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Results for “stochastic reconstruction”

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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Stochastic Reconstruction of Thermal Protection Material Properties from Arc-Jet Experiments

Material response models are used to assess reliability using variances in the bond-line temperature predictions based on uncertainties in trajectory, aerothermal environment, and material properties. A key deficiency in the current approach is that input uncertainties are too often subjective, empirical, or ad-hoc, and are not rigorously linked to the arc-jet test data used to develop the TPS material model. While materials such as PICA are well understood, future missions may require more novel materials such as HEEET where unknown uncertainties have real consequences on the ability to assess reliability. A quantifiable estimate of reliability requires an iterative methodology where the parameters driving the variance in bond-line temperature (for example) are systematically identified. A test campaign to collect data or develop new models can then be identified to reduce those input uncertainties. A Bayesian inference loop defines these connections mathematically, i.e., prior knowledge about uncertainty is updated based on observation. While these concepts are well known (and often applied intuitively in a non-rigorous approach), only recent advances in reduced-order modelling have made them computationally viable methods for engineering. By replacing deterministic inverse methods with stochastic approaches, the hope is new materials proposed for future missions can more rapidly be developed with a greater understanding of the TPS material reliability. Two additional steps for the analysis of arc jet test data are discussed. The first is ability to construct a reduced-order model using material response simulations (Icarus/US3D) of the arc-jet test articles, and the second is the inclusion of this surrogate model in the Bayesian inversion process. Both capabilities will be demonstrated using prior PICA arc-jet test data. The quality of a surrogate model will be investigated and the variances on the calibrated material properties will be compared to our current understanding of the PICA material model.

Material response↗
NASA NTRS1994

Improved image decompression for reduced transform coding artifacts

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem which can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality.

Orourke, Thomas P.↗
NASA NTRS2009

Trajectory Reconstruction of the ST-9 Sounding Rocket Experiment Using IMU and Landmark Data

This paper presents trajectory reconstruction of the ST-9 sounding rocket experiment using the onboard IMU data and descent imagery. The raw IMU accelerometer measurements are first converted into inertial acceleration and then used in trajectory integration. The descent images are pre-processed using a map-matching algorithm and unique landmarks for each image are created. Using the converted IMU data and descent images, the result from dead-reckoning and the kinematic-fix approaches are first compared with the GPS measurements. Then, both the IMU data and landmarks are processed together using a batch least-squares filter and the position, velocity, stochastic acceleration, and camera orientation of each image are estimated. The reconstructed trajectory is compared with the GPS data and the corresponding formal uncertainties are presented. The result shows that IMU data and descent images processed with a batch filter algorithm provide the trajectory accuracy required for pin-point landing.

Park, Ryan S.↗
NASA NTRS1997

Incompatible Stochastic Processes and Complex Probabilities

The definition of conditional probabilities is based upon the existence of a joint probability. However, a reconstruction of the joint probability from given conditional probabilities imposes certain constraints upon the latter, so that if several conditional probabilities are chosen arbitrarily, the corresponding joint probability may not exist.

Probabilities Mathematics↗
NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗
NASA NTRS2019

A Communication Channel Density Estimating Generative Adversarial Network

Autoencoder-based communication systems use neural network channel models to backwardly propagate message reconstruction error gradients across an approximation of the physical communication channel. In this work, we develop and test a new generative adversarial network (GAN) architecture for the purpose of training a stochastic channel approximating neural network. In previous research, investigators have focused on additive white Gaussian noise (AWGN) channels and/or simplified Rayleigh fading channels, both of which are linear and have well defined analytic solutions. Given that training a neural network is computationally expensive, channel approximation networks— and more generally the autoencoder systems—should be evaluated in communication environments that are traditionally difficult. To that end, our investigation focuses on channels that contain a combination of non-linear amplifier distortion, pulse shape filtering, intersymbol interference, frequency-dependent group delay, multipath, and non-Gaussian statistics. Each of our models are trained without any prior knowledge of the channel. We show that the trained models have learned to generalize over an arbitrary amplifier drive level and constellation alphabet. We demonstrate the versatility of our GAN architecture by comparing the marginal probability density function of several channel simulations with that of their corresponding neural network approximations

Smith, Aaron↗
NASA NTRS1993

Solar flux forecasting using mutual information with an optimal delay

Solar flux F(sub 10.7) directly affects the atmospheric density, thereby changing the lifetime and prediction of satellite orbits. For this reason, accurate forecasting of F(sub 10.7) is crucial for orbit determination of spacecraft. Our attempts to model and forecast F(sub 10.7) uncovered highly entangled dynamics. We concluded that the general lack of predictability in solar activity arises from its nonlinear nature. Nonlinear dynamics allow us to predict F(sub 10.7) more accurately than is possible using stochastic methods for time scales shorter than a characteristic horizon, and with about the same accuracy as using stochastic techniques when the forecasted data exceed this horizon. The forecast horizon is a function of two dynamical invariants: the attractor dimension and the Lyapunov exponent. In recent years, estimation of the attractor dimension reconstructed from a time series has become an important tool in data analysis. In calculating the invariants of the system, the first necessary step is the reconstruction of the attractor for the system from the time-delayed values of the time series. The choice of the time delay is critical for this reconstruction. For an infinite amount of noise-free data, the time delay can, in principle, be chosen almost arbitrarily. However, the quality of the phase portraits produced using the time-delay technique is determined by the value chosen for the delay time. Fraser and Swinney have shown that a good choice for this time delay is the one suggested by Shaw, which uses the first local minimum of the mutual information rather than the autocorrelation function to determine the time delay. This paper presents a refinement of this criterion and applies the refined technique to solar flux data to produce a forecast of the solar activity.

Ashrafi, S.↗
NASA NTRS1991

Error analysis of image reconstruction by a synthetic aperture interferometric radiometer

An error analysis is presented for the image reconstruction algorithm currently employed by the electronically steered thinned array radiometer aircraft instrument. This instrument samples components of the Fourier transform of the brightness temperature distribution at 1.4 GHz. Sources of error are identified, including systematic offsets and stochastic noise in the measurements, errors in the interference pattern antenna calibration, multipath scattering from the antenna support structure, mutual coupling between nearby array elements, and variations in the antenna patterns of the array elements. These errors are shown to produce artifacts in the image which appear as 'streaks' running along the instrument flight line. Possible hardware corrections are discussed. These include a reduction in the profile of the antenna support structure and a decoupling of adjacent and other nearby array elements using corrugated 'skirts'. Also discussed are several image enhancement techniques which detect and correct for some of the streaking. These techniques include a renormalization of the interference patterns based on the measured element patterns, a perturbation based interpretation of image contrast, and a covariance analysis of noise present in the image.

Ruf, Christopher S.↗
NASA NTRS2010

Robust Fault Detection and Isolation for Stochastic Systems

This paper outlines the formulation of a robust fault detection and isolation scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves precise reconstruction of sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the robust fault detection and isolation system.

George, Jemin↗
NASA NTRS1990

Stochastic estimation of coherent structures in turbulent boundary layers

The stochastic estimation technique is described as well as its extension to two-point and space-time conditional estimates. Based on the experimental data of Guezennec (1985), a comparison is made between the stochastic estimates and the conventional ensemble averaging results. As only the normal velocity and streamwise components were measured at each point on the sampling grid, the assumption of statistical symmetry in the spanwise direction about the centerline was used and the spanwise velocity component was reconstructed by integrating the continuity equation from the centerline outward.

Guezennec, Y. G.↗
NASA NTRS2009

3D Lunar Terrain Reconstruction from Apollo Images

Generating accurate three dimensional planetary models is becoming increasingly important as NASA plans manned missions to return to the Moon in the next decade. This paper describes a 3D surface reconstruction system called the Ames Stereo Pipeline that is designed to produce such models automatically by processing orbital stereo imagery. We discuss two important core aspects of this system: (1) refinement of satellite station positions and pose estimates through least squares bundle adjustment; and (2) a stochastic plane fitting algorithm that generalizes the Lucas-Kanade method for optimal matching between stereo pair images.. These techniques allow us to automatically produce seamless, highly accurate digital elevation models from multiple stereo image pairs while significantly reducing the influence of image noise. Our technique is demonstrated on a set of 71 high resolution scanned images from the Apollo 15 mission

Broxton, Michael J.↗
NASA NTRS1979

Molecular phylogenetic trees - On the validity of the Goodman-Moore augmentation algorithm

A response is made to the reply of Nei and Tateno (1979) to the letter of Holmquist (1978) supporting the validity of the augmentation algorithm of Moore (1977) in reconstructions of nucleotide substitutions by means of the maximum parsimony principle. It is argued that the overestimation of the augmented numbers of nucleotide substitutions (augmented distances) found by Tateno and Nei (1978) is due to an unrepresentative data sample and that it is only necessary that evolution be stochastically uniform in different regions of the phylogenetic network for the augmentation method to be useful. The importance of the average value of the true distance over all links is explained, and the relative variances of the true and augmented distances are calculated to be almost identical. The effects of topological changes in the phylogenetic tree on the augmented distance and the question of the correctness of ancestral sequences inferred by the method of parsimony are also clarified.

Holmquist, R.↗
NASA NTRS2021

Drought Relief and Reversal Over North America from 1500 to 2016

Season-to-season persistence of soil moisture drought varies across North America. Such inter-seasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool (December-April) and warm (May-July) season precipitation series and offers new insights on the intra-annual changes in drought for up to 2,000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and three-month long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the 20th century and are used to estimate relief and reversals for the most skillfully reconstructed past 500-years. Sub-continental to continental scale reversals of cool season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the Mid-Atlantic region and the US Southwest. Drought relief at the continental scale may arise in part from macroscale ocean-atmosphere processes, while the smaller scale regional reversals may reflect land-surface feedbacks and stochastic variability.

Drought↗
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 NTRSDate not supplied

Loss of Control Detection for Commercial Transport Aircraft Using Conditional Variational Autoencoders

This work describes a detector for the loss of control condition 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. First, 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 approaching a flight state change by measuring a shift in the sampling distributions of the CVAE latent space. The sampling distributions for flight observations that are approaching envelope limits are localized to external areas of the latent space. We provide an analysis of its applicability to flight data from NASA’s dynamically-scaled generic transport model (GTM) aircraft.

Loss of control↗
NASA NTRS1990

Comparison of FFP predictions with measurements of a low-frequency signal propagated in the atmosphere

An experimental study of low-frequency propagation over a distance of 770 m was previously reported (J. Acoust. Soc. Am. Suppl. 1 86, S120 (1989)). For that study, sound speed profiles were reconstructed entirely from surface-layer micrometeorological data. When the acoustic data were compared with theoretical predictions from a fast field program (FFP), it was found that the FFP underpredicted sound levels measured in a shadow zone. Here, the effect on the predictions of including meteorological data for heights greater than the surface layer, i.e., wind profiles measured by a Doppler sodar, is discussed. Vertical structure of turbulence is simulated by stochastically perturbing the mean profiles, and the agreement between the acoustic data and FFP predictions is improved.

Wilson, K. Keith↗
NASA NTRS1988

Statistical analysis of SSME system data

A statistical methodology to enhance the Space Shuttle Main Engine (SSME) performance prediction accuracy is proposed. This methodology was to be used in conjunction with existing SSME performance prediction computer codes to improve parameter prediction accuracy and to quantify that accuracy. However, after a review of related literature, researchers concluded that the proposed problem required a coverage of areas such as linear and nonlinear system theory, measurement theory, statistics, and stochastic estimation. Since state space theory is the foundation for a more complete study of each of the before mentioned areas, these researchers chose to refocus emphasis to cover the more specialized topic of state vector estimation procedures. State vector estimation was also selected because of current and future concerns by NASA for SSME performance evaluation; i.e., there is a current interest in an improved evaluation procedure for actual SSME post flight performance as well as for post static test performance of a single SSME. A current investigation of analytical methods may be used to improve test stand failure detection. This paper considers the issue of post flight/test state variable reconstruction through the application of observations made on the output of the Space Shuttle propulsion system. Rogers used the Kalman filtering procedure to reconstruct the state variables of the Space Shuttle propulsion system. An objective of this paper is to give the general setup of the Kalman filter and its connection to linear regression. A second objective is to examine the reconstruction methodology for application to the reconstruction of the state vector of a single Space Shuttle Main Engine (SSME) by using static test firing data.

Temple, Enoch C.↗