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NASA NTRS2024

Artemis I Optical Navigation System Performance

This paper summarizes the assessment of the Optical Navigation Flight Test Objective (FTO) during the flight of Artemis I. The Optical Navigation (OpNav) System was tested under a variety of range, target, and lighting conditions to evaluate the performance compared to the pre-flight predicted error models. In general, OpNav performed very well – successfully processing over a thousand images of starfields, Earth, and Moon. The performance of the algorithm when processing Moon images matched the pre-flight expected error models. The errors when processing Earth images were notably higher than the pre-flight models predicted, however this was found to be due to an over-estimation of the atmosphere bias used in the tuning of the algorithm. After the bias was re-tuned and the images reprocessed, performance significantly improved.

GNC↗

NASA NTRS2002

Estimating Single-Trial Responses in EEG

Accurate characterization of single-trial field potential responses is critical from a number of perspectives. For example, it allows differentiation of an evoked response from ongoing EEG. We previously developed the multiple component Event Related Potential (mcERP) algorithm to improve resolution of the single-trial evoked response. The mcERP model states that multiple components, each specified by a stereotypic waveform varying in latency and amplitude from trial to trial, comprise the evoked response. Application of the mcERP algorithm to simulated data with three independent, synthetic components has shown that the model is capable of separating these components and estimating their variability. Application of the model to single trial, visual evoked potentials recorded simultaneously from all V1 laminae in an awake, fixating macaque yielded local and far-field components. Certain local components estimated by the model were distributed in both granular and supragranular laminae. This suggests a linear coupling between the responses of thalamo-recipient neuronal ensembles and subsequent responses of supragranular neuronal ensembles, as predicted by the feedforward anatomy of V1. Our results indicate that the mcERP algorithm provides a valid estimation of single-trial responses. This will enable analyses that depend on trial-to-trial variations and those that require separation of the evoked response from background EEG rhythms

Shah, A. S.↗

NASA NTRS2006

Comparison of NASA Team2 and AES-York Ice Concentration Algorithms Against Operational Ice Charts From the Canadian Ice Service

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)- York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100%. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS'S estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS'S estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations.

Shokr, Mohammed↗

NASA NTRS2006

Ion Thruster Discharge Performance Per Magnetic Field Topography

DC-ION is a detailed computational model for predicting the plasma characteristics of rain-cusp ion thrusters. The advanced magnetic field meshing algorithm used by DC-ION allows precise treatment of the secondary electron flow. This capability allows self-consistent estimates of plasma potential that improves the overall consistency of the results of the discharge model described in Reference [refJPC05mod1]. Plasma potential estimates allow the model to predict the onset of plasma instabilities, and important shortcoming of the previous model for optimizing the design of discharge chambers. A magnetic field mesh simplifies the plasma flow calculations, for both the ions and the secondary electrons, and significantly reduces numerical diffusion that can occur with meshes not aligned with the magnetic field. Comparing the results of this model to experimental data shows that the behavior of the primary electrons, and the precise manner of their confinement, dictates the fundamental efficiency of ring-cusp. This correlation is evident in simulations of the conventionally sized NSTAR thruster (30 cm diameter) and the miniature MiXI thruster (3 cm diameter).

plasma processes↗

NASA NTRS2013

Mass Conservation and Positivity Preservation with Ensemble-type Kalman Filter Algorithms

Maintaining conservative physical laws numerically has long been recognized as being important in the development of numerical weather prediction (NWP) models. In the broader context of data assimilation, concerted efforts to maintain conservation laws numerically and to understand the significance of doing so have begun only recently. In order to enforce physically based conservation laws of total mass and positivity in the ensemble Kalman filter, we incorporate constraints to ensure that the filter ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. We show that the analysis steps of ensemble transform Kalman filter (ETKF) algorithm and ensemble Kalman filter algorithm (EnKF) can conserve the mass integral, but do not preserve positivity. Further, if localization is applied or if negative values are simply set to zero, then the total mass is not conserved either. In order to ensure mass conservation, a projection matrix that corrects for localization effects is constructed. In order to maintain both mass conservation and positivity preservation through the analysis step, we construct a data assimilation algorithms based on quadratic programming and ensemble Kalman filtering. Mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate constraints. Some simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. The results show clear improvements in both analyses and forecasts, particularly in the presence of localized features. Behavior of the algorithm is also tested in presence of model error.

EnKR↗

NASA NTRS2005

Optimal Prediction of Clocks from Finite Data

This talk is about optimal linear prediction of processes with stationary dth increments, which serve as a class of models for random clock disturbances. The predictor is obtained by orthogonal projection on the affine space of estimators whose errors are invariant to additive polynomials of degree < d. The projection conditions give a system of linear equations thatcan be solved straightforwardly for the regression coefficients. If the data are equally spaced, then the predictor can be obtained by an extension of Levinson's algorithm.

stationary increments↗

NASA NTRS2014

Fine Particulate Matter Predictions Using High Resolution Aerosol Optical Depth (AOD) Retrievals

To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM(sub 2.5) concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations, with out-of- sample R(sub 2) of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM(sub 2.5) levels.

aerosol optical depth↗

NASA NTRS1993

PNS predictions of axisymmetric hypersonic blunt-body and afterbody flowfields

A new space-marching full-body PNS algorithm capable of treating the complete blunt-body and afterbody flowfields over typical wide-bodied configurations is developed and demonstrated. A hybrid differencing scheme involving Flux-Vector Splitting (FVS) across embedded shocks and flowfield discontinuities, and central differencing in smooth (shock-free) regions is used. It is demonstrated that this new full-body PNS scheme can be marched from the spherical stagnation point over the entire body using bow-shock capturing, and provides an efficient and effective way for predicting blunt-body flowfields for various reentry application. The Mach 20 flow over a 30-deg sphere cone is predicted and comparisons are made with an existing VSL scheme for the blunt-body region and an existing PNS scheme for the conical afterbody region. The predicted flowfield and surface-measurable quantities are in excellent agreement, and demonstrate the accuracy and efficiency of the new full-body PNS scheme.

Bhutta, Bilal A.↗

NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗

NASA NTRS2013

Prediction of Ablation Rates from Solid Surfaces Exposed to High Temperature Gas Flow

A mathematical model and a solution algorithm is developed to study the physics of high temperature heat transfer and material ablation and identify the problems associated with the flow of hydrogen gas at very high temperatures and velocities through pipes and various components of Nuclear Thermal Rocket (NTR) motors. Ablation and melting can be experienced when the inner solid surface of the cooling channels and the diverging-converging nozzle of a Nuclear Thermal Rocket (NTR) motor is exposed to hydrogen gas flow at temperatures around 2500 degrees Kelvin and pressures around 3.4 MPa. In the experiments conducted on typical NTR motors developed in 1960s, degradation of the cooling channel material (cracking in the nuclear fuel element cladding) and in some instances melting of the core was observed. This paper presents the results of a preliminary study based on two types of physics based mathematical models that were developed to simulate the thermal-hydrodynamic conditions that lead to ablation of the solid surface of a stainless steel pipe exposed to high temperature hydrogen gas near sonic velocities. One of the proposed models is one-dimensional and assumes the gas flow to be unsteady, compressible and viscous. An in-house computer code was developed to solve the conservations equations of this model using a second-order accurate finite-difference technique. The second model assumes the flow to be three-dimensional, unsteady, compressible and viscous. A commercial CFD code (Fluent) was used to solve the later model equations. Both models assume the thermodynamic and transport properties of the hydrogen gas to be temperature dependent. In the solution algorithm developed for this study, the unsteady temperature of the pipe is determined from the heat equation for the solid. The solid-gas interface temperature is determined from an energy balance at the interface which includes heat transfer from or to the interface by conduction, convection, radiation, and ablation. Two different ablation models are proposed to determine the heat loss from the solid surface due to the ablation of the solid material. Both of them are physics based. Various numerical simulations were carried out using both models to predict the temperature distribution in the solid and in the gas flow, and then predict the ablation rates at a typical NTR motor hydrogen gas temperature and pressure. Solid mass loss rate per foot of a pipe was also calculated from these predictions. The results are presented for fully developed turbulent flow conditions in a sample SS pipe with a 6 inch diameter.

Akyuzlu, Kazim M.↗

NASA NTRS1997

Registration assisted mosaic generation

This paper presents a general strategy for assembling mosaics from numerous individual images where uncertainty exists in the position and orientation of those images. Both of the presented applications relate to remotely operated camera platforms, the first being the Galileo solid state imaging (SSI) camera presently in orbit around Jupiter, and the second being the Imager for Mars Pathfinder (IMP) stereo camera on Mars. A basic strategy in both applications is to determine the correct relative camera pointing followed by direct map projection of the images. It is assumed that approximate camera pointing exists sufficient to locate adjacent images and to place initial tiepoints within reach of the correlator. Spatial correlation is used to fix tiepoints whose initial locations are predicted by the camera pointing. We use either an fast fourier transform (fft) algorithm or a variant of Gruen's scheme permitting limited image rotation and skew. The Gruen correlator has three hierarchical modes: 1) A classical spatial least squares correlation on integral pixel boundaries used when rotation is small. 2) An annealing non-deterministic search used when rotations are unknown. A simplex deterministic search used for the end game. The correlation operation can be performed either interactively or autonomously. The final camera pointing solution relies upon a simplex downhill search in 2n or 3n dimensions where n is the number of images comprising the mosaic and the objective function to be minimized is the disagreement between tiepoint locations predicted from the camera pointing with those observed by the correlator. For Galileo the 3n unknowns are euler angles defining camera pointing in planet coordinates, and for Mars Pathfinder they are 2n unknowns representing commanded azimuth and elevation in the Lander coordinate system.

Lorre, Jean J.↗

NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗

NASA NTRS1992

Unsteady turbomachinery flow simulations on massively parallel architectures

The accurate numerical simulation of unsteady, three-dimensional viscous flow in turbomachines is computationally very intensive, requiring prohibitively large amounts of computer time on current vector supercomputers. In recent years, computer systems based on massively parallel architectures have been developed that offer the promise of meeting the computational power requirements of such large-scale simulations. However, a rethinking of existing algorithms and methodology is required in order to fully harness the computational power of such architectures. In this paper the capabilities of the Connection Machine (CM-2) in predicting unsteady flows in turbomachines are evaluated. The implementation on the CM-2 of an implicit, time-accurate, zonal algorithm for the Navier-Stokes equations in two dimensions is described. Programming issues and modifications made to the original algorithm (developed for vector, pipelined supercomputers) in order to improve performance on the CM-2 are outlined. Algorithm performance is evaluated and compared with a functionally equivalent code for the CRAY-YMP.

Madavan, N. K.↗

NASA NTRS1994

A Feasibility Study for Perioperative Ventricular Tachycardia Prognosis and Detection and Noise Detection Using a Neural Network and Predictive Linear Operators

To locate the accessory pathway(s) in preexicitation syndromes, epicardial and endocardial ventricular mapping is performed during anterograde ventricular activation via accessory pathway(s) from data originally received in signal form. As the number of channels increases, it is pertinent that more automated detection of coherent/incoherent signals is achieved as well as the prediction and prognosis of ventricular tachywardia (VT). Today's computers and computer program algorithms are not good in simple perceptual tasks such as recognizing a pattern or identifying a sound. This discrepancy, among other things, has been a major motivating factor in developing brain-based, massively parallel computing architectures. Neural net paradigms have proven to be effective at pattern recognition tasks. In signal processing, the picking of coherent/incoherent signals represents a pattern recognition task for computer systems. The picking of signals representing the onset ot VT also represents such a computer task. We attacked this problem by defining four signal attributes for each potential first maximal arrival peak and one signal attribute over the entire signal as input to a back propagation neural network. One attribute was the predicted amplitude value after the maximum amplitude over a data window. Then, by using a set of known (user selected) coherent/incoherent signals, and signals representing the onset of VT, we trained the back propagation network to recognize coherent/incoherent signals, and signals indicating the onset of VT. Since our output scheme involves a true or false decision, and since the output unit computes values between 0 and 1, we used a Fuzzy Arithmetic approach to classify data as coherent/incoherent signals. Furthermore, a Mean-Square Error Analysis was used to determine system stability. The neural net based picking coherent/incoherent signal system achieved high accuracy on picking coherent/incoherent signals on different patients. The system also achieved a high accuracy of picking signals which represent the onset of VT, that is, VT immediately followed these signals. A special binary representation of the input and output data allowed the neural network to train very rapidly as compared to another standard decimal or normalized representations of the data.

Moebes, T. A.↗

NASA NTRSDate not supplied

Fusing GeoNEX and VIIRS Surface BRDF Retrievals: Exploring a GEO-LEO Synergy

The Bidirectional Reflectance Distribution Function or BRDF, which describes the dependency of surface reflectance on the illumination-view geometries, are the foundation of many high-level satellite products for terrestrial and aquatic system monitoring. The latest geostationary sensors like GOES ABI provide high frequent (~10 minutes) observations of the Earth surface that feature continuously changing sun angles, allowing us to retrieve surface BRDF with dedicated atmospheric correction algorithms like MAIAC (Multi-Angle Implementation of Atmospheric Correction). For mid-latitude locations, because geostationary satellites have fixed view angles in the back-scattering directions, the angular sampling of surface BRDF by GEO sensors is not comprehensive. This study explores a GEO-LEO synergy to address this issue. We first extract concurrent GeoNEX and VIIRS BRDF data with the best quality (cloud-free and low aerosol loading) at chosen AERONET sites. We then compare the magnitude and the shape factors of the two set of BRDF parameters as well as their variations through the season. We calculate the “distances” between the GeoNEX and VIIRS BRDF by using them to cross-predict the top-of-atmosphere reflectance measured by their counterpart and evaluating the corresponding prediction errors. This metric allows us to derive a set of optimized BRDF parameters that minimize such distances or prediction errors, which are considered as the fused BRDF result. We validate the algorithm with reserved AERONET data and then apply it to generate the GEO-LEO BRDF synergy over CONUS. We expect the fused BRDF to have reduced uncertainties as compared to the source GeoNEX or VIIRS data and may find broadly application in deriving other high-level satellite products.

Geostationary satellite↗

NASA NTRS1994

An algorithm for forecasting mountain wave-related turbulence in the stratosphere

A global mountain wave parameterization for prediction of wave-related displacements and turbulence is described. The parameterization is used with input from National Meteorological Center (NMC) analyses of wind and temperature to examine small-scale disturbances encountered by the National Aeronautics and Space Administration high-altitude ER-2 during the Second Airborne Arctic Stratosphere Experiment (AASE-II). The magnitude and location of observed large wave events are well reproduced. A strong correlation is suggested between patches of moderate turbulence encountered by the ER-2 and locations where breaking mountain waves are predicted by the parameterization. These facts suggest that useful forecasts of global mountain wave activity, including wave-related clear-air turbulence, can be made quickly and inexpensively using our mountain wave parameterization with input from current numerical forecast models.

Bacmeister, Julio T.↗

NASA NTRS2006

An Intercomparison of Microphysical Retrieval Algorithms for Upper Tropospheric Ice Clouds

The large horizontal extent, location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the earth's radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud scale processes in large-scale models and for accurately predicting the earth's future climate. A number of passive and active remote sensing retrieval algorithms exist for estimating the microphysical properties of upper tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade, however, there is room for improvement. Members of the Atmospheric Radiation measurement program (ARM) Cloud properties Working Group are involved in an intercomparison of optical depth(tau), ice water path, and characteristic particle size in clouds retrieved using ground-based instruments. The goals of this intercomparison are to evaluate the accuracy of state-of-the-art algorithms, quantify the uncertainties, and make recommendations for improvement.

Comstock, Jennifer M.↗

NASA NTRS1984

Nimbus-Earth radiation budget instrument analysis

The Wide Field Of View (WFOV) Earth Flux Channels of the Nimbus Earth Radiation Budget (ERB) instrument are studied with the objective of improving the understanding and confidence in the data. Laboratory tests on flight space Earth flux subassemblies followed by a set of in flight verification procedures are examined to see if the laboratory derived correction factors produced consistent results. Intercomparisons between the WFOV data and integrated scanner data as well as other truth are used in these verificaton procedures. The main source of errors are temperature and temperature gradient related offsets. The findings led to construction of a model for the short wave WFOV channel which use instrument temperatures and temperature differences to predict or establish offsets which need to be applied to data reduction algorithms for maximum final data product accuracy.

Maschhoff, R. M.↗