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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 NTRS2022

Optimized Umkehr Profile Algorithm for Ozone Trend Analyses

The long-term record of Umkehr measurements from four NOAA Dobson spectrophotometers was reprocessed after updates to the instrument calibration procedures. In addition, a new data quality-control tool was developed for the Dobson automation software (WinDobson). This paper presents a comparison of Dobson Umkehr ozone profiles from NOAA ozone network stations (Boulder, OHP, MLO, Lauder) against several satellite records, including Aura Microwave Limb Sounder (MLS; ver. 4.2), and combined SBUV and OMPS records (NASA AGG and NOAA COH). A subset of satellite data is selected to match Dobson Umkehr observations at each station spatially (distance less than 200 km) and temporally (within 24 hours). Umkehr Averaging Kernels (AKs) are applied to vertically smooth all overpass satellite profiles prior to comparisons. The station Umkehr record consists of several instrumental records, which have different optical characterizations, and thus instrument-specific stray light contributes to the data processing errors and creates step changes in the record. This work evaluates the overall quality of Umkehr long-term measurements at NOAA ground-based stations and assesses the impact of the instrumental changes on the stability of the Umkehr ozone profile record. This paper describes a method designed to correct biases and discontinuities in the retrieved Umkehr profile that originate from the Dobson calibration process, repair, or optical realignment of the instrument. The M2GMI and GMI CTM ozone profile model output matched to station location and date of observation is used to evaluate instrumental step changes in the Umkehr record. Homogenization of the Umkehr record and discussion of the apparent stray light error in retrieved ozone profiles are the focus of this paper. Homogenization of ground-based records is of great importance for studies of long-term ozone trends and climate change.

Umkehr↗
NASA NTRS1991

Rapid near-optimal aerospace plane trajectory generation and guidance

Problems associated with onboard trajectory optimization, propulsion system cycle selection, and the synthesis of guidance laws are addressed for ascent to low earth orbit of an airbreathing, single-stage-to-orbit vehicle. A multicycle propulsion system is assumed that incorporates turbojet, ramjet, scramjet, and rocket engines. An energy state approximation is applied to a singularly perturbed, four-state dynamic model for flight of a point mass over a spherical nonrotating earth. An algorithm is then derived for generating both the fuel-optimal climb profile and the guidance commands required to follow that profile. In particular, analytic switching conditions are derived that, under appropriate assumptions, efficiently govern optimal transition from one propulsion cycle to another. The algorithm proves to be computationally efficient and suitable for real-time implementation. The paper concludes with the presentation of representative numerical results that illustrate the nature of the fuel-optimal climb paths and the tracking performance of the guidance algorithm.

Corban, J. E.↗
NASA NTRS1992

Selection of LCTV operating curves for input and filter

A liquid crystal television (LCTV) SLM's phase, amplitude, and polarization all influence our selection of operating curves for input and filter. With its continuum of drive voltage, the LCTV permits grey-level control in both locations. Selection of an optimum curve depends on expected variations in signal amplitude, presence of input scene structured noise, and other factors. Using modulators obtained from a commercially available projection LCTV, and with no specifically added input noise present, we have obtained laboratory results in which the ratio of peak intensity to correlator system noise exceeded 100:1. Unfortunately, it was massively inefficient to implement the algorithm which had been developed as the abstract for this paper was submitted. Fortunately, we have quite recently developed two insights that will speed it by several orders of magnitude, and we shall report that result in the future. We will also extend the work to include clutter objects and additive input scene noise.

Juday, Richard D.↗
NASA NTRS1992

Human-like agents with posture planning ability

Human body models are geometric structures which may be ultimately controlled by kinematically manipulating their joints, but for animation, it is desirable to control them in terms of task-level goals. We address a fundamental problem in achieving task-level postural goals: controlling massively redundant degrees of freedom. We reduce the degrees of freedom by introducing significant control points and vectors, e.g., pelvis forward vector, palm up vector, and torso up vector, etc. This reduced set of parameters are used to enumerate primitive motions and motion dependencies among them, and thus to select from a small set of alternative postures (e.g., bend vs. squat to lower shoulder height). A plan for a given goal is found by incrementally constructing a goal/constraint set based on the given goal, motion dependencies, collision avoidance requirements, and discovered failures. Global postures satisfying a given goal/constraint set are determined with the help of incremental mental simulation which uses a robust inverse kinematics algorithm. The contributions of the present work are: (1) There is no need to specify beforehand the final goal configuration, which is unrealistic for the human body, and (2) the degrees of freedom problem becomes easier by representing body configurations in terms of 'lumped' control parameters, that is, control points and vectors.

Jung, Moon R.↗
NASA NTRS1994

Neural net controller for inlet pressure control of rocket engine testing

Many dynamic systems operate in select operating regions, each exhibiting characteristic modes of behavior. It is traditional to employ standard adjustable gain proportional-integral-derivative (PID) loops in such systems where no apriori model information is available. However, for controlling inlet pressure for rocket engine testing, problems in fine tuning, disturbance accommodation, and control gains for new profile operating regions (for research and development) are typically encountered. Because of the capability of capturing I/O peculiarities, using NETS, a back propagation trained neural network is specified. For select operating regions, the neural network controller is simulated to be as robust as the PID controller. For a comparative analysis, the higher order moment neural array (HOMNA) method is used to specify a second neural controller by extracting critical exemplars from the I/O data set. Furthermore, using the critical exemplars from the HOMNA method, a third neural controller is developed using NETS back propagation algorithm. All controllers are benchmarked against each other.

Trevino, Luis C.↗
NASA NTRS1998

US Participation in the GOME and SCIAMACHY Projects

This report summarizes research done under NASA Grant NAGW-2541 through September 30, 1997. The research performed under this grant includes development and maintenance of scientific software for the GOME retrieval algorithms, consultation on operational software development for GOME, sensitivity and instrument studies to define GOME and SCIAMACHY instruments, consultation on optical and detector issues for both GOME and SCIAMACHY, consultation and development for SCIAMACHY near-real-time (NRT) and off-line (OL) data products, and development of infrared line-by-line atmospheric modeling and retrieval capability for SCIAMACHY. The European Space Agency selected the SAO to participate in GOME validation and science studies, part of the overall ERS AO. This provided access to all GOME data; The SAO activities that are carried out as a result of selection by ESA were funded by the present grant. The Global Ozone Monitoring Experiment was successfully launched on the ERS- 2 satellite on April 20, 1995, and remains working in normal fashion. SCIAMACHY is currently scheduled for launch in early 2000. The first two European ozone monitoring instruments (OMI), to fly on the q series of operational meteorological satellites being planned by Eumetsat, have been selected to be GOME-type instruments (the first, in fact, will be the refurbished GOME flight spare). K. Chance is the U.S. member of the OMI Users Advisory Group.

Chance, K. V.↗
NASA NTRS2009

Spatial Analysis of Environmental Factors Related to Lyme Disease in Alabama by Means of NASA Earth Observation Systems

This slide presentation reviews the epidemiology of Lyme Disease that accounts for more than 95% or vector borne diseases in the United States. The history, symptoms and the life cycle of the tick, the transmitting agent of Lyme Disease, a map that shows the cases reported to the CDC between1990 and 2006 and the number of cases in Alabama by year from 1986 to 2007. A NASA project is described, the goals of which are to (1) Demonstrate the presence of the chain of infection of Lyme disease in Alabama (2) Identify areas with environmental factors that support tick population using NASA Earth Observation Systems data in selected areas of Alabama and (3) Increase community awareness of Lyme disease and recommend primary and secondary prevention strategies. The remote sensing methods included: Analyzed Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and DigitalGlobe Quickbird satellite imagery from summer months and Performed image analyses in ER Mapper 7.1. Views from the ASTER and Quickbird land cover are shown, the Normalized Difference Vegetation Index (NDVI) algorithm was applied to all ASTER and Quickbird imagery. The use of the images to obtain the level of soil moisture is reviewed, and this analysis was used along with the NDVI, was used to identify the areas that support the tick population.

Renneboog, Nathan↗
NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided valuable insight on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus were segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution available for capturing cloud initiation, growth, and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool and its results for cloud fields sampled throughout CAMP2EX by the airborne P-3. Cloud tracks were selected from about 10 flights to form ensembles, specific groups of tracks occurring in a region with airborne sampling. Cloud lifecycle properties, including duration and maximum area, are calculated for all ensemble members, and analyzed for cloud behavior and P-3 coincidences. By following this strategy, we quantitatively assess the degree of airborne sampling for specific cloud classes defined by the lifecycle calculations. We can summarize which cloud classes had more sampling, the stage of development during sampling, and general differences in character (e.g., isolated congestus vs. cold-pool producer).

Cloud Tracking↗
NASA NTRS1992

External calibration of polarimetric radar images using distributed targets

A new technique is presented for calibrating polarimetric synthetic aperture radar (SAR) images using only the responses from natural distributed targets. The model for polarimetric radars is assumed to be X = cRST where X is the measured scattering matrix corresponding to the target scattering matrix S distorted by the system matrices T and R (in general T does not equal R(sup t)). To allow for the polarimetric calibration using only distributed targets and corner reflectors, van Zyl assumed a reciprocal polarimetric radar model with T = R(sup t); when applied for JPL SAR data, a heuristic symmetrization procedure is used by POLCAL to compensate the phase difference between the measured HV and VH responses and then take the average of both. This heuristic approach causes some non-removable cross-polarization responses for corner reflectors, which can be avoided by a rigorous symmetrization method based on reciprocity. After the radar is made reciprocal, a new algorithm based on the responses from distributed targets with reflection symmetry is developed to estimate the cross-talk parameters. The new algorithm never experiences problems in convergence and is also found to converge faster than the existing routines implemented for POLCAL. When the new technique is implemented for the JPL polarimetric data, symmetrization and cross-talk removal are performed on a line-by-line (azimuth) basis. After the cross-talks are removed from the entire image, phase and amplitude calibrations are carried out by selecting distributed targets either with azimuthal symmetry along the looking direction or with some well-known volume and surface scattering mechanisms to estimate the relative phases and amplitude responses of the horizontal and vertical channels.

Yueh, Simon H.↗
NASA NTRS2007

Advanced Control Algorithms for Compensating the Phase Distortion Due to Transport Delay in Human-Machine Systems

The desire to create more complex visual scenes in modern flight simulators outpaces recent increases in processor speed. As a result, simulation transport delay remains a problem. New approaches for compensating the transport delay in a flight simulator have been developed and are presented in this report. The lead/lag filter, the McFarland compensator and the Sobiski/Cardullo state space filter are three prominent compensators. The lead/lag filter provides some phase lead, while introducing significant gain distortion in the same frequency interval. The McFarland predictor can compensate for much longer delay and cause smaller gain error in low frequencies than the lead/lag filter, but the gain distortion beyond the design frequency interval is still significant, and it also causes large spikes in prediction. Though, theoretically, the Sobiski/Cardullo predictor, a state space filter, can compensate the longest delay with the least gain distortion among the three, it has remained in laboratory use due to several limitations. The first novel compensator is an adaptive predictor that makes use of the Kalman filter algorithm in a unique manner. In this manner the predictor can accurately provide the desired amount of prediction, while significantly reducing the large spikes caused by the McFarland predictor. Among several simplified online adaptive predictors, this report illustrates mathematically why the stochastic approximation algorithm achieves the best compensation results. A second novel approach employed a reference aircraft dynamics model to implement a state space predictor on a flight simulator. The practical implementation formed the filter state vector from the operator s control input and the aircraft states. The relationship between the reference model and the compensator performance was investigated in great detail, and the best performing reference model was selected for implementation in the final tests. Theoretical analyses of data from offline simulations with time delay compensation show that both novel predictors effectively suppress the large spikes caused by the McFarland compensator. The phase errors of the three predictors are not significant. The adaptive predictor yields greater gain errors than the McFarland predictor for short delays (96 and 138 ms), but shows smaller errors for long delays (186 and 282 ms). The advantage of the adaptive predictor becomes more obvious for a longer time delay. Conversely, the state space predictor results in substantially smaller gain error than the other two predictors for all four delay cases.

Guo, Liwen↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRS1991

NASA/ASEE Summer Faculty Fellowship Program. 1991 Research Reports

Reports from the NASA/ASEE Summer Faculty Fellowship Program are presented. The editors are responsible for selecting appropriately qualified faculty to address some of the many problems of current interest to NASA Kennedy. Some representative titles are as follows: Development of an Accelerated Test Method for the Determination of Susceptibility to Atmospheric Corrosion; Hazardous Gas Leak Analysis in the Space Shuttle; Modeling and Control of the Automated Radiator Inspection Device; Study of the Finite Element Software Packages at KSC; Multispectral Image Processing for Plants; Algorithms for Contours Depicting Static Electric Fields during Adverse Weather Conditions; Transient Study of a Cryogenic Hydrogen Filling System; and Precision Cleaning Verification of Nonvolatile Residues by using Water, Ultrasonics, and Turbidity Analyses.

Hosler, E. Ramon↗
NASA NTRS1992

Monthly mean global satellite data sets available in CCM history tape format

Satellite data for climate monitoring have become increasingly important over the past decade, especially with increasing concern for inadvertent antropogenic climate change. Although most satellite based data are of short record, satellites can provide the global coverage that traditional meteorological observations network lack. In addition, satellite data are invaluable for the validation of climate models, and they are useful for many diagnostic studies. Herein, several satellite data sets were processed and transposed into 'history tape' format for use with the Community Climate Model (CCM) modular processor. Only a few of the most widely used and best documented data sets were selected at this point, although future work will expand the number of data sets examined as well as update the archived data sets. An attempt was made to include data of longer record and only monthly averaged data were processed. For studies using satellite data over an extended period, it is important to recognize the impact of changes in instrumentation, drift in instrument calibration, errors introduced by retrieval algorithms and other sources of errors such as those resulting from insufficient space and/or time sampling.

Hurrell, James W.↗
NASA NTRS2017

Robust Targeting for the Smartphone Video Guidance Sensor

The Smartphone Video Guidance Sensor (SVGS) is a miniature, self-contained autonomous rendezvous and docking sensor developed using a commercial off the shelf Android-based smartphone. It aims to provide a miniaturized solution for rendezvous and docking, enabling small satellites to conduct proximity operations and formation flying while minimizing interference with a primary payload. Previously, the sensor was limited by a slow (2 Hz) refresh rate and its use of retro-reflectors, both of which contributed to a limited operating environment. To advance the technology readiness level, a modified approach was developed, combining a multi-colored LED target with a focused target-detection algorithm. Alone, the use of an LED system was determined to be much more reliable, though slower, than the retro-reflector system. The focused target-detection system was developed in response to this problem to mitigate the speed reduction of using color. However it also improved the reliability. In combination these two methods have been demonstrated to dramatically increase sensor speed and allow the sensor to select the target even with significant noise interfering with the sensor, providing millimeter level precision at a range of two meters with a 1U target.

Carter, C.↗
NASA NTRS2017

Robust Targeting for the Smartphone Video Guidance Sensor

The Smartphone Video Guidance Sensor (SVGS) is a miniature, self-contained autonomous rendezvous and docking sensor developed using a commercial off the shelf Android-based smartphone. It aims to provide a miniaturized solution for rendezvous and docking, enabling small satellites to conduct proximity operations and formation flying while minimizing interference with a primary payload. Previously, the sensor was limited by a slow (2 Hz) refresh rate and its use of retro-reflectors, both of which contributed to a limited operating environment. To advance the technology readiness level, a modified approach was developed, combining a multi-colored LED target with a focused target-detection algorithm. Alone, the use of an LED system was determined to be much more reliable, though slower, than the retro-reflector system. The focused target-detection system was developed in response to this problem to mitigate the speed reduction of using color. However, it also improved the reliability. In combination these two methods have been demonstrated to dramatically increase sensor speed and allow the sensor to select the target even with significant noise interfering with the sensor, providing millimeter level accuracy at a range of two meters with a 1U target.

Carter, Christopher↗
NASA NTRS2020

Wildfire Smoke Particle Properties and Evolution, from Space-Based Multi-Angle Imaging

Emitted smoke composition is determined by properties of the biomass burning source and ambient ecosystem. However, conditions that mediate the partitioning of black carbon (BC) and brown carbon (BrC) formation, as well as the spatial and temporal factors that drive particle evolution, are not understood adequately for many climate and air-quality related modeling applications. In situ observations provide considerable detail about aerosol microphysical and chemical properties, although sampling is extremely limited. Satellites offer the frequent global coverage that would allow for statistical characterization of emitted and evolved smoke, but generally lack microphysical detail. However, once properly validated, data from the National Aeronautics and Space Administration (NASA) Earth Observing System’s Multi-Angle Imaging Spectroradiometer (MISR) instrument can create at least a partial picture of smoke particle properties and plume evolution. We use in situ data from the Department of Energy’s Biomass Burning Observation Project (BBOP) field campaign to assess the strengths and limitations of smoke particle retrieval results from the MISR Research Aerosol (RA) retrieval algorithm. We then use MISR to characterize wildfire smoke particle properties and to identify the relevant aging factors in several cases, to the extent possible. The RA successfully maps qualitative changes in effective particle size, light absorption, and its spectral dependence, when compared to in situ observations. By observing the entire plume uniformly, the satellite data can be interpreted in terms of smoke plume evolution, including size-selective deposition, new-particle formation, and locations within the plume where BC or BrC dominates.

Noyes, Katherine Junghenn↗
NASA NTRS2011

Development of Jet Noise Power Spectral Laws

High-quality jet noise spectral data measured at the Aero-Acoustic Propulsion Laboratory (AAPL) at NASA Glenn is used to develop jet noise scaling laws. A FORTRAN algorithm was written that provides detailed spectral prediction of component jet noise at user-specified conditions. The model generates quick estimates of the jet mixing noise and the broadband shock-associated noise (BBSN) in single-stream, axis-symmetric jets within a wide range of nozzle operating conditions. Shock noise is emitted when supersonic jets exit a nozzle at imperfectly expanded conditions. A successful scaling of the BBSN allows for this noise component to be predicted in both convergent and convergent-divergent nozzles. Configurations considered in this study consisted of convergent and convergent- divergent nozzles. Velocity exponents for the jet mixing noise were evaluated as a function of observer angle and jet temperature. Similar intensity laws were developed for the broadband shock-associated noise in supersonic jets. A computer program called sJet was developed that provides a quick estimate of component noise in single-stream jets at a wide range of operating conditions. A number of features have been incorporated into the data bank and subsequent scaling in order to improve jet noise predictions. Measurements have been converted to a lossless format. Set points have been carefully selected to minimize the instability-related noise at small aft angles. Regression parameters have been scrutinized for error bounds at each angle. Screech-related amplification noise has been kept to a minimum to ensure that the velocity exponents for the jet mixing noise remain free of amplifications. A shock-noise-intensity scaling has been developed independent of the nozzle design point. The computer program provides detailed narrow-band spectral predictions for component noise (mixing noise and shock associated noise), as well as the total noise. Although the methodology is confined to single streams, efforts are underway to generate a data bank and algorithm applicable to dual-stream jets. Shock-associated noise in high-powered jets such as military aircraft can benefit from these predictions.

Khavaran, Abbas↗
NASA NTRS1983

Autonomous earth feature classification - Shuttle and aircraft flight test results

The Feature Identification and Location Experiment (FILE) flown on the Shuttle STS-2 mission November 12-14, 1981, tested a technique for autonomous real-time classification of selected earth features, i.e., water; bare land; vegetation; and clouds, snow, and ice. A second instrument, designed for aircraft flights, flew over regions of the west and east coasts of the United States and across the country. In each instrument, two bore-sighted CCD cameras image earth scenes in two spectral bands. Each camera includes a 100-element by 100-element detector array, and classification circuits. A simple algorithm and logic circuit provides classification decisions within a few microseconds. The experiment records the number of picture elements (pixels) representing each feature and the reflected solar radiation for each band. After flight, pixel-by-pixel classification images are constructed and compared with 70-mm color photographs taken simultaneously with the CCD-camera data.

Sivertson, W. E., Jr.↗