterize the effects of CG and CB positioning, in which the 3D human-suit model was used to determine optimal weigh-out combinations of weights and foam. Postural, balance, and subjective feedback were gathered for each weigh-out configuration. The results indicated that, as the CB was shifted higher and the CB and CG located closer to each other, the subject tended to be more stable and their EVA performance improved. A high CB location was then prioritized across 4 additional subjects in both small and large size spacesuits. When compared to the initial xEMU test series, improved performance was observed across all subjects as the CB moved higher and aligned closer to the system CG.

Pouyan Sabahi↗
NASA NTRSDate not supplied

In-Situ Heat Rejection to the Martian Atmosphere

In order to provide in-situ heat rejection to the thin Martian atmosphere, approaches relying on forced convection and buoyancy flow are conceptualized. A convective radiator concept utilizing a fan to draw local atmosphere through an expanded metal foam heat exchanger panel is developed. To provide a pumped fluid loop interface, flow tubes are embedded in the heat exchanger panel and heat is transferred from the tubes to the foam via conduction and subsequently convectively to the environment. To forgo the use of a specialized high throughput fan, a concept utilizing a buoyant chimney tower to draw atmospheric flow through a low pressure drop heat exchanger is also considered. Both concepts may best align with moderate- to high-temperature heat loads that provide a significant delta temperature relative to the ambient atmosphere temperature. While eliminating dust accumulation on radiator surfaces, some type of serviceable filtration or screening may be required to prevent accumulation of dust inside the expanded metal foam heat exchanger for both concepts.

Heat Rejection↗
NASA NTRS1950

Rise of Air Bubbles in Aircraft Lubricating Oils

Lubricating and antifoaming additives in aircraft lubricating oils may impede the escape of small bubbles from the oil by forming shells of liquid with a quasi-solid or gel structure around the bubbles. The rates of rise of small air bubbles, up to 2 millimeters in diameter, were measured at room temperature in an undoped oil, in the same oil containing foam inhibitors, and in an oil containing lubricating additives. The apparent diameter of the air bubbles was measured visually through an ocular micrometer on a traveling telescope. The bubbles in the undoped oil obeyed Stokes' Law, the rate of rise being proportional to the square of the apparent diameter and inversely proportional to the viscosity of the oil. The bubbles in the oils containing lubricating additives or foam inhibitors rose more slowly than the rate predicted by Stokes 1 Law from the apparent diameter, and the rate of rise decreased as the length of path the bubbles traveled increased. A method is derived to calculate the thickness of the liquid shell which would have to move with the bubbles in the doped oils to account for the abnoi'I!l8.lly slow velocity. The maximum thickness of this shell, calculated from the velocities observed, was equal to the bubble radius.

LUBRICATING OILS - FOAMING↗
NASA NTRS1966

Storage-stable foamable polyurethane is activated by heat

Polyurethane foamable mixture remains inert in storage unit activated to produce a rapid foaming reaction. The storage-stable foamable composition is spread as a paste on the surface of an expandable structure and, when heated, yields a rigid open-cell polyurethane foam that is self-bondable to the substrate.

Source record↗
NASA NTRS1966

Process produces chlorinated aromatic isocyanate in high yield

Tetrachloreterephthaloyl chloride reacts with sodium azide in an atmosphere of nitrogen to form a high yield of tetrachloro-p-phenylene diisocyanate. The chlorinated diisocyanate should have application as an intermediate in the preparation of polyurethane foams. The high halogen content would impart added flame resistance to these foams.

Trischler, F.↗
NASA NTRS1972

Deflection resistance indicator

Instrument for nondestructively measuring compression resistance of spray foam used as thermal insulation was developed. Cylindrical indicator has probe with coil spring to provide force and indicating dial. Probe is manually pressed against area to be tested until complete foot area contacts foam surfaces.

Massey, H. S.↗
NASA NTRS1972

Microwave characteristics of the ocean surface in the 1-10 GHz band

The microwave characteristics of calm, rough, and foam-covered ocean surfaces were studied, and a technique was developed for deriving thermodynamic ocean surface temperatures from brightness temperatures measured by an earth-orbiting radiometer. This investigation encompassed frequencies in the range 1 to 10 GHz (wavelength range of 30 to 3 cm) and was based on the use of a one-dimensional geometrical optics roughness model, including shadowing and multiple scattering of radiant electromagnetic energy. Provision is made in the model for characterizing surface roughness through the rms slope versus wind velocity relations previously established. Suitable foam and atmospheric models were superimposed on the roughness model.

Strong, A. E.↗
NASA NTRS1971

Heat shields for aircraft - A new concept to save lives in crash fires.

A passenger compartment surrounded by a fire-retardant shell, to protect the occupants long enough for the fire to burn out or for fire-fighting equipment to reach the aircraft and extinguish it, is proposed as a new concept for saving lives in crash fires. This concept is made possible by the recent development of two new fire-retardant materials: a very lightweight foam plastic, called polyisocyanurate foam, and an intumescent paint. Exposed to heat, the intumescent paint expands to many times its original thickness and insulates the surface underneath it. Demonstration tests are illustrated, described and discussed. However, some problems, such as preventing fuselage rupture and protecting windows, must be solved before such a system can be used.

Neel, C. B.↗
NASA NTRS1973

Development and Characterization of CPI Surface Insulation

A new type of reusable surface material that could find application as a component of the thermal protection system of the space shuttle orbiter is discussed. These materials consist of 20-30% dense closed cell high emittance glass ceramic foams formulated by sintering an intimate mixture of fly ash cenospheres with 4-12 wt % cobalt oxide. These unique ceramic foams exhibit: (1) High mechanical strengths; (2) no need for waterproof coating due to the non-interconnecting cell network; (3) high emissivities; (4) ability to withstand space shuttle thermal environments with no loss of reuse capability; and (5) a machinable ceramic with capability of maintaining the close tolerances required of an integrated heat shield system.

Tobin, A.↗