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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2013

The NASA Fireball Network

In the summer of 2008, the NASA Meteoroid Environments Office (MEO) began to establish a video fireball network, based on the following objectives: (1) determine the speed distribution of cm size meteoroids, (2) determine the major sources of cm size meteoroids (showers/sporadic sources), (3) characterize meteor showers (numbers, magnitudes, trajectories, orbits), (4) determine the size at which showers dominate the meteor flux, (5) discriminate between re-entering space debris and meteors, and 6) locate meteorite falls. In order to achieve the above with the limited resources available to the MEO, it was necessary that the network function almost fully autonomously, with very little required from humans in the areas of upkeep or analysis. With this in mind, the camera design and, most importantly, the ASGARD meteor detection software were adopted from the University of Western Ontario's Southern Ontario Meteor Network (SOMN), as NASA has a cooperative agreement with Western's Meteor Physics Group. 15 cameras have been built, and the network now consists of 8 operational cameras, with at least 4 more slated for deployment in calendar year 2013. The goal is to have 15 systems, distributed in two or more groups east of automatic analysis; every morning, this server also automatically generates an email and a web page (http://fireballs.ndc.nasa.gov) containing an automated analysis of the previous night's events. This analysis provides the following for each meteor: UTC date and time, speed, start and end locations (longitude, latitude, altitude), radiant, shower identification, light curve (meteor absolute magnitude as a function of time), photometric mass, orbital elements, and Tisserand parameter. Radiant/orbital plots and various histograms (number versus speed, time, etc) are also produced. After more than four years of operation, over 5,000 multi-station fireballs have been observed, 3 of which potentially dropped meteorites. A database containing data on all these events, including the videos and calibration information, has been developed and is being modified to include data from the SOMN and other camera networks.

Cooke, William J.↗
NASA NTRS2014

Automated Optical Meteor Fluxes and Preliminary Results of Major Showers

NASA's Meteoroid Environment Office (MEO) recently established a two‐station system to calculate daily automated meteor fluxes in the millimeter‐size‐range for both single‐station and double‐station meteors. The cameras each consist of a 17 mm focal length Schneider lens (f/0.95) on a Watec 902H2 Ultimate CCD video camera, producing a 21.7x15.5 degree field of view. This configuration sees meteors down to a magnitude of +6. This paper outlines the concepts of the system, the hardware and software, and results of 3,000+ orbits from the first 18 months of operations. Video from the cameras are run through ASGARD (All Sky and Guided Automatic Real‐time Detection), which performs the meteor detection/photometry, and invokes MILIG and MORB (Borovicka 1990) codes to determine the trajectory, speed, and orbit of the meteor. A subroutine in ASGARD allows for approximate shower identification in single‐station detections. The ASGARD output is used in routines to calculate the flux. Before a flux can be calculated, a weather algorithm indicates if sky conditions are clear enough to calculate fluxes, at which point a limiting magnitude algorithm is employed. The limiting stellar magnitude is found using astrometry.net (Lang et al. 2012) to identify stars and translated to the corresponding shower and sporadic limiting meteor magnitude. It is found every 10 minutes and is able to react to quickly changing sky conditions. The extensive testing of these results on the Geminids and Eta Aquariids is shown. The flux involves dividing the number of meteors by the collecting area of the system, over the time interval for which that collecting area is valid. The flux algorithm employed here differs from others currently in use in that it does not make the gross oversimplication of choosing a single height to calculate the collection area of the system. In the MEO system, the volume is broken up into a set of height intervals, with the collecting areas determined by the position of the active shower or sporadic source radiant. The flux per height interval is calculated and summed to obtain the total meteor flux. Both single station and double station fluxes are currently found daily. Geminid fluxes on the peak night in 2012 (12‐14‐2012) were 0.058 meteors/km2/hr as found with double‐station meteors and 0.057 meteors/ km2/hr as found with single‐station meteors, to a limiting magnitude of +6.5. Both of those numbers are in agreement with the well‐calibrated fluxes from the Canadian Meteor Orbit Radar. Along with flux algorithms and initial flux results, presented will be results from the first 18 months of operation, covering 3,000+ meteoroid orbits.

Blaauw, R.↗
NASA NTRSDate not supplied

Stochastic Reconstruction of Thermal Protection Material Properties from Arc-Jet Experiments

Material response models are used to assess reliability using variances in the bond-line temperature predictions based on uncertainties in trajectory, aerothermal environment, and material properties. A key deficiency in the current approach is that input uncertainties are too often subjective, empirical, or ad-hoc, and are not rigorously linked to the arc-jet test data used to develop the TPS material model. While materials such as PICA are well understood, future missions may require more novel materials such as HEEET where unknown uncertainties have real consequences on the ability to assess reliability. A quantifiable estimate of reliability requires an iterative methodology where the parameters driving the variance in bond-line temperature (for example) are systematically identified. A test campaign to collect data or develop new models can then be identified to reduce those input uncertainties. A Bayesian inference loop defines these connections mathematically, i.e., prior knowledge about uncertainty is updated based on observation. While these concepts are well known (and often applied intuitively in a non-rigorous approach), only recent advances in reduced-order modelling have made them computationally viable methods for engineering. By replacing deterministic inverse methods with stochastic approaches, the hope is new materials proposed for future missions can more rapidly be developed with a greater understanding of the TPS material reliability. Two additional steps for the analysis of arc jet test data are discussed. The first is ability to construct a reduced-order model using material response simulations (Icarus/US3D) of the arc-jet test articles, and the second is the inclusion of this surrogate model in the Bayesian inversion process. Both capabilities will be demonstrated using prior PICA arc-jet test data. The quality of a surrogate model will be investigated and the variances on the calibrated material properties will be compared to our current understanding of the PICA material model.

Material response↗
NASA NTRS2011

Orbit Determination of Spacecraft in Earth-Moon L1 and L2 Libration Point Orbits

The ARTEMIS mission, part of the THEMIS extended mission, is the first to fly spacecraft in the Earth-Moon Lissajous regions. In 2009, two of the five THEMIS spacecraft were redeployed from Earth-centered orbits to arrive in Earth-Moon Lissajous orbits in late 2010. Starting in August 2010, the ARTEMIS P1 spacecraft executed numerous stationkeeping maneuvers, initially maintaining a lunar L2 Lissajous orbit before transitioning into a lunar L1 orbit. The ARTEMIS P2 spacecraft entered a L1 Lissajous orbit in October 2010. In April 2011, both ARTEMIS spacecraft will suspend Lissajous stationkeeping and will be maneuvered into lunar orbits. The success of the ARTEMIS mission has allowed the science team to gather unprecedented magnetospheric measurements in the lunar Lissajous regions. In order to effectively perform lunar Lissajous stationkeeping maneuvers, the ARTEMIS operations team has provided orbit determination solutions with typical accuracies on the order of 0.1 km in position and 0.1 cm/s in velocity. The ARTEMIS team utilizes the Goddard Trajectory Determination System (GTDS), using a batch least squares method, to process range and Doppler tracking measurements from the NASA Deep Space Network (DSN), Berkeley Ground Station (BGS), Merritt Island (MILA) station, and United Space Network (USN). The team has also investigated processing of the same tracking data measurements using the Orbit Determination Tool Kit (ODTK) software, which uses an extended Kalman filter and recursive smoother to estimate the orbit. The orbit determination results from each of these methods will be presented and we will discuss the advantages and disadvantages associated with using each method in the lunar Lissajous regions. Orbit determination accuracy is dependent on both the quality and quantity of tracking measurements, fidelity of the orbit force models, and the estimation techniques used. Prior to Lissajous operations, the team determined the appropriate quantity of tracking measurements that would be needed to meet the required orbit determination accuracies. Analysts used the Orbit Determination Error Analysis System (ODEAS) to perform covariance analyses using various tracking data schedules. From this analysis, it was determined that 3.5 hours of DSN TRK-2-34 range and Doppler tracking data every other day would suffice to meet the predictive orbit knowledge accuracies in the Lissajous region. The results of this analysis are presented. Both GTDS and ODTK have high-fidelity environmental orbit force models that allow for very accurate orbit estimation in the lunar Lissajous regime. These models include solar radiation pressure, Earth and Moon gravity models, third body gravitational effects from the Sun, and to a lesser extent third body gravitational effects from Jupiter, Venus, Saturn, and Mars. Increased position and velocity uncertainties following each maneuver, due to small execution performance errors, requires that several days of post-maneuver tracking data be processed to converge on an accurate post-maneuver orbit solution. The effects of maneuvers on orbit determination accuracy will be presented, including a comparison of the batch least squares technique to the extended Kalman filter/smoother technique. We will present the maneuver calibration results derived from processing post-maneuver tracking data. A dominant error in the orbit estimation process is the uncertainty in solar radiation pressure and the resultant force on the spacecraft. An estimation of this value can include many related factors, such as the uncertainty in spacecraft reflectivity and surface area which is a function of spacecraft orientation (spin-axis attitude), uncertainty in spacecraft wet mass, and potential seasonal variability due to the changing direction of the Sun line relative to the Earth-Moon Lissajous reference frame. In addition, each spacecraft occasionally enters into Earth or Moon penumbra or umbra and these shadow crossings reduche solar radiation force for several hours. The effects of these events on orbit determination accuracy will be presented. In order to plan for upcoming stationkeeping maneuvers, the maneuver planning team must take the current orbit estimate, propagate it forward to the planned maneuver time, and determine the optimal maneuver to maintain the Lissajous orbit for one or more revolutions. The propagation is performed using a Runge-Kutta 7/8 integrator and typically the position and velocity uncertainty increases with propagation time, increasing the overall uncertainty of the orbit state at the maneuver execution time. The effect of orbit knowledge uncertainty on stationkeeping operations will be presented.

Woodard, Mark↗
NASA NTRS2022

Predicting the Penetration of a Shielded TPS Tile

All spacecraft are subject to the possibility of high-speed particle impacts during their mission life. In low earth orbit, those impacts could be the result of collisions with pieces of orbital debris or with meteorites. Beyond LEO, and especially beyond GEO, those impacts will likely be caused by meteorites. Such high-speed impacts on spacecraft surfaces create debris clouds that travel towards and eventually impact other downstream spacecraft components. In addition to the impulsive load that such debris clouds would impart to the spacecraft elements with which they subsequently collide, the largest fragment in these debris clouds poses a significant threat on its own to those spacecraft elements. In order to be able to assess the severity of the threat posed by such a fragment, it is important to be able to predict the extent of damage sustained by the impacted spacecraft element. In this paper, we present a new process for determining the penetration depth in a shielded TPS tile system. The requirements for the process were that it be applicable across a full spectrum of impact low velocities (i.e. from ~ 7 km/s to above 50 km/s), that it be applicable over a wide range of projectile materials densities (i.e. ranging from water to steel, for example), and that it be applicable over a wide range of trajectory obliquities (i.e. not just normal impacts). Furthermore, the process developed should be sufficiently flexible so that it can be used for shield design parameter and / or system configuration trade studies (i.e. be written in terms of as many shield and TPS tile system parameters as possible). Following the initial impact of the projectile on the outer wall of the dual-wall shield, a debris cloud (i.e. the primary debris cloud) is created that travels towards and impacts the inner wall of the shield. Within this debris cloud is a combination of solid, liquid, and vaporized material, depending on the impact velocity and the impedance mismatch of the projectile and outer wall materials. The impact of this debris cloud on the inner wall creates another debris cloud (i.e. the secondary debris cloud) that then travels towards and impacts the TPS tile. Within the particulate distribution of the material in each of these debris clouds is a so-called largest fragment. The process used to calculate the penetration depth in a shielded TPS tile system developed herein consists of the following three steps: 1. Does the initial projectile perforate the outer wall of the shield? If NO, the TPS tile remains undamaged. If YES, calculate the size and speed of the largest fragment exiting the outer wall of the shield and traveling towards the shield’s inner wall. 2. Does the largest fragment in the debris cloud exiting the outer wall of the shield perforate the inner wall of the shield? If NO, the TPS tile again remains undamaged. If YES, calculate the size and speed of the largest fragment exiting the rear of the shield inner wall. 3. Calculate the penetration depth of the largest fragment in the debris cloud exiting the inner wall into the TPS. Perforation of the inner and outer shield walls is predicted using the Cour-Palais penetration depth equation and an associated failure criterion [1]. The largest debris cloud fragment diameters and associated velocities are calculated using regression equations developed from a mix of hydrocode and experimental diameter and velocity information [2]. Finally, TPS penetration depths are calculated using an empirical penetration depth predictor equation based on high-speed impact test data and are calibrated using a limited number of hydrocode penetration depth predictions for water, nylon, aluminum, Dunite, and steel projectiles The predictions of the penetration depth calculation process are compared against the predictions of more than 60 SPHC hydrocode runs. These comparisons show that the process used to calculate TPS tile penetration depths usually yields values that are within 1 mm of the predictions of hydrocode simulations. If a maximum allowable TPS penetration depth is known, the process developed herein can now be used to develop a failure limit equation that would predict whether or not an impacting particle would penetrate deeply enough into the TPS to result in a “failed” or “not failed” end state. References [1] E.L. Christiansen, “Design and performance equations for advanced meteoroid and debris shields”, International Journal of Impact Engineering, Vol. 14, 1993, pp. 145-156. [2] W.P. Schonberg, “Predicting the size of the largest particle fragment in a debris cloud created by an orbital debris impact and its associated velocity”, 2021 Applied Space Environments Conference, NASA Jet Propulsion Laboratory, Pasadena, California, November, 2021.

Meteroid↗
NASA NTRS2010

Cast Glance Near Infrared Imaging Observations of the Space Shuttle During Hypersonic Re-Entry

High resolution calibrated infrared imagery of the Space Shuttle was obtained during hypervelocity atmospheric entries of the STS-119, STS-125 and STS128 missions and has provided information on the distribution of surface temperature and the state of the airflow over the windward surface of the Orbiter during descent. This data collect was initiated by NASA s Hypersonic Thermodynamic Infrared Measurements (HYTHIRM) team and incorporated the use of air- and land-based optical assets to image the Shuttle during atmospheric re-entry. The HYTHIRM objective is to develop and implement a set of mission planning tools designed to establish confidence in the ability of an existing optical asset to reliably acquire, track and return global quantitative surface temperatures of the Shuttle during entry. On Space Shuttle Discovery s STS-119 mission, NASA flew a specially modified thermal protection system tile and instrumentation package to monitor heating effects from boundary layer transition during re-entry. On STS-119, the windward airflow on the port wing was deliberately disrupted by a four-inch wide and quarter-inch tall protuberance built into the modified tile. In coordination with this flight experiment, a US Navy NP-3D Orion aircraft was flown 28 nautical miles below Discovery and remotely monitored surface temperature of the Orbiter at Mach 8.4 using a long-range infrared optical package referred to as Cast Glance. Approximately two months later, the same Navy Cast Glance aircraft successfully monitored the surface temperatures of the Orbiter Atlantis traveling at approximately Mach 14.3 during its return from the successful Hubble repair mission. In contrast to Discovery, Atlantis was not part of the Boundary Layer Transition (BLT) flight experiment, thus the vehicle was not configured with a protuberance on the port wing. In September 2009, Cast Glance was again successful in capturing infrared imagery and monitoring the surface temperatures on Discovery s next flight, STS-128. Again, NASA flew a specially modified thermal protection system tile and instrumentation package to monitor heating effects from boundary layer transition during re-entry. During this mission, Cast Glance was able to image laminar and turbulent flow phenomenology optimizing data collection for Mach 14.7. The purpose of this paper is to describe key elements associated with STS-119/125/128 mission planning and execution from the perspective of the Cast Glance flight crew that obtained the imagery. The paper will emphasize a human element of experience, expertise and adaptability seamlessly coupled with Cast Glance system and sensor technology required to manually collect the required imagery. Specific topics will include a near infrared (NIR) camera upgrade that was implemented just prior to the missions, how pre-flight radiance modeling was utilized to optimize the IR sensor configuration, communications, the development of aircraft test support positions based upon Shuttle trajectory information, support to contingencies such as Shuttle one orbit wave-offs/west coast diversions and then the Cast Glance perspective during an actual Shuttle imaging mission.

Tack, Steve↗