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NASA NTRS2015

The Suomi National Polar-Orbiting Partnership (SNPP): Continuing NASA Research and Applications

The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC).

remote sensing↗

NASA NTRS2013

The Collection 6 'dark-target' MODIS Aerosol Products

Aerosol retrieval algorithms are applied to Moderate resolution Imaging Spectroradiometer (MODIS) sensors on both Terra and Aqua, creating two streams of decade-plus aerosol information. Products of aerosol optical depth (AOD) and aerosol size are used for many applications, but the primary concern is that these global products are comprehensive and consistent enough for use in climate studies. One of our major customers is the international modeling comparison study known as AEROCOM, which relies on the MODIS data as a benchmark. In order to keep up with the needs of AEROCOM and other MODIS data users, while utilizing new science and tools, we have improved the algorithms and products. The code, and the associated products, will be known as Collection 6 (C6). While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. In its entirety, the C6 algorithm is comprised of three sub-algorithms for retrieving aerosol properties over different surfaces: These include the dark-target DT algorithms to retrieve over (1) ocean and (2) vegetated-dark-soiled land, plus the (3) Deep Blue (DB) algorithm, originally developed to retrieve over desert-arid land. Focusing on the two DT algorithms, we have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to 84) to increase pole-ward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such as topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence in the retrieval, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time as we have introduced algorithm changes, we have also accounted for upstream changes including: new instrument calibration, revised land-sea masking, and changed cloud masking. Upstream changes also impact the coverage and global statistics of the retrieved AOD. Although our responsibility is to the DT code and products, we have also added a product that merges DT and DB product over semi-arid land surfaces to provide a more gap-free dataset, primarily for visualization purposes. Preliminary validation shows that compared to surface-based sunphotometer data, the C6, Level 2 (along swath) DT-products compare at least as well as those from C5. C6 will include new diagnostic information about clouds in the aerosol field, including an aerosol cloud mask at 500 m resolution, and calculations of the distance to the nearest cloud from clear pixels. Finally, we have revised the strategy for aggregating and averaging the Level 2 (swath) data to become Level 3 (gridded) data. All together, the changes to the DT algorithms will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. Changes in calibration will have more impact to Terras time series, especially over land. This will result in a significant reduction in artificial differences in the Terra and Aqua datasets, and will stabilize the MODIS data as a target for AEROCOM studie

Aerosol retrieval algorithms↗

NASA NTRSDate not supplied

Satellite-Assisted Particulate Matter (SAPM) for the Models, In situ, and Remote Sensing of Aerosols (MIRA) Working Group

Models, In situ, and Remote sensing of Aerosols (MIRA) is an international working group that provides a forum for collaborations amongst these three atmospheric aerosol communities. MIRA consists of a collection of interdisciplinary and independently funded Topic/Project groups with clear goals and generally characterized by requests for additional scientific data. The Satellite-Assisted Particulate Matter (SAPM) Topic group focuses on the study of fine particulate matter (PM2.5), as it is a major contributor to air pollution and negatively impacts human health. Our group aims to provide intercomparisons of various methods and techniques for estimating surface PM2.5 assisted by satellite remote sensors (passive and active), global aerosol models, and in situ aerosol measurements. The overall motivation of our work is to enhance PM2.5 coverage beyond in situ ground stations, which can be limited. In this presentation, we provide an overview of various PM2.5 estimation approaches by current SAPM team members, as each approach has its own strengths and limitations. One approach uses near-surface aerosol extinction retrievals from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and an assumed value of mass extinction efficiency to derive PM2.5 concentrations (Toth et al 2019). A study using this method found a promising agreement (R=0.60; slope=0.89) with U.S. Environmental Protection Agency (EPA) in situ PM2.5 measurements over the contiguous United States (CONUS) for a 12-year (2007-2018) period (Toth et al 2022). In a different approach combining spaceborne lidar and a model, the Cloud Aerosol Transport System (CATS) lidar and Goddard Earth Observing System (GEOS) model are blended in an ensemble variational assimilation scheme to retrieve aerosol extinction and convert speciated mass concentrations to total PM2.5. A recent study (Matus et al in prep.) found agreement within 2 μg/m3 over the CONUS on the 2016 annual mean when compared to EPA ground-based measurements. Another study explored trends in city aerosol optical depth (AOD) from a spaceborne passive remote sensor, Moderate Resolution Imaging Spectroradiometer (MODIS), and found that these trends agree well with trends in city surface PM2.5, which provides confidence in the use of city AOD for city PM2.5 trend studies (Vohra et al 2021). In a follow-on study, steep and significant trends (~2.5 to 7.8% a-1) in AOD are found for several South Asian cities (e.g., Bangalore and Hyderabad), suggesting rapid growth in PM2.5 (Vohra et al 2022). Also in this presentation, we report results on our current collective SAPM study of India, a country that exhibits high levels of PM2.5 pollution. We show the temporal (i.e., seasonal) and spatial variability of PM2.5 over India using the CALIOP and CATS+model approaches, comparisons between the estimated PM2.5 from the retrieval schemes, and validation with in situ PM2.5 ground-based observations from the Central Pollution Control Board in India. This region provides us with an excellent case study to test our PM2.5 retrievals in heavily polluted scenes and over a complex and varying topography. The SAPM Topic group actively seeks international participants/collaborators in our group, including those working with in situ aerosol measurements (e.g., ACTRIS). We are interested in aerosol datasets in order to either improve our PM2.5 estimates from various approaches (e.g., using in situ mass scattering/absorption coefficient and aerosol hygroscopic properties for various aerosol species) and/or validate the PM2.5 estimates (e.g., using in situ ground-based PM2.5 concentrations).

Travis D Toth↗

NASA NTRS2000

Case Study Analyses of the SUCCESS DC-8 Scanning Lidar Database

Under project SUCCESS (Subsonic Aircraft Contrail and Cloud Effects Special Study) funded by the Atmospheric Effects of Aviation Program, SRI International (SRI) developed an angular scanning backscatter lidar for operation on the NASA DC-8 research aircraft and deployed the scanning lidar during the SUCCESS field campaign. The primary purpose of the lidar was to generate real-time video displays of clouds and contrails above, ahead of, and below the DC-8 as a means to help position the aircraft for optimum cloud and contrail sampling by onboard in situ sensors, and to help extend the geometrical domain of the in situ sampling records. A large, relatively complex lidar database was collected and several data examples were processed to illustrate the value of the lidar data for interpreting the other data records collected during SUCCESS. These data examples were used to develop a journal publication for the special SUCCESS Geophysical Research Letters issue. The data examples justified data analyses of a larger part of the DC-8 lidar database and is the objective of the current study. Efficient processing of the SUCCESS DC-8 scanning lidar database required substantial effort to enhance hardware and software components of the data system that was used for the initial analyses. MATLAB instructions are used to generate altitude and distance color-coded lidar displays corrected for effects introduced by aircraft pitch and forward movement during an angular scan time interval. Onboard in situ sensor atmospheric measurements are propagated to distances ahead of the DC-8 using recorded aircraft velocity so that they can be plotted on the lidar displays for comparison with lidar remotely observed aerosol distributions. Resulting lidar and in situ sensor polar scan displays over extended sampling intervals are integrated into a time series movie format for 36 case studies. Contrails and clouds were detected to ranges of 15 km by the forward-viewing angular scanning lidar and were progressively mapped as the aircraft approached and penetrated them. Near aircraft lidar observations were much better correlated with in situ sensor observations than lidar observations at greater distances ahead of the aircraft. The major cause of this difference was thought to be the about 2 deg. offset of the lidar viewing direction from the flight direction. Contrail spatial distributions were not of the quality obtainable from ground-based lidar observations. This results because contrails tend to become horizontally stratified, vertical distance between angular lidar observations increases with increased distance from the aircraft, and erratic aircraft motions during an angular scan. The most useful lidar observations were made with lidar viewing directions of vertically upward or vertically downward. These provided real-time information on aircraft altitudes to achieve optimum in situ cloud and contrail sampling. At sampling altitudes, the forward viewing angular scanning observations were useful for fine-tuning the aircraft altitude for cloud and contrail penetration. Best information on cloud and contrail properties were obtained from vertically directed lidar observations as the aircraft performed a series of upward and downward penetrations of contrails. This operational mode was especially well suited for lidar and radiometric evaluation of cloud and contrail optical and radiative properties. The vertical viewing lidar detected ice crystals thought to be precipitating from an aircraft contrail and their scavenging by a cirrus cloud layer. The lidar display indicates that the crystals are effective for increasing cirrus cloud density. Vertical angular scanning observations can evaluate the sharp decrease in lidar backscatter for small off-vertical viewing directions that result from horizontally aligned ice crystals and perhaps can provide additional information on crystal shapes. The about 2 deg. offset of the lidar viewing direction from the flight direction is thought to have greatly degraded the forward-viewing angular scanning observations and this mode of operation was not fully evaluated. However, the reasoning for this capability remains valid and the angular scan presentations collected during this program justifies modification of the lidar pod for true forward direction lidar viewing during future cloud and contrail studies.

Uthe, Edward E.↗

NASA NTRS2016

Tethered Space Satellite-1 (TSS-1): Technical Roundabouts

In the early 1990's US and Italian scientists collaborated to study the electrodynamics of dragging a satellite on a tether through the electrically charged portion of Earth's atmosphere called the ionosphere. An electrical current induced in the long wire could be used for power and thrust generation for a satellite. Other tether uses include momentum exchange, artificial gravity, deployment of sensors or antennas, and gravity-gradient stabilization for satellites. Before the Tethered Space Satellite (TSS-1), no long tether had ever been flown, so many questions existed on how it would actually behave. The TSS consisted of a satellite with science experiments attached to a 12.5 mile long, very thin (0.10 inch diameter) copper wire assembly wound around a spool in the deployer reel mechanism. With the Space Shuttle at an altitude of 160 nautical miles above earth, the satellite was to be deployed by raising it from the Shuttle bay on a boom facing away from Earth. Once cleared of the bay, the deployer mechanism was to slowly feed out the 12-plus miles of tether. Scientific data would be collected throughout the operation, after which the satellite would be reeled back in. Pre-flight testing system level tests involved setting up a tether receiver to catch the 12.5 mile tether onto another reel as it was being unwound by the deployer reel mechanism. Testing only the reel mechanism is straightforward. This test becomes more complicated when the TSS is mounted on the flight pallet at Kennedy Space Center (KSC). The system level tests must be passed before the pallet can be installed into the Space Shuttle cargo bay. A few months before flight, the TSS payload had been integrated onto the Spacelab pallet and system level tests, including unreeling and reeling the tether, had been successfully completed. Some of this testing equipment was then shipped back to the contractor Martin Marietta. Systems-level load analyses, which cannot be run until all information about each payload is finalized, was run in parallel with the physical integration of the hardware into the Shuttle payload bay. The coupled loads analysis, as it is called, incorporates any updates to the model due to system level tests, and any changes that were found during integration. The coupled loads analysis revealed that a single bolt attaching the deployer reel mechanism to the support structure had a "negative margin" - which is an indication that it might fail during operation. Hardware certification rules do not allow for hardware to fly with negative margins, so this issue had to be resolved before the flight. Since there is conservatism in engineering analysis, there is an option to "waive" the margin requirement, and fly the experiment as is. On the other hand, a structural failure of one payload could have serious or catastrophic consequences to other payloads and possibly the mission. Minor design changes or fixes might be feasible within the payload bay prior to launch. Any major design changes that required the spooling test to validate the hardware, or for the pallet to be removed, would cause TSS not to be ready for the Shuttle launch.

O'Connor, Brian↗

NASA NTRS1998

A Multi-Scale Sampling Strategy for Detecting Physiologically Significant Signals in AVIRIS Imagery

Models of photosynthetic production at ecosystem and global scales require multiple input parameters specifying physical and physiological surface features. While certain physical parameters (e.g., absorbed photosynthetically active radiation) can be derived from current satellite sensors, other physiologically relevant measures (e.g., vegetation type, water status, carboxylation capacity, or photosynthetic light-use efficiency), are not generally directly available from current satellite sensors at the appropriate geographic scale. Consequently, many model parameters must be assumed or derived from independent sources, often at an inappropriate scale. An abundance of ecophysiological studies at the leaf and canopy scales suggests strong physiological control of vegetation-atmosphere CO2 and water vapor fluxes, particularly in evergreen vegetation subjected to diurnal or seasonal stresses. For example hot, dry conditions can lead to stomatal closure, and associated "downregulation" of photosynthetic biochemical processes, a phenomenon often manifested as a "midday photosynthetic depression". A recent study with the revised simple biosphere (SiB2) model demonstrated that photosynthetic downregulation can significantly impact global climate. However, at the global scale, the exact significance of downregulation remains unclear, largely because appropriate physiological measures are generally unavailable at this scale. Clearly, there is a need to develop reliable ways of extracting physiologically relevant information from remote sensing. Narrow-band spectrometers offer many opportunities for deriving physiological parameters needed for ecosystem and global scale photosynthetic models. Experimental studies on the ground at the leaf- to stand-scale have indicated that several narrow-band features can be used to detect plant physiological status. One physiological signal is caused by xanthophyll cycle pigment activity, and is often expressed as the Photochemical Reflectance Index (PRI). Because the xanthophyll cycle pigments are photoregulatory pigments closely linked to photosynthetic function, this index can be used to derive relative photosynthetic rates. An additional signal with physiological significance is the 970 nm water absorption band, which provides a measure of liquid water content. This feature has been quantified both using a simple 2-band ratio (900/970 nm, here referred to as the "Water Band Index" or WBI;), and using the "continuum removal" method. Current atmospheric correction methods for AVIRIS imagery also obtain quantitative expressions of surface liquid water absorption based on the 970 nm water band and may be comparable to ground-based estimates of water content using this feature. However, physiological interpretations of both the PRI and the WBI are best understood at the leaf and canopy scales, where complications of atmospheric interference and complex stand and landscape features can be minimized, and where experimental manipulations can be readily applied. Currently it is not known whether these physiological indices can be used to derive meaningful physiological information from AVIRIS imagery. In addition to the problem of atmospheric interference, another challenge is that any simple physiological index can be confounded by multiple factors unrelated to physiology, and this problem can become more severe at progressively larger spatial scales. For example, previous work has suggested that both the PRI and the WBI, are strongly correlated with other optical measures of canopy structure (e.g., the Normalized Difference Vegetation Index or green vegetation fraction), indicating a confounding effect of structure on physiological signals at the larger, landscape scale. Furthermore, the normal operating mode of most imaging spectrometers does not allow simultaneous, ground truthing at a level of detail needed for physiological sampling. Additionally, manipulative experiments of physiology are difficult to apply at a geographic scale suitable for comparison with remote imagery, which often works at spatial scales that are several orders of magnitude larger than those typically used for physiological studies. These limitations require the consideration of alternative approaches to validating physiological information derived from AVIRIS data. In this report, we present a multi-scale sampling approach to detecting physiologically significant signals in narrow-band spectra. This approach explores the multi-dimensional data space provided by narrow-band spectrometry, and combines AVIRIS imagery at a large scale, with ground spectral sampling at an intermediate scale, and detailed ecophysiological measurements at a fine scale, to examine seasonally and spatially changing relationships between multiple structural and physiological variables. Examples of this approach are provided by simultaneous sampling of the Normalized Difference Vegetation Index (NDVI), an index of fractional PAR interception and green vegetation cover, the Water Band Index (WBI, an index of liquid water absorption, and the Photochemical Reflectance Index (PRI, an index of xanthophyll cycle pigment activity and photosynthetic light-use efficiency. By directly linking changing optical properties sampled on the ground with measurable physiological states, we hope to develop a basis for interpreting similar signals in AVIRIS imagery.

Gamon, John A.↗

NASA NTRSDate not supplied

Implementation of A New Microwave Scattering Database and A Forward Model for Active Microwave Sensors in CRTM

Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. CRTM is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk scattering lookup tables in order to perform all-sky RT calculations. However, the current CRTM lookup tables for microwave frequencies were generated based on the Mie theory by assuming spherical frozen particles. The scattering lookup tables generated using the DDA technique has shown to largely improve the RT scattering calculations in the MW region. This presentation targets (i) the implementation and validation of a DDA database that was originally developed for the ARTS RT model into CRTM, and (ii) developing the CRTM active sensor module that takes advantage of the backscattering coefficients computed using the DDA method. The DDA database only provides single scattering properties of different habits, while CRTM requires bulk scattering properties. The CRTM cloud coefficients were previously generated based on the effective radius for representing the size of the particles. However, effective radius is neither measurable nor provided by the NWP models, thus need to be estimated from other geophysical variables such as water content. Therefore, in addition to calculating the CRTM bulk scattering properties from the DDA single scattering database, the CRTM was also largely modified to use cloud water content (kg.m-3), instead of effective radius, for performing the interpolation over size/mass of the particles. CRTM already requires water content as input, thus no extra variables are required for performing scattering calculations using the new ARTS DDA database. The CRTM scattering modules search for effective radius in cloud coefficient files and will use the cloud water content if the effective radius dimension is not found in the cloud coefficient files. Figure 1 shows the CRTM simulated brightness temperatures computed using different cloud coefficients versus ATMS observed values over Hurricane Irma on September 7, 2017 at 18:00 UTC. We used all the cloud water content values included in ERA5 with default CRTM/DDA habits for water, rain, snow, ice, hail, and graupel. ERA5 does not provide separate water content values for ice, hail, and graupel, thus the ice water content values were divided between ice, hail, and graupel clouds similar to what was explained in the previous section. In channels with a frequency lower than 90 GHz, emission from water and rain clouds can compensate for cloud scattering so that cloud contaminated Tbs are larger than corresponding clear sky Tbs. The DDA simulations for channels 1-7 largely perform better than the Mie simulations. The DDA simulations show a mix of small negative and positive simulated minus observed values, while the Mie results show large negative biases. The weighting functions for some of the ATMS temperature sounding channels (channels 9-15) peak mostly above the clouds, therefore the measured Tbs become less sensitive to clouds so that the results of both Mie and DDA become very similar. The Mie lookup tables generate excessive scattering for channel 16, but not enough scattering for the water vapor channels. In the specific case of Hurricane Maria, the DDA lookup tables do not generate enough scattering for channel 16, but the DDA results are much more consistent with observations for water vapor channels than for channel 16. It should be noted that the results may vary if we use other habits to represent snow, hail, and graupel in the DDA simulations. Although these results clearly show the advantage of the DDA database over the Mie dataset, different error sources such as error in the observations, displacement of clouds in the ERA5 reanalysis, and also lack of convective clouds or in general errors in the input atmospheric and cloud profiles contribute to the differences between the simulated and observed values. Aside from the improvements in the simulations, a major advantage of the new dataset is a large number of habits that can be used to tune the data assimilation systems to perform well in different weather conditions.

Isaac Moradi↗

NASA NTRS2016

Forward Skirt Structural Testing on the Space Launch System (SLS) Program

Structural testing was performed to evaluate heritage forward skirts from the Space Shuttle program for use on the Space Launch System (SLS) program. One forward skirt is located in each solid rocket booster. Heritage forward skirts are aluminum 2219 welded structures. Loads are applied at the forward skirt thrust post and ball assembly. Testing was needed because SLS ascent loads are roughly 40% higher than Space Shuttle loads. Testing objectives were to determine margins of safety, demonstrate reliability, and validate analytical models. Two forward skirts were structurally tested using the test configuration. The test stand applied loads to the thrust post. Four hydraulic actuators were used to apply axial load and two hydraulic actuators were used to apply radial and tangential loads. The first test was referred to as FSTA-1 (Forward Skirt Structural Test Article) and was performed in April/May 2014. The purpose of FSTA-1 was to verify the ultimate capability of the forward skirt subjected to ascent ultimate loads. Testing consisted of two liftoff load cases taken to 100% limit load followed by an ascent load case taken to 110% limit load. The forward skirt was unloaded to no load after each test case. Lastly, the forward skirt was tested to 140% limit and then to failure using the ascent loads. The second test was referred to as FSTA-2 and performed in July/August of 2014. The purpose of FSTA-2 was to verify the ultimate capability of the forward skirt subjected to liftoff ultimate loads. Testing consisted of six liftoff load cases taken to 100% limit load followed by the six liftoff cases taken to 140% limit load. Two ascent load cases were then tested to 100% limit load. The forward skirt was unloaded to no load after each test case. Lastly, the forward skirt was tested to 140% limit and then to failure using the ascent loads. The forward skirts on FSTA-1 and FSTA-2 successfully carried all applied liftoff and ascent load cases. Both FSTA-1 and FSTA-2 were tested to failure by increasing the ascent loads. Failure occurred in the forward skirt thrust post radius. The forward skirts on FSTA-1 and FSTA-2 had nearly identical failure modes. FSTA-1 failed at 1.72 times limit load and FSTA-2 failed at 1.62 times limit load. This difference is primarily attributed to variation in material properties in the thrust post region. Test data were obtained from strain gages, deflection gages, ARAMIS digital strain measurement, acoustic emissions, and high-speed video. Strain gage data and ARAMIS strain were compared to finite element (FE) analysis predictions. Both the forward skirt and tooling were modeled. This allows the analysis to simulate the loading as close as possible to actual test configuration. FSTA-1 and FSTA-2 were instrumented with over 200 strain gages to ensure all possible failure modes could be captured. However, it turned out that three gages provided critical strain data. One was located in the post bore and two on the post radius. More gages were not specified due to space limitations and the desire to not interfere with the use of the ARAMIS system on the post radius. Measured strains were compared to analysis results for the load cycle to failure. Note that FSTA-1 gages were lost before failure was reached. FSTA-2 gages made it to the failure load but one of the radius gages was lost before testing began. This gage was not replaced because of the time and cost associated with disassembly of the test structure. Correlation to analysis was excellent for FSTA-1. FSTA-2 was not quite as good because there was more residual strain from previous load cycles. FSTA-2 was loaded and unloaded with 12 liftoff cases and two ascent cases before taking the skirt to failure. FSTA-1 only had two liftoff cases and one ascent case before taking the skirt to failure. The ARAMIS system was used to determine strain at the post radius by processing digital images of a speckled paint pattern. Digital cameras recorded images of the speckled paint pattern. ARAMIS strain results for FSTA-2 just prior to failure. Note a high strain location develops near the left side. This high strain compares well to analysis prediction for both FSTA-1 and FSTA-2. The strain at this location was also plotted versus limit load. Both FSTA-1 and FSTA-2 had excellent correlation between ARAMIS and analysis strains. Acoustic emission (AE) sensors were used to monitor for damage formation that may occur during testing (e.g., crack formation and growth or propagation). AE was very important because after disassembly of FSTA-1, a crack was observed in the ball fitting radius. The ball fitting did not crack on FSTA-2. AE data was used to reconstruct when the crack occurred. The AE energy versus time plot for FSTA. The energy increased considerably at 850 seconds (152% limit load), indicating a crack could have formed at this point. The only visual evidence found that could have corresponded to this was the crack that initiated in the ball fitting. The cracks in the forward skirt aluminum structures would likely have been lower energy due to a lower modulus and all that were found after failure correlated to occurring after the initial crack in the post radius. This was verified by high-speed cameras used to record the failure.

Lohrer, J. D.↗