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NASA NTRS2014

High Energy Replicated Optics to Explore the Sun Balloon-Borne Telescope: Astrophysical Pointing

On September 21, 2013, the High Energy Replicated Optics to Explore the Sun, or HEROES, balloon-borne x-ray telescope launched from the Columbia Scientific Balloon Facility's site in Ft. Summer, NM. The flight lasted for approximately 27 hours and the observational targets included the Sun and astrophysical sources GRS 1915+105 and the Crab Nebula. Over the past year, the HEROES team upgraded the existing High Energy Replicated Optics (HERO) balloon-borne telescope to make unique scientific measurements of the Sun and astrophysical targets during the same flight. The HEROES Project is a multi-NASA Center effort with team members at both Marshall Space Flight Center (MSFC) and Goddard Space Flight Center (GSFC), and is led by Co-PIs (one at each Center). The HEROES payload consists of the hard X-ray telescope HERO, developed at MSFC, combined with several new systems. To allow the HEROES telescope to make observations of the Sun, a new solar aspect system was added to supplement the existing star camera for fine pointing during both the day and night. A mechanical shutter was added to the star camera to protect it during solar observations and two alignment monitoring systems were added for improved pointing and post-flight data reconstruction. This mission was funded by the NASA HOPE (Hands-On Project Experience) Training Opportunity awarded by the NASA Academy of Program/Project and Engineering Leadership, in partnership with NASA's Science Mission Directorate, Office of the Chief Engineer and Office of the Chief Technologist.

Gaskin, Jessica↗

NASA NTRS2017

Investigating Access Performance of Long Time Series with Restructured Big Model Data

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

reanalysis↗

NASA NTRS2018

A Momentum-Based Indicator for Predicting the Peak Opening Load of Supersonic Parachutes

In this paper, a new empirical indicator for predicting the peak opening loads of supersonic parachutes is presented. The proposed indicator is proportional to twice the free-stream dynamic pressure and the projected area of the parachute, which is equivalent to estimating the opening load as a percentage of the free-stream momentum flux through the projected area at the moment of peak inflation. The form of this expression is motivated by a classical control volume analysis of the aerodynamic forces acting on a parachute during inflation, under the simplifying assumptions of quasi-static and one-dimensional flow. For parachute geometries and flight conditions typical of Mars Entry, Descent, and Landing systems, the largest contribution to the total drag is shown to be a momentum flux term that is associated with the entrainment of atmosphere within the inflating parachute volume. Using this new method, empirical constants are calculated from existing flight reconstruction data and are shown to have a smaller standard deviation than similar constants determined using the customary indicator form, which is based on the steady-state subsonic drag and proportional to the parachute reference area. These empirical constants are also compared to an analytic estimate, derived from the control volume analysis, and shown to have excellent agreement across a wide range of Mach numbers and dynamic pressures for several parachute geometries. While opening loads estimated using both methods produce similar results at low supersonic Mach numbers typical of past inflations, the proposed method predicts notably larger loads at higher Mach numbers, those above Mach 2.0, due to the omission of any Mach Efficiency Factor. Several current Mars EDL projects have adopted this new indicator.

Way, David W.↗

NASA NTRSDate not supplied

High-Fidelity Simulations of HyMETS Arc-Jet Flows for PICA-N Modeling

Arc-jet testing is a fundamental tool in the screening of thermal protection systems under atmospheric entry conditions. In this work, high-fidelity computational fluid dynamics simulations coupled with machine learning methods for the Hypersonic Materials Environmental Test System arc-jet facility have been carried out. This effort improves the MEDLI-2 data reconstruction fidelity by understanding the impact of the NuSil coating over the PICA thermocouple plugs. Thermochemical non-equilibrium models are employed to simulate the flows inside the arc-jet nozzle and chamber and machine learning techniques are used to calibrate the arc-jet inflow conditions. The material response is simulated with the recession and in-depth numerical results compared to experimental measurements.

STMD↗

NASA NTRS1997

The Galileo Spacecraft: A Communications Legacy for Future Space Flight

The Galileo mission to Jupiter has implemented a wide range of telecommunication improvements in response to the loss of it's high gain antenna. Among the communications enhancements that have been made are the use of advanced compression techniques, packetized telemetry, new error correcting codes and algorithms, more efficient modulation, variable transmission data rates, routine ground antenna arraying (even between continents,) extremely sensitive ground receivers, and non-real-time automated data reconstruction. This telecommunications legacy of Galileo will aid in our abillity to conduct a meaningful exploration of the solar system, and beyond, at a reasonable cost.

Galileo↗

NASA NTRS1991

Surface reconstruction from scattered data through pruning of unstructured grids

This paper describes an algorithm for reconstructing a surface from a randomly digitized object. Scan data (treated as a cloud of points) is first tesselated out to its convex hull using Delaunay triangulation. The line-of-sight between each surface point and the scanning device is traversed, and any tetrahedra which are pierced by it are removed. The remaining tetrahedra form an approximate solid model of the scanned object. Due to the inherently limited resolution of any scan, this algorithm requires two additional procedures to produce a smooth, polyhedral surface: one process removes long, narrow tetrahedra which span indentations in the surface between digitized points; the other smooths sharp edges. The results for a moderately resolved sample body and a highly resolved aircraft are displayed.

Maksymiuk, C. M.↗

NASA NTRS1984

Extraction of topography from side-looking satellite systems A case study with SPOT simulation data

Techniques for reconstructing topographic data from side-looking satellite systems have been developed whereby nadir and off-nadir passes are coaligned to calculate the stereo displacement for each pixel in the nadir view by correlating a small subarea to a corresponding subarea in the off-nadir pass. The correlation algorithm was tested on two pairs of data sets consisting of patterns of bars and boxes and then applied to the SPOT simulation data set. The correlation was a maximum in areas where the relief was changing gradually, while in areas of constantly changing topography, the correlation decreased.

Ungar, S. G.↗

NASA NTRS2023

Reconstructing PM 2.5 Data Record for the Kathmandu Valley Using a Machine Learning Model

This paper presents a method for reconstructing the historical hourly concentrations of particulate matter 2.5 (PM2.5) over the Kathmandu Valley from 1980 to the present. The method uses a machine learning model that is trained using PM2.5 readings from US Embassy (Phora Durbar) as a ground truth, and the meteorological data from Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA2) as input. The Extreme Gradient Boosting (XGBoost) model acquires a credible 10-fold cross-validation (CV) score of ~83.4%, an r2-score of ~84%, a Root Mean Square Error (RMSE) of ~15.82 µg/m3, and a Mean Absolute Error (MAE) of ~10.27 µg/m3. Further demonstrating the model's applicability to years other than those for which truth values are unavailable, the multiple cross-test with an unseen data set offered r2-scores for 2018, 2019, and 2020 ranging from 56% to 67%. The model-predicted data agrees with true values and indicates that MERRA2 underestimates PM2.5 over the region. It strongly agrees with ground-based evidence showing substantially higher mass concentrations in the dry pre- and post-monsoon seasons than in the monsoon months. It also shows a strong anti-correlation between PM2.5 concentration and humidity. The results also demonstrate that none of the years fulfilled the annual mean air quality index (AQI) standards set by the World Health Organization (WHO).

machine learning↗

NASA NTRS1973

Linear filtering of ballistic-entry-probe data for atmospheric reconstruction.

A Kalman-Schmidt filter is used to estimate atmospheric and trajectory parameters for entry into the Venusian atmosphere. A significantly improved version of the Landing Trajectory Reconstruction (LTR) computer program, used to obtain the estimates is described. Major improvements involve precision and linearity control of numerical differencing, corrected perturbation modeling, and incorporation of a refractivity model. Important results show that gyroscopic data are not usable with LTR, that atmospheric properties are generally well estimated for altitudes less than 100 km, and that the two LTR modes of operation are complementary in performance.

Sabin, M. L.↗

NASA NTRS1993

Methods of evaluating the effects of coding on SAR data

It is recognized that mean square error (MSE) is not a sufficient criterion for determining the acceptability of an image reconstructed from data that has been compressed and decompressed using an encoding algorithm. In the case of Synthetic Aperture Radar (SAR) data, it is also deemed to be insufficient to display the reconstructed image (and perhaps error image) alongside the original and make a (subjective) judgment as to the quality of the reconstructed data. In this paper we suggest a number of additional evaluation criteria which we feel should be included as evaluation metrics in SAR data encoding experiments. These criteria have been specifically chosen to provide a means of ensuring that the important information in the SAR data is preserved. The paper also presents the results of an investigation into the effects of coding on SAR data fidelity when the coding is applied in (1) the signal data domain, and (2) the image domain. An analysis of the results highlights the shortcomings of the MSE criterion, and shows which of the suggested additional criterion have been found to be most important.

Dutkiewicz, Melanie↗

NASA NTRS1989

Space Shuttle propulsion performance reconstruction from flight data

The aplication of extended Kalman filtering to estimating Space Shuttle Solid Rocket Booster (SRB) performance, specific impulse, from flight data in a post-flight processing computer program. The flight data used includes inertial platform acceleration, SRB head pressure, and ground based radar tracking data. The key feature in this application is the model used for the SRBs, which represents a reference quasi-static internal ballistics model normalized to the propellant burn depth. Dynamic states of mass overboard and propellant burn depth are included in the filter model to account for real-time deviations from the reference model used. Aerodynamic, plume, wind and main engine uncertainties are included.

Rogers, Robert M.↗

NASA NTRSDate not supplied

A Machine Learning Model for Solar Sail Shape Reconstruction Using Flight Data

Solar sail deformation leads to disturbance torques from solar radiation pressure, driving performance requirements for momentum management systems. For the Solar Cruiser technology demonstrator mission, we have developed a model leveraging neural network-based machine learning to derive sail shape characteristics. The model uses torque and attitude telemetry simulated from a reduced-order tensor model of the deformed sail mesh over a characterization sequence. The machine learning model predicts sail boom deflection with comparable accuracy to that of an onboard context camera. This model can discover sail shape with no additional mass or data downlink requirements, allowing for validation of sail force modeling assumptions using in flight data. The results from the project hold promise for the further implementation of machine learning techniques in solar sail telemetry analysis and control.

solar sail↗

NASA NTRSDate not supplied

A Machine Learning Model for Solar Sail Shape Reconstruction Using Flight Data

solar sail↗

NASA NTRS2014

Revisiting AVHRR Tropospheric Aerosol Trends Using Principal Component Analysis

The advanced very high resolution radiometer (AVHRR) satellite instruments provide a nearly 25 year continuous record of global aerosol properties over the ocean. It offers valuable insights into the long-term change in global aerosol loading. However, the AVHRR data record is heavily influenced by two volcanic eruptions, El Chichon on March 1982 and Mount Pinatubo on June 1991. The gradual decay of volcanic aerosols may last years after the eruption, which potentially masks the estimation of aerosol trends in the lower troposphere, especially those of anthropogenic origin. In this study, we show that a principal component analysis approach effectively captures the bulk of the spatial and temporal variability of volcanic aerosols into a single mode. The spatial pattern and time series of this mode provide a good match to the global distribution and decay of volcanic aerosols. We further reconstruct the data set by removing the volcanic aerosol component and reestimate the global and regional aerosol trends. Globally, the reconstructed data set reveals an increase of aerosol optical depth from 1985 to 1990 and decreasing trend from 1994 to 2006. Regionally, in the 1980s, positive trends are observed over the North Atlantic and North Arabian Sea, while negative tendencies are present off the West African coast and North Pacific. During the 1994 to 2006 period, the Gulf of Mexico, North Atlantic close to Europe, and North Africa exhibit negative trends, while the coastal regions of East and South Asia, the Sahel region, and South America show positive trends.

volcanoes↗

NASA NTRS1981

Shuttle /STS-1/ entry trajectory reconstruction

The methods and data used to reconstruct the Shuttle entry trajectory aerodynamics from 180 km to landing are reviewed. Onboard accelerometer and gyro measurements are used with a weighted least squares filter along with ground tracking data to provide the evolving spacecraft state. The software development is described, noting that computations include linear acceleration and angular rate data, aerodynamic coefficient, and inertial measurement units, sampled at 170 Hz. Ground tracking data was gathered on S- and C-bands; Guam data is considered critical as it was taken during the blackout at the beginning of entry. The generation of a best estimate trajectory, combining flight and ground data, yields inplane parameters of altitude, flight path angle, and velocity for comparison with navigational instrumentation. Predicted lift/drag ratios were found to be accurate from Mach 1-25, and up to 23% low below Mach 1.

Compton, H. R.↗

NASA NTRS2015

Aerodynamic Models for the Low Density Supersonic Declerator (LDSD) Supersonic Flight Dynamics Test (SFDT)

An overview of pre-flight aerodynamic models for the Low Density Supersonic Decelerator (LDSD) Supersonic Flight Dynamics Test (SFDT) campaign is presented, with comparisons to reconstructed flight data and discussion of model updates. The SFDT campaign objective is to test Supersonic Inflatable Aerodynamic Decelerator (SIAD) and large supersonic parachute technologies at high altitude Earth conditions relevant to entry, descent, and landing (EDL) at Mars. Nominal SIAD test conditions are attained by lifting a test vehicle (TV) to 36 km altitude with a large helium balloon, then accelerating the TV to Mach 4 and and 53 km altitude with a solid rocket motor. The first flight test (SFDT-1) delivered a 6 meter diameter robotic mission class decelerator (SIAD-R) to several seconds of flight on June 28, 2014, and was successful in demonstrating the SFDT flight system concept and SIAD-R. The trajectory was off-nominal, however, lofting to over 8 km higher than predicted in flight simulations. Comparisons between reconstructed flight data and aerodynamic models show that SIAD-R aerodynamic performance was in good agreement with pre-flight predictions. Similar comparisons of powered ascent phase aerodynamics show that the pre-flight model overpredicted TV pitch stability, leading to underprediction of trajectory peak altitude. Comparisons between pre-flight aerodynamic models and reconstructed flight data are shown, and changes to aerodynamic models using improved fidelity and knowledge gained from SFDT-1 are discussed.

Van Norman, John W.↗

NASA NTRS1989

Image interpretation and prediction in microwave diversity imaging

The microwave image of a metallic object is interpreted from a point of view based on the understanding of the interconnection between the scattering mechanisms, the data acquisition system, and the image reconstruction algorithm. From this understanding it is possible to interpret and predict microwave images reconstructed from data collected over specified and angular windows. The connection between a special scattering mechanism, edge diffraction, and its reconstructed image is established. The microwave image of an edge is two bright points whose locations correspond to the end points of the edge if the normal aspect angle is not included in the angular windows; otherwise a line joining the two end points and representing the edge will appear in the image. Experimental images of a trihedral reflector constructed from data collected over different angular windows support this approach to image interpretation and prediction.

Li, Hsueh-Jyh↗

NASA NTRSDate not supplied

GT2024-128885: Flow Reconstruction in a Transonic Turbine Cascade using Physics-Informed Neural Networks (PINNs)

This presentation investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. PINNs are a machine learning method trained on losses calculated from reconstructed governing equations, assigned boundary/initial conditions, and measured data. We reconstruct the 2-D flow field in a transonic turbine cascade in two ways: the traditional forward approach (without training/experimental data) and by training the PINN using experimental data. We then compare the PINN solutions to measured data. This is repeated for three different turbine blades with distinct loading characteristics. The experimental data used for training is the static pressure measurements along the suction and pressure sides of each blade. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. It's shown that the PINN can reconstruct the flow field in all cases with acceptable errors. Cases where the PINN is trained on all the data, and even half the data, resulted in the lowest errors. The exit Mach number is inferred for each case and compared to the experimentally calculated value.

Machine Learning↗