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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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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Phase-Retrieval Uncertainty Estimation and Algorithm Comparison for the JWST-ISIM Test Campaign

Phase retrieval, the process of determining the exitpupil wavefront of an optical instrument from image-plane intensity measurements, is the baseline methodology for characterizing the wavefront for the suite of science instruments (SIs) in the Integrated Science Instrument Module (ISIM) for the James Webb Space Telescope (JWST). JWST is a large, infrared space telescope with a 6.5-meter diameter primary mirror. JWST is currently NASA's flagship mission and will be the premier space observatory of the next decade. ISIM contains four optical benches with nine unique instruments, including redundancies. ISIM was characterized at the Goddard Space Flight Center (GSFC) in Greenbelt, MD in a series of cryogenic vacuum tests using a telescope simulator. During these tests, phase-retrieval algorithms were used to characterize the instruments. The objective of this paper is to describe the Monte-Carlo simulations that were used to establish uncertainties (i.e., error bars) for the wavefronts of the various instruments in ISIM. Multiple retrieval algorithms were used in the analysis of ISIM phase-retrieval focus-sweep data, including an iterativetransform algorithm and a nonlinear optimization algorithm. These algorithms emphasize the recovery of numerous optical parameters, including low-order wavefront composition described by Zernike polynomial terms and high-order wavefront described by a point-by-point map, location of instrument best focus, focal ratio, exit-pupil amplitude, the morphology of any extended object, and optical jitter. The secondary objective of this paper is to report on the relative accuracies of these algorithms for the ISIM instrument tests, and a comparison of their computational complexity and their performance on central and graphical processing unit clusters. From a phase-retrieval perspective, the ISIM test campaign includes a variety of source illumination bandwidths, various image-plane sampling criteria above and below the Nyquist- Shannon critical sampling value, various extended object sizes, and several other impactful effects.

Design Analysis↗
NASA NTRS2016

A Comparison of Metamodeling Techniques via Numerical Experiments

This paper presents a comparative analysis of a few metamodeling techniques using numerical experiments for the single input-single output case. These experiments enable comparing the models' predictions with the phenomenon they are aiming to describe as more data is made available. These techniques include (i) prediction intervals associated with a least squares parameter estimate, (ii) Bayesian credible intervals, (iii) Gaussian process models, and (iv) interval predictor models. Aspects being compared are computational complexity, accuracy (i.e., the degree to which the resulting prediction conforms to the actual Data Generating Mechanism), reliability (i.e., the probability that new observations will fall inside the predicted interval), sensitivity to outliers, extrapolation properties, ease of use, and asymptotic behavior. The numerical experiments describe typical application scenarios that challenge the underlying assumptions supporting most metamodeling techniques.

Crespo, Luis G.↗
NASA NTRS2016

Spin Glass Patch Planting

In this paper, we propose a patch planting method for creating arbitrarily large spin glass instances with known ground states. The scaling of the computational complexity of these instances with various block numbers and sizes is investigated and compared with random instances using population annealing Monte Carlo and the quantum annealing DW2X machine. The method can be useful for benchmarking tests for future generation quantum annealing machines, classical and quantum mechanical optimization algorithms.

Quantum Annealing↗
NASA NTRS2016

Polarized Radiative Transfer of a Cirrus Cloud Consisting of Randomly Oriented Hexagonal Ice Crystals: The 3 x 3 Approximation for Non-Spherical Particles

The reflection and transmission of polarized light for a cirrus cloud consisting of randomly oriented hexagonal columns were calculated by two very different vector radiative transfer models. The forward peak of the phase function for the ensemble-averaged ice crystals has a value of order 6 x 10(exp 3) so a truncation procedure was used to help produce numerically efficient yet accurate results. One of these models, the Vectorized Line-by-Line Equivalent model (VLBLE), is based on the doubling- adding principle, while the other is based on a vector discrete ordinates method (VDISORT). A comparison shows that the two models provide very close although not entirely identical results, which can be explained by differences in treatment of single scattering and the representation of the scattering phase matrix. The relative differences in the reflected I and Q Stokes parameters are within 0.5 for I and within 1.5 for Q for all viewing angles. In 1971 Hansen showed that for scattering by spherical particles the 3 x 3 approximation is sufficient to produce accurate results for the reflected radiance I and the degree of polarization (DOP), and he conjectured that these results would hold also for non-spherical particles. Simulations were conducted to test Hansen's conjecture for the cirrus cloud particles considered in this study. It was found that the 3 x 3 approximation also gives accurate results for the transmitted light, and for Q and U in addition to I and DOP. For these non-spherical ice particles the 3 x 3 approximation leads to an absolute error 2 x 10(exp -6) for the reflected and transmitted I, Q and U Stokes parameters. Hence, it appears to be an excellent approximation, which significantly reduces the computational complexity and burden required for multiple scattering calculations.

Polarization↗
NASA NTRS2017

Structured Light-Based Hazard Detection For Planetary Surface Navigation

This paper describes a structured light-based sensor for hazard avoidance in planetary environments. The system presented here can also be used in terrestrial applications constrained by reduced onboard power and computational complexity and low illumination conditions. The sensor is on a calibrated camera and laser dot projector system. The onboard hazard avoidance system determines the position of the projected dots in the image and through a triangulation process detects potential hazards. The paper presents the design parameters for this sensor and describes the image based solution for hazard avoidance. The system presented here was tested extensively in day and night conditions in Lunar analogue environments. The current system achieves over 97 detection rate with 1.7 false alarms over 2000 images.

Nefian, Ara↗
NASA NTRS2017

Vision-Aided Inertial Navigation

This document discloses, among other things, a system and method for implementing an algorithm to determine pose, velocity, acceleration or other navigation information using feature tracking data. The algorithm has computational complexity that is linear with the number of features tracked.

Roumeliotis, Stergios I.↗
NASA NTRS2019

Standardizing Microprocessor and GPU Radiation Test Approaches

Microprocessor, Graphics Processing Units (GPUs) and DDRx memory devices have emerged as promising next-generation technologies that enables both high performance processing and acceleration of complex algorithms for the latest challenges in human spaceflight, autonomous vehicles and artificial intelligence (AI). The feature sets of these devices offer exponential increases to throughput, calculation capability and system autonomy when compared to legacy flight systems. NASA's Electronic Part and Packaging (NEPP) Program has conducted an investigation into the radiation susceptibility of leading edge devices and process technologies by establishing standardized test approaches. Unlike most discrete devices, these require state of the art test systems to induce specific hardware activity similar to application software, thus allowing the characterization of failure modes within the system. To best characterize the tested part, NEPP eliminates variables that may impact device performance under radiation. Simplification of remaining system-level variables leads to an improved understanding of complex computational devices and their intended applications. The failure modes and error signatures that are recorded during testing are used to determine radiation sensitivity of the semiconductor process and the microcode architecture of the design. This presentation will discuss the test methodology that NASA Electronic Parts and Packaging (NEPP) is working to establish for its microprocessor, GPU and DDRx memory test programs to provide guidance on these devices and their underlying technology, in regards to their potential usage in future space flight systems.

GPU↗
NASA NTRS2019

Forming Aggregations using Virtual Sharding: Lessons Learned from Simple Scalable Storage (S3)

Data aggregation is the ability to combine separate datasets to form a single new logical dataset provides users with a powerful abstraction. The advantage of an aggregate dataset is that the users are freed from having to understand, and incorporate into their workflow, knowledge about the (ad hoc) organization of the constituent datasets. However, aggregating large numbers of files can be computationally complex with data server systems performing many repetitive operations. As part of the authors work on subsetting data stored on Amazon Web Service (AWS) Simple Storage Service (S3), we developed technology to read portions of otherwise monolithic data files. This enables the formation of virtual shards for user in subsetting data stored in HDF5 (hierarchical data format, version 5) files. This same tool can be used to form aggregations that combine data stored in many HDF5 files when those files are stored on S3. The nature of the virtual sharding and the algorithm that exploits it for subsetting is such that it can also be used for aggregation with the need for many of the repetitive operations required by the per file aggregation techniques. We will present timing information that demonstrates the flexibility of this approach. However, the lessons learned is that while this is a useful result in and of itself, these very same techniques can be applied in other contexts where data are stored in services and on media other than S3. For example, this same technique can be applied to data stored on spinning disk. Pushing the envelope for S3 forced a reexamination of our data access techniques which lead to unexpected positive benefits.

Gallagher, James↗
NASA NTRSDate not supplied

Validation of Kestrel IDDES Simulations for SLS Transition Analysis

Complex computational simulations are needed to support the Space Launch System (SLS) program, and the fidelity of the computational results must be defended. More specifically, a number of databases are produced for the transition phase of flight, which occurs after the rocket clears the tower but before reaching transonic speeds. In an effort to reduce computational uncertainty, many of the computational parameters were altered to determine the sensitivity of the results to the value of the parameter and then updating the best practice procedures. The baseline routines were developed over many years through the maturation of the SLS program, and this paper delivers a detailed discussion of these baseline results. This section is followed by demonstration of perturbing some of the more significant parameters, including time step and turbulence model, and concluded with a summary of the herein determined best practices for such analysis.

CFD↗
NASA NTRS2021

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity of flood mapping performance to different combinations of SAR and MS derived spectral indices or band transformations in color space. This study explores the use of diverse bands of Sentinel 2 (S2) through well-established water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to assess their capability for generating accurate flood inundation maps. The robustness in performance of S-1 and S-2 band combinations was evaluated using 446 hand labeled flood inundation images spanning across 11 flood events from Sen1Floods11 dataset which are highly diverse in terms of land cover as well as location. A modified K-fold cross validation approach is used to evaluate the performance of 32 combinations of S1 and S2 bands using a fully connected deep convolutional neural network known as U-Net. Our results indicated that usage of elevation information has improved the capability of S1 imagery to produce more accurate flood inundation maps. Compared to a median F1 score of 0.62 when using only S1 bands, the combined use of S1 and elevation information led to an improved median F1 score of 0.73. Water extraction indices based on S2 bands have a statistically significant superior performance in comparison to S1. Among all the band combinations, HSV (Hue, Saturation, Value) transformation of S2 bands provides a median F1 score of 0.9, outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Additionally, U-Net algorithm was able to learn the relationship between raw S2 based water extraction indices and their corresponding raw S2 bands, but not of HSV owing to relatively complex computation involved in the latter. Results of the paper establishes important benchmarks for the extension of S1 and S2 data-based flood inundation mapping efforts over large spatial extents.

Goutam Konapala↗
NASA NTRS2022

Simultaneous Stoquasticity

Stoquastic Hamiltonians play a role in the computational complexity of the local Hamiltonian problem as well as the study of classical simulability. In particular, stoquastic Hamiltonians can be straightforwardly simulated using Monte Carlo techniques. We address the question of whether two or more Hamiltonians may be made simultaneously stoquastic via a unitary transformation. This question has important implications for the complexity of simulating quantum annealing where quantum advantage is related to the stoquasticity of the Hamiltonians involved in the anneal. We find that for almost all problems no such unitary exists and show that the problem of determining the existence of such a unitary is equivalent to identifying if there is a solution to a system of polynomial (in)equalities in the matrix elements of the initial and transformed Hamiltonians. Solving such a system of equations is NP-hard. We highlight a geometric understanding of this problem in terms of a collection of generalized Bloch vectors.

Jacob Bringewatt↗
NASA NTRSDate not supplied

Simultaneous Stoquasticity

Stoquastic Hamiltonians play a role in the computational complexity of the local Hamiltonian problem as well as the study of classical simulability. In particular, stoquastic Hamiltonians can be straightforwardly simulated using Monte Carlo techniques. We address the question of whether two or more Hamiltonians may be made simultaneously stoquastic via a unitary transformation. This question has important implications for the complexity of simulating quantum annealing where quantum advantage is related to the stoquasticity of the Hamiltonians involved in the anneal. We find that for almost all problems no such unitary exists and show that the problem of determining the existence of such a unitary is equivalent to identifying if there is a solution to a system of polynomial (in)equalities in the matrix elements of the initial and transformed Hamiltonians. Solving such a system of equations is NP-hard. We highlight a geometric understanding of this problem in terms of a collection of generalized Bloch vectors.

Monte Carlo↗
NASA NTRS2023

A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-band Under Bare and Vegetated Soil Conditions

P-band radiometry has been demonstrated to have a deeper sensing depth than at L-band, making the consideration of multi-layer microwave interactions necessary. Additionally, the scattering and phase interference effects are different at P-band, requiring a re-consideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L-band and P-band is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multi-layer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L-band and P-band radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band, 2 K at L-band), correlating strongly with soil moisture variations at 0-10 cm. Findings suggest that coherent and incoherent models perform similarly; thus, incoherent models may be preferable for estimating TB at L- and P-band due to reduced computational complexity.

Soil moisture profile↗
NASA NTRSDate not supplied

An Efficient Filter for Measurements Corrupted with Cauchy Noise

This paper present a new sequential filter for state estimation using measurements corrupted with Cauchy noise. The new filter retains the familiar structure of the Kalman filter and is computationally efficient. In addition, it does not exhibit computational complexity which grows or varies in time like existing methods. These results are based upon a nearly 50 year old result by Masreliez in which the conditional mean estimator is approximated via linearization of the measurement predictive density. This work derives the new filter, provides discussion regarding practical implementation, and present Monte Carlo analyses to validate and assess the new filter's performance.

James S McCabe↗
NASA NTRS2024

FRAAME Version 1.0 User Manual

The code for Forced Response Aeromechanics Analysis in a MATLAB-based (The MathWorks, Inc.) Environment (FRAAME Version 1.0) was developed for internal use in aeromechanics efforts undertaken at the NASA Glenn Research Center for computing turbomachine component forced response and Goodman diagrams via modal summation method. The main working script (FRAAMEv1.m) allows users to input case-specific manual inputs while the triple-nested loop invokes functions to compute forced response per blade, per nodal diameter, and per mode. Secondly, forced response values are applied to modal stresses to compute complex Von Mises stress values and generate Goodman diagrams per blade, per nodal diameter, and per mode using a linear modal summation method. Currently, this code functions in the Windows (Microsoft Corporation) operating system using MATLAB Version R2023a, but it can be adapted for use in the Linux (Linus Torvalds) operating system by changing the appropriate file path structure in the main script, as well as functions that call external results files.

Aeromechanics↗
NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗
DOE OSTI2024

Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World

Nature performs complex computations constantly at clearly lower cost and higher performance than digital computers. It is crucial to understand how to harness the unique computational power of nature in Machine Learning (ML). In the past decade, besides the development of Neural Networks (NNs), the community has also relentlessly explored nature-powered ML paradigms. Although most of them are still predominantly theoretical, a new practical paradigm enabled by the recent advent of CMOS-compatible room-temperature nature-based computers has emerged. By harnessing the nature's power of entropy increase, this paradigm can solve binary learning problems delivering immense speedup and energy savings compared with NNs, while maintaining comparable accuracy. Regrettably, its values to the real world are highly constrained by its binary nature. A clear pathway to its extension to real-valued problems remains elusive. This paper aims to unleash this pathway by proposing a novel end-to-end Nature-Powered Graph Learning (NP-GL) framework. Specifically, through a three-dimensional co-design, NP-GL can leverage the nature's power of entropy increase to efficiently solve real-valued graph learning problems. Experimental results across 4 real-world applications with 6 datasets demonstrate that NP-GL delivers, on average, 6970X speedup and 10^5x energy consumption reduction with comparable or even higher accuracy than Graph Neural Networks (GNNs).

artificial intelligence↗