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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Position Error Covariance Matrix Validation and Correction

In order to calculate operationally accurate collision probabilities, the position error covariance matrices predicted at times of closest approach must be sufficiently accurate representations of the position uncertainties. This presentation will discuss why the Gaussian distribution is a reasonable expectation for the position uncertainty and how this assumed distribution type is used in the validation and correction of position error covariance matrices.

Frisbee, Joe, Jr.↗
NASA NTRS2014

Smooth Pursuit of Flicker-Defined Motion

We examined the pursuit response to stimuli defined by space-variant flicker of a dense random dot carrier pattern. On each frame, every element of the pattern could change polarity, with a probability given by a two-dimensional Gaussian distribution. A normal distribution produces a circular region of twinkle, while inverting the distribution results in a spot of static texture in a twinkling surround. In this latter case, the carrier texture could be stationary, or could move with the twinkle modulator,thereby producing first-order motion in the region of the spot. While the twinkle-defined spot produces a strong sensation of motion, the complementary stimulus defined by the absence of twinkle does not;when viewed peripherally, it appears to move in steps even when the generating distribution moves smoothly. We examined pursuit responses to these stimuli using two techniques: 1) the eye movement correlogram, obtained by cross-correlating eye velocity with the velocity of a randomly-moving stimulus; and 2) delayed visual feedback, where transient stabilization of a target can produce spontaneous oscillations of the eye, with a period empirically observed to vary linearly with the applied delay. Both techniques provide an estimate of the internal processing time, which can be as short as100 milliseconds for a first-order target. Assessed by the correlogram method, the response to flicker defined motion is delayed by more than 100 milliseconds, and significantly weaker (especially in the vertical dimension). When initially presented in the delayed feedback condition, purely saccadic oscillation is observed. One subject eventually developed smooth oscillations (albeit with significant saccadic intrusions), showing a period-versus-delay slope similar to that observed for first-order targets. This result is somewhat surprising, given that we interpret the slope of the period-versus-delayfunction as reflecting the balance between position- and velocity-sensitive inputs to pursuit.

smooth pursuit↗
NASA NTRSDate not supplied

Simulations of a Turbulent Flow Subjected to Favorable and Adverse Pressure Gradients

This paper reports the results from a direct numerical simulation of an initially turbulent boundary layer passing over a wall-mounted “speed bump” geometry. The speed bump, represented in the form of a Gaussian distribution profile, generates a favorable pressure gradient region over the upstream half of the geometry, followed by an adverse pressure gradient over the downstream half. The boundary layer approaching the bump undergoes strong acceleration in the favorable pressure gradient region before experiencing incipient or very weak separation within the adverse pressure gradient region. These types of flows have proven to be particularly challenging to predict using lower-fidelity simulation tools based on various turbulence modeling approaches and warrant the use of the highest-fidelity simulation techniques. Simulation results are utilized to examine the key phenomena present in the flowfield, such as relaminarization/stabilization in the strong acceleration region succeeded by retransition to turbulence near the onset of adverse pressure gradient, incipient/weak separation, and development of internal layers where the sense of streamwise pressure gradient changes at the foot, apex and tail of the bump. The present direct numerical simulation is performed using a flow solver developed exclusively for graphics processing units, which is found to provide a significant speedup compared to an earlier solver optimized for central processing unit architectures.

Ali Uzun↗
NASA NTRS2021

Loss of Control Detection for Commercial Transports Using Conditional Variational Autoencoders

This work describes a detector for the loss of control of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. We demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is experiencing a flight state change is approaching by measuring a shift in the sampling Gaussian distributions of the latent space. We provide an analysis of its applicability to flight data from a NASA generic commercial transport-type aircraft.

Newton H Campbell↗
NASA NTRS2001

Capacity and capacity sensitivity of soft output optical channels

In this paper, we derive the capacity of pulse position modulation (PPM) on a general soft output, memoryless channel, and evaluate the capacity formula for a variety of optical channel models, including AWGN, webb (1), and Webb plus Gaussian distributions.

capacity↗
NASA NTRSDate not supplied

Estimation With Range Depended Sensor Model

This paper focuses on the improvement of object detection accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. The tracking problem is considered in a mixed continuous-discrete time domain, where the target dynamics is in continuous-time and the measurements are in discrete-time, which is an optimal choice in many tracking and navigation applications. We adopt a commonly used continuous time coordinate-uncoupled white-noise acceleration model for a point object to describe the target motion, and use Extended Kalman Filter (EKF) framework to estimate the target's state and the unknown proportionality coefficient.

Vahram Stepanyan↗
NASA NTRSDate not supplied

Influence of Thermophysical Property Variability on Thermal Predictions of Ti-6Al-4V

The additive manufacturing (AM) industry has experienced rapid growth in recent decades as industrial interest in the process has grown. Because of this, there are many groups interested in simulations of the AM process. However, the influence of thermophysical property variability on the melt pool geometry during processing of various AM alloys is unclear. The goal of my work at NASA Langley Research Center (LaRC) was to characterize this influence on Ti-6Al-4V during AM processing alongside developing a tool to enable equivalent studies on other AM materials. To facilitate this process, a database tool was developed to contain and organize thermophysical data previously reported by primary sources. The specific thermophysical properties of interest for this study were density, specific heat capacity, and conductivity. Thermal diffusivity was calculated using the other three properties. This data was then fit with a Gaussian distribution and then randomly sampled from using Monte Carlo random value sampling. Using the Rosenthal equation, this sample data was used to simulate the temperature field during additive manufacturing and extract the melt pool geometry. Specifically, the melt pool’s length, width, and depth. This process was repeated an arbitrary number of times, with the default being 1000. Once this information was obtained, histograms were made showing the distributions of the sizes of the melt pool’s length, width, and depth. Representative statistical metrics of the distributions were calculated (mean, standard deviation, and coefficient of variance). This work found that at the simulated processing values, the melt pool’s width and depth had a 5.7% variation while the length had a 3.2% variation. Additionally, a tool was constructed to graph a thermal color map representation of the melt pool for specific, arbitrary values of density, specific heat, and conductivity. This tool allowed for more efficient plotting of individual queries of the thermophysical properties. The results that were observed in the research were that values reported in literature vary and this variance can have a significant impact on the simulation. Predictions of the melt pool’s dimensions show that length has a standard deviation of approximately 6 μm, width has a standard deviation of approximately 5 μm, and depth can vary by approximately 3 μm. Considering the mean sizes of length, width, and depth are 185 μm, 88 μm, and 44 μm respectively, such a deviation is significant. This shows that depth, for example, could be more than 10% larger or smaller than expected. This demonstrates that variability in the reported thermophysical properties are not negligible and should be expected to have an influence on the results of the laser powder bed fusion additive manufacturing process. When simulating this process in the future, measures should be taken to account for this discrepancy and the uncertainty involved

Justin Martin↗
NASA NTRSDate not supplied

Estimation With Range Dependent Sensor Model

This paper focuses on the improvement of target tracking accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. We present two approaches to tracking a target with constant velocity model. The first approach uses Extended Kalman Filter (EKF) framework to estimate target's states and the unknown proportionality coefficient with linearization on the observation model on the predicted states at each time step. The second one uses a measurement conversion method to estimate the target's scaled by the unknown constant term states, which is shown to be unbiased. This conversion results in linear state and observation models, hence the standard Kalman filter can be applied. The actual target state is computed by application of back scaling with the estimate of unknown scale factor. We evaluate the approaches in desktop simulations.

Target tracking↗
NASA NTRS2023

Characterization of Errors in Satellite-Based HCHO/NO 2 Tropospheric Column Ratios With Respect to Chemistry, Column-to-PBL Translation, Spatial Representation, and Retrieval Uncertainties

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO 2 ) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO 2 + RO 2 (LRO x ) to the chemical loss of NO x (LNO x ). Subsequently, we challenge the predictive power of HCHO/NO 2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LRO x /LNO x . FNRs show a strongly linear (R 2 =0.94) relationship with LRO x /LNO x , but only on the logarithmic scale. Following the baseline (i.e., ln(LRO x /LNO x ) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NO x -sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the RO x –HO x cycle. We calculate high ozone production rates (PO 3 ) dominated by large HCHO × NO 2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO 3 and HCHO × NO 2 becomes more pronounced when moving towards NO x -sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO 2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO 2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km 2 ). A pixel with a grid size of 216 km 2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO 2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO 2 columns, should be above 1.2–1.5 × 10 16 molec. cm −2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

Amir H. Souri↗
NASA NTRS2024

Comparative Analysis of Type III Radio Bursts and Solar Flares: Spatial Localization and Correlation with Solar Flare Intensity

We present a comprehensive study of type III radio bursts and their association with solar flares of magnitude M1.0 and larger, as observed by four widely separated spacecraft (Parker Solar Probe, Solar Orbiter, STEREO-A, and Wind). Our main focus is the introduction and validation of two methods for localizing radio bursts using the available multispacecraft data. The first method utilizes intensity fitting with a circular Gaussian distribution, while the second method is based on the time arrival of radio bursts. We demonstrate the effectiveness of these methods through the analysis of a single type III burst event and compare their results with the traditional radio triangulation technique. Furthermore, we conduct a statistical study of 17 type III bursts associated with M- and X-class solar flares in years 2020–2022. Our findings suggest a possible correlation between solar flare intensities and longitudes, with east limb flares tending to be weaker than west limb flares. We also observe a systematic drift of radio burst longitudes toward the east, potentially explained by a poleward component of the local density gradient. Our results suggest a strong correlation between solar flare intensities and radio burst properties, enhancing our understanding of the relationship between solar flares and type III radio bursts.

Radio astronomy↗
DOE OSTI2021

Structure Perception in 3D Point Clouds: Preprint

Understanding human perception is critical to the design of ef- fective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the liter- ature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projec- tions of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rota- tion, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under ro- tation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns per- ceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.

data analysis↗
DOE OSTI2022

Cybersecurity assessment and risk management tool

Techniques and apparatuses are described for a cybersecurity risk management tool to assess cybersecurity risk and prioritize cybersecurity correction plans. The cybersecurity risk management tool categorizes cybersecurity framework security controls into maturity indicator levels, identifies implementation states achieved by an entity with respect to the cybersecurity framework security controls, and determines which of the maturity indicator levels represents the implementation state achieved by the entity with respect to each of the cybersecurity framework security controls. A cost-benefit analysis for modifying from the implementation state achieved by the entity to a next implementation state to be achieved by the entity with respect to the cybersecurity framework security controls is also enabled. The cost-benefit analysis leverages factored weights including aspects indicative of security perspectives, Gaussian distributions, and the maturity indicator levels.

Gourisetti, Sri Nikhil Gupta↗
DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a novel stability analysis method and provide sufficient conditions of DMM's stochastic stability. The proposed stability analysis is based on the contraction of probabilistic maps modeled by deep neural networks. We make connections between the spectral properties of neural network's weights and different types of used activation function on the stability and overall dynamic behavior of DMMs with Gaussian distributions. Based on the theory, we propose a few practical methods for designing constrained DMMs with guaranteed stability. We empirically substantiate our theoretical results via intuitive numerical experiments using the proposed stability constraints.

Drgona, Jan↗
NASA NTRS2010

Distributed Prognostic Health Management with Gaussian Process Regression

Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

Saha, Sankalita↗
DOE OSTI2024

Beam Scattering Through Foil at NSRL

In this note, we describe the foil structure that is used in the NASA Space Radiation Laboratory (NSRL) transport line just before D6 septum magnet. Foils of different materials and thickness are used through which different ion species at different energies pass. Foil removes the electrons from the incoming ions and make it fully stripped (or partially stripped is also possible). Further, when charged particles pass through a foil, the outgoing particles form a Gaussian like angular distribution. This Gaussian like distribution passes through a set of octupole magnets creates a uniform beam distribution at the NSRL target which is required for various beam experiments. We utilize the Bmad and SRIM packages to calculate the energy loss through the foils for various ion species at different energies and charge states. Finally, we provide a summary of the energy loss calculations through the foils using these two different methods.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Unsteady aerodynamic loads on pitching aerofoils represented by Gaussian body force distributions

The actuator line model (ALM) is an approach commonly used to represent lifting and dragging devices like wings and blades in large-eddy simulations (LES). The crux of the ALM is the projection of the actuator point forces onto the LES grid by means of a Gaussian regularisation kernel. The minimum width of the kernel is constrained by the grid size; however, for most practical applications like LES of wind turbines, this value is an order of magnitude larger than the optimal value that maximises accuracy. This discrepancy motivated the development of corrections for the actuator line, which, however, neglect the effect of unsteady spanwise shed vorticity. In this work we develop a model for the impact of spanwise shed vorticity on the unsteady loading of an aerofoil modelled as a Gaussian body force distribution, where the model is applicable within the regime of unsteady attached flow. The model solution is derived both in the time and frequency domain and features an explicit dependence on the Gaussian kernel width. We verify the model with ALM-LES for both pitch steps and periodic pitching. The model solution is compared with Theodorsen theory and validated with both computational fluid dynamics using body fitted grids and experiment. It is concluded that the optimal kernel width for unsteady aerodynamics is approximately 40 % of the chord. The ALM is able to predict the magnitude of the unsteady loading up to a reduced frequency of 𝑘 ≈ 0.2.

17 WIND ENERGY↗
NASA NTRS1989

The area of isodensity contours in cosmological models and galaxy surveys

The contour crossing statistic, defined as the mean number of times per unit length that a straight line drawn through the field crosses a given contour, is applied to model density fields and to smoothed samples of galaxies. Models in which the matter is in a bubble structure, in a filamentary net, or in clusters can be distinguished from Gaussian density distributions. The shape of the contour crossing curve in the initially Gaussian fields considered remains Gaussian after gravitational evolution and biasing, as long as the smoothing length is longer than the mass correlation length. With a smoothing length of 5/h Mpc, models containing cosmic strings are indistinguishable from Gaussian distributions. Cosmic explosion models are significantly non-Gaussian, having a bubbly structure. Samples from the CfA survey and the Haynes and Giovanelli (1986) survey are more strongly non-Gaussian at a smoothing length of 6/h Mpc than any of the models examined. At a smoothing length of 12/h Mpc, the Haynes and Giovanelli sample appears Gaussian.

Ryden, Barbara S.↗
NASA NTRS2013

Incorporating Skew into RMS Surface Roughness Probability Distribution

The standard treatment of RMS surface roughness data is the application of a Gaussian probability distribution. This handling of surface roughness ignores the skew present in the surface and overestimates the most probable RMS of the surface, the mode. Using experimental data we confirm the Gaussian distribution overestimates the mode and application of an asymmetric distribution provides a better fit. Implementing the proposed asymmetric distribution into the optical manufacturing process would reduce the polishing time required to meet surface roughness specifications.

Stahl, Mark T.↗