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This award was relinquished by the University of Alabama at Birmingham (UAB) shortly after transferred to Indiana University. No research activities were conducted at UAB under this award. No personnel were supported, no funds were expended, and no publications or other research outputs were produced at UAB.
Thermal, acoustical, and acceleration environments of XB-70 airplane crew compartment in airworthiness tests
A test loop was designed and fabricated for observing and measuring pressure drops of two-phase flow in reduced gravity. The portable flow test loop was then tested aboard the NASA-JSC KC135 reduced gravity aircraft. The test loop employed the Sundstrand Two-Phase Thermal Management System (TPTMS) concept which was specially fitted with a clear two-phase return line and condenser cover for flow observation. A two-phase (liquid/vapor) mixture was produced by pumping nearly saturated liquid through an evaporator and adding heat via electric heaters. The quality of the two-phase flow was varied by changing the evaporator heat load. The test loop was operated on the ground before and after the KC135 flight tests to create a one-gravity data base. The ground testing included all the test points run during the reduced gravity testing. Two days of reduced gravity tests aboard the KC135 were performed. During the flight tests, reduced-gravity, one-gravity, and nearly two-gravity accelerations were experienced. Data was taken during the entire flight which provided flow regime and pressure drop data for the three operating conditions. The test results show that two-phase pressure drops and flow regimes can be accurately predicted in zero-gravity.
Analysis of the data obtained from two flights of a balloonborne gamma-ray detector to observe SN 1987A was completed. The detector, which included a spark chamber to determine the arrival directions of the photons, was sensitive in the energy range 50-500 MeV. The 95 percent confidence upper limit to the flux on day 55 after the explosion has been established to be 1.1 x 10 exp -5 photons/sq cm/s and on day 407 to be 3.4 x 10 exp -5 photons/sq cm/s. These limits are compared with various theoretical predictions.
Two full-scale crash tests of an MD-500 helicopter were conducted in 2009 and 2010 at NASA Langley's Landing and Impact Research Facility in support of NASA s Subsonic Rotary Wing Crashworthiness Project. The first crash test was conducted to evaluate the performance of an externally mounted composite deployable energy absorber under combined impact conditions. In the second crash test, the energy absorber was removed to establish baseline loads that are regarded as severe but survivable. Accelerations and kinematic data collected from the crash tests were compared to a system integrated finite element model of the test article. Results from 19 accelerometers placed throughout the airframe were compared to finite element model responses. The model developed for the purposes of predicting acceleration responses from the first crash test was inadequate when evaluating more severe conditions seen in the second crash test. A newly developed model calibration approach that includes uncertainty estimation, parameter sensitivity, impact shape orthogonality, and numerical optimization was used to calibrate model results for the second full-scale crash test. This combination of heuristic and quantitative methods was used to identify modeling deficiencies, evaluate parameter importance, and propose required model changes. It is shown that the multi-dimensional calibration techniques presented here are particularly effective in identifying model adequacy. Acceleration results for the calibrated model were compared to test results and the original model results. There was a noticeable improvement in the pilot and co-pilot region, a slight improvement in the occupant model response, and an over-stiffening effect in the passenger region. This approach should be adopted early on, in combination with the building-block approaches that are customarily used, for model development and test planning guidance. Complete crash simulations with validated finite element models can be used to satisfy crash certification requirements, thereby reducing overall development costs.
A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.
Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.
A large increase in stratospheric aerosol data has become available recently from the SAM II and SAGE satellite sensors and the impetus from increased volcanic perturbations. Six years of SAM II and nearly 3 years of SAGE measurements have been accumulated. The increase in large volcanic eruptions since 1979 has caused an acceleration of new data sets from worldwide lidars and airborne lidar campaigns and from various airborne in situ measurements. The SAM II and SAGE data sets show the tropical stratosphere as a source for background stratospheric aerosols, and midlatitudes as a possible sink. Analyses of SAM II data show that the aerosol within the northern wintertime polar vortex becomes isolated from the outside. Subsidence occurs within the vortex, changing the vertical aerosol distribution over the winter period. SAM II and SAGE data show that the aerosol is transported in the stratosphere from low to high latitudes in wintertime. Entry regions of tropospheric air in the Tropics are also evident in the SAGE data as shown by stratospheric cirrus clouds being formed well above the local tropopause. Nature has provided over the past 5 years a number of large volcanic eruptions which spewed tons of new aerosol into the stratosphere. These eruptions have occurred at various latitudes which allow transport differences to be studied. Satellite and lidar aerosol data will be used to describe the stratospheric motions of aerosols produced after these violet volcanic eruptions.
Support of microgravity research on the 89th flight of the Space Transportation System (STS-89) and a continued effort to characterize the acceleration environment of the Space Shuttle Orbiter and the Mir Space Station form the basis for this report. For the STS-89 mission, the Space Shuttle Endeavour was equipped with a Space Acceleration Measurement System (SAMS) unit, which collected more than a week's worth of data. During docked operations with Mir, a second SAMS unit collected approximately a day's worth of data yielding the only set of acceleration measurements recorded simultaneously on the two spacecraft. Based on the data acquired by these SAMS units, this report serves to characterize a number of acceleration events and quantify their impact on the local nature of the accelerations experienced at the Mechanics of Granular Materials (MGM) experiment location. Crew activity was shown to nearly double the median root-mean-square (RMS) acceleration level calculated below 10 Hz, while the Enhanced Orbiter Refrigerator/Freezer operating at about 22 Hz was a strong acceleration source in the vicinity of the MGM location. The MGM science requirement that the acceleration not exceed q I mg was violated numerous times during their experiment runs; however, no correlation with sample instability has been found to this point. Synchronization between the SAMS data from Endeavour and from Mir was shown to be close much of the time, but caution with respect to exact timing should be exercised when comparing these data. When orbiting as a separate vehicle prior to docking, Endeavour had prominent structural modes above 3 Hz, while Mir exhibited a cluster of modes around 1 Hz. When mated, a transition to common modes was apparent in the two SAMS data sets. This report is not a comprehensive analysis of the acceleration data, so those interested in further details should contact the Principal Investigator Microgravity Services team at the National Aeronautics and Space Administration's John H. Glenn Research Center.
Support of microgravity research on the 89th flight of the Space Transportation System (STS-89) and a continued effort to characterize the acceleration environment of the Space Shuttle Orbiter and the Mir Space Station form the basis for this report. For the STS-89 mission, the Space Shuttle Endeavour was equipped with a Space Acceleration Measurement System (SAMS) unit, which collected more than a week's worth of data. During docked operations with Mir, a second SAMS unit collected approximately a day's worth of data yielding the only set of acceleration measurements recorded simultaneously on the two spacecraft. Based on the data acquired by these SAMS units, this report serves to characterize a number of acceleration events and quantify their impact on the local nature of the accelerations experienced at the Mechanics of Granular Materials (MGM) experiment location. Crew activity was shown to nearly double the median root-mean-square (RMS) acceleration level calculated below 10 Hz, while the Enhanced Orbiter Refrigerator/Freezer operating at about 22 Hz was a strong acceleration source in the vicinity of the MGM location. The MGM science requirement that the acceleration not exceed plus or minus 1 mg was violated numerous times during their experiment runs; however, no correlation with sample instability has been found to this point. Synchronization between the SAMS data from Endeavour and from Mir was shown to be close much of the time, but caution with respect to exact timing should be exercised when comparing these data. When orbiting as a separate vehicle prior to docking, Endeavour had prominent structural modes above 3 Hz, while Mir exhibited a cluster of modes around 1 Hz. When mated, a transition to common modes was apparent in the two SAMS data sets. This report is not a comprehensive analysis of the acceleration data, so those interested in further details should contact the Principal Investigator Microgravity Services team at the National Aeronautics and Space Administration's John H. Glenn Research Center.
Transient kinetic data contain a wealth of information about intrinsic features of a catalyst as well as the reaction mechanism. Currently, high volume transient data is underutilized, and data science methods could both increase the value of information that can be extracted from this data, integrate experimental with theoretical data sources, and accelerate the pace of catalyst technology advancement. Transient kinetic characterizations with simple probe molecules exhibiting reversible adsorption, irreversible adsorption and bulk-surface diffusion are presented as training components for similar experiments with more complex surface reactions. In conclusion, by increasing the availability and accessibility of transient kinetic data through details of its structure and acquisition, we aim to decrease the barrier for data scientists to apply machine learning methods to this valuable data source.
Main objective for launch vehicle (LV) modal testing is to quantify the LV’s modal properties in the free-free state (i.e., post pad separation). Given the size of most LVs, free-free testing is a challenge and often not feasible. A test stand, typically the launch pad itself, is utilized as the means to support the LV an Integrated Modal Test (IMT). This shifts the challenge to developing robust numerical methods for removing the influence of the launch pad from the IMT. Preferably fully test driven (method’s that operate based on measured data only). The Accelerance Decoupling (AD) approach, developed and verified by NASA and ASD, is a method with the capability to “subtract out” the influence of the test stand from the IMT using measured data only
NASA is developing a new capsule-based, crewed vehicle that will land in the ocean, and the space agency desires to reduce the risk of injury from impact during these landings. Because landing impact occurs for each flight and the crew might need to perform egress tasks, current injury assessment reference values (IARV) were deemed insufficient. Because NASCAR occupant restraint systems are more effective than the systems used to determine the current IARVs and are similar to NASA s proposed restraint system, an analysis of NASCAR impacts was performed to develop new IARVs that may be more relevant to NASA s context of vehicle landing operations. Head IARVs associated with race car impacts were investigated by completing a detailed analysis of all of the 2002-2008 NASCAR impact data. Specific inclusion and exclusion criteria were used to select 4071 impacts from the 4015 recorder files provided (each file could contain multiple impact events). Of the 4071 accepted impacts, 274 were selected for numerical simulation using a custom NASCAR restraint system and Humanetics Hybrid-III 50th percentile numerical dummy model in LS-DYNA. Injury had occurred in 32 of the 274 selected impacts, and 27 of those injuries involved the head. A majority of the head injuries were mild concussions with or without brief loss of consciousness. The 242 non-injury impacts were randomly selected and representative of the range of crash dynamics present in the total set of 4071 impacts. Head dynamics data (head translational acceleration, translational change in velocity, rotational acceleration, rotational velocity, HIC-15, HIC-36, and the Head 3ms clip) were filtered according to SAE J211 specifications and then transformed to a log scale. The probability of head injury was estimated using a separate logistic regression analysis for each log-transformed predictor candidate. Using the log transformation constrains the estimated probability of injury to become negligible as IARVs approach zero. For the parameters head translational acceleration, head translational velocity change, head rotational acceleration, HIC-15, and HIC-36, conservative values (in the lower 95% confidence interval) that gave rise to a 5% risk of any injury occurring were estimated as 40.0 G, 7.9 m/s, 2200 rad/s2, 98.4, and 77.4 respectively. Because NASA is interested in the consequence of any particular injury on the ability of the crew to perform egress tasks, the head injuries that occurred in the NASCAR dataset were classified according to a NASA-developed scale (Classes I - III) for operationally relevant injuries, which classifies injuries on the basis of their operational significance. Additional analysis of the data was performed to determine the probability of each injury class occurring, and this was estimated using an ordered probit model. For head translational acceleration, head translational velocity change, head rotational acceleration, head rotational velocity, HIC-36, and head 3ms clip, conservative values of IARVs that produced a 5% risk of Class II injury were estimated as 50.7 G, 9.5 m/s, 2863 rad/s2, 11.0 rad/s, 30.3, and 46.4 G respectively. The results indicate that head IARVs developed from the NASCAR dataset may be useful to protect crews during landing impact.
Introduction: Recent data show a landing-attributed injury rate among Soyuz crew members to be higher than predicted in original certification analysis. New vehicles being developed for NASA are being certified using analytical tools to quantify injury risk. This assessment will provide data to allow the Agency to evaluate whether these tools are able to capture true injury risk and allow a comparison between Soyuz landings and upcoming vehicles. Methods: A finite element model (FEM) of the Soyuz descent module (DM) and its landing environment were developed to recreate Soyuz landing conditions. Occupant breakout models composed of the Soyuz seat and Sokol helmet were then developed to evaluate occupant response. These models included both anthropomorphic test device (ATD) and human body model (HBM) surrogates in three anthropometric sizes. A generalized distribution analysis of the Soyuz landing environment was performed using landing variable distributions from available literature. Results were used to identify trends in vehicle landing acceleration with landing conditions and to compare landing model injury risk predictions with those estimated in vehicle certification analysis. Next, historic Soyuz landings identified as inducing occupant injury during were reconstructed using the developed landing model and estimated landing conditions. Results and Discussion: This assessment demonstrated the capability of certification metrics, which include a combination of vehicle- and occupant-level injury risk analysis, to identify elevated injury risk landing conditions. However, the developed landing load predictions were limited by available vehicle and landing condition data. Additional data, particularly landing acceleration data specific to historic Soyuz landing conditions, would greatly enhance this assessment’s findings.
Tracking data reduction to geopotential accelerations for satellites
The method and data for determining the accelerations on the heat flow and convection demonstration are presented. From the analysis, it is concluded that accelerations as low as 0.02 micro g can be detected by using the attitude look angles to the earth and sun, the Apollo trajectory ephemeris, and the ephemeris of the sun.
Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.
The development of design load factors for aerospace and aircraft components and experiment support structures, which are subject to a simultaneous vehicle dynamic vibration (quasi-static) and acoustically generated random vibration, require the selection of a combination methodology. Typically, the procedure is to define the quasi-static and the random generated response separately, and arithmetically add or root sum square to get combined accelerations. Since the combination of a probabilistic and a deterministic function yield a probabilistic function, a viable alternate approach would be to determine the characteristics of the combined acceleration probability density function and select an appropriate percentile level for the combined acceleration. The following paper develops this mechanism and provides graphical data to select combined accelerations for most popular percentile levels.