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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1979

Two-dimensional random surface model for asperity-contact in elastohydrodynamic lubrication

Relations for the asperity-contact time function during elastohydrodynamic lubrication of a ball bearing are presented. The analysis is based on a two-dimensional random surface model, and actual profile traces of the bearing surfaces are used as statistical sample records. The results of the analysis show that transition from 90 percent contact to 1 percent contact occurs within a dimensionless film thickness range of approximately four to five. This thickness ratio is several times large than reported in the literature where one-dimensional random surface models were used. It is shown that low pass filtering of the statistical records will bring agreement between the present results and those in the literature.

Coy, J. J.↗

NASA NTRS1979

Two-dimensional random surface model for asperity-contact in elastohydrodynamic lubrication

Relations for the asperity-contact time fraction during elastohydrodynamic lubrication of a ball bearing are presented. The analysis is based on a two-dimensional random surface model, and actual profile traces of the bearing surfaces are used as statistical sample records. The results of the analysis show that transition from 90 percent contact to 1 percent contact occurs within a dimensionless film thickness range of approximately four to five. This thickness ratio is several times larger than reported in the literature where one-dimensional random surface models were used. It is shown that low pass filtering of the statistical records will bring agreement between the present results and those in the literature.

Coy, J. J.↗

NASA NTRS1981

Correlation function studies for snow and ice

The random medium model is used to characterize snow and ice fields in the interpretation of active and passive microwave remote sensing data. A correlation function is used to describe the random permittivity fluctuations with the associated mean and variance and correlation lengths; and several samples are investigated to determine typical correlation functions for snow and ice. It is shown that correlation functions are extracted directly from appropriate ground truth data, and an exponential correlation function is observed for snow and ice with lengths corresponding to the actual size of ice particles or air bubbles. Thus, given that a medium has spatially stationary statistics and a small medium, the random medium model can interpret remote sensing data where theoretical parameters correspond to actual physical parameters of the terrain.

Vallese, F.↗

DOE OSTI2021

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2024

Classifying Microwave Radiometer Observations Over the Netherlands Into Dry, Shallow-, and Non-Shallow Precipitation Using A Random Forest Model

Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures, Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using space-borne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest model (RF) to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands - a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF is trained on five years of data (2016-2020) and tested with two independent years (2015, 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM’s Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA-5 2-meter temperature and freezing level reanalysis and/or Dual Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb-values as non-shallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb-values, likely resulting from the presence of ice particles in non-precipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

Linda Bogerd↗

DOE OSTI2021

Characterization and differentiation of aluminum powders used in improvised explosive devices. Part 2: Micromorphometric method refinement and preliminary statistical analysis

Abstract Aluminum (Al) powder is commonly encountered in improvised explosive devices (IEDs) as a metallic fuel due to its availability and low cost. Although available commercially in powder form, amateur bomb‐makers also produce their own Al powder via simple methods found online. In order to provide investigative leads and forensic intelligence, it is important to evaluate not only the composition of homemade devices, but also to distinguish between the various forms of Al powder they contain. To achieve this goal, a method using automated microscopy in combination with statistical techniques has been demonstrated to have the potential to provide source discrimination and investigative leads in source attribution of Al powders in IEDs. The present research refined this method and investigated 59 industrially and amateurly produced Al powder sources with seven subsamples per source using two traditional linear discriminant analyses (LDA), one with a standard data split for training and testing, and another using leave‐one‐out cross‐validation. Averaging the classification accuracies for the two LDA‐based analyses, LDA has the ability to correctly classify 59.26%, 83.35%, and 80.69% of the samples based on their powder source, type, and production method, respectively. This classification accuracy represents a 3407%, 317%, and 61.38% increase in accuracy from random class assignment, respectively. Further, in most instances of incorrect data attribution to a particular source, the subsample has been misidentified with another sample of the same powder type or production method.

Ommen, Danica M.↗

NASA NTRS1995

Genetic algorithms as global random search methods

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Peck, Charles C.↗

NASA NTRS1995

Genetic algorithms as global random search methods

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Peck, Charles C.↗

NASA NTRS2009

Lossless Astronomical Image Compression and the Effects of Random Noise

In this paper we compare a variety of modern image compression methods on a large sample of astronomical images. We begin by demonstrating from first principles how the amount of noise in the image pixel values sets a theoretical upper limit on the lossless compression ratio of the image. We derive simple procedures for measuring the amount of noise in an image and for quantitatively predicting how much compression will be possible. We then compare the traditional technique of using the GZIP utility to externally compress the image, with a newer technique of dividing the image into tiles, and then compressing and storing each tile in a FITS binary table structure. This tiled-image compression technique offers a choice of other compression algorithms besides GZIP, some of which are much better suited to compressing astronomical images. Our tests on a large sample of images show that the Rice algorithm provides the best combination of speed and compression efficiency. In particular, Rice typically produces 1.5 times greater compression and provides much faster compression speed than GZIP. Floating point images generally contain too much noise to be effectively compressed with any lossless algorithm. We have developed a compression technique which discards some of the useless noise bits by quantizing the pixel values as scaled integers. The integer images can then be compressed by a factor of 4 or more. Our image compression and uncompression utilities (called fpack and funpack) that were used in this study are publicly available from the HEASARC web site.Users may run these stand-alone programs to compress and uncompress their own images.

Pence, William↗

DOE OSTI2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.

42 ENGINEERING↗

DOE OSTI2025

Rippled metamaterials with scale-dependent tailorable elasticity

Thermally induced ripples are intrinsic features of nanometer-thick films, atomically thin materials, and cell membranes, significantly affecting their elastic properties. Despite decades of theoretical studies on the mechanics of suspended thermalized sheets, controversy still exists over the impact of these ripples, with conflicting predictions about whether elasticity is scale-dependent or scale-independent. Experimental progress has been hindered so far by the inability to have a platform capable of fully isolating and characterizing the effects of ripples. This knowledge gap limits the fundamental understanding of thin materials and their practical applications. Here, we show that thermal-like static ripples shape thin films into a class of metamaterials with scale-dependent, customizable elasticity. Utilizing a scalable semiconductor manufacturing process, we engineered nanometer-thick films with precisely controlled frozen random ripples, resembling snapshots of thermally fluctuating membranes. Resonant frequency measurements of rippled cantilevers reveal that random ripples effectively renormalize and enhance the average bending rigidity and sample-to-sample variations in a scale-dependent manner, consistent with recent theoretical estimations. The predictive power of the theoretical model, combined with the scalability of the fabrication process, was further exploited to create kirigami architectures with tailored bending rigidity and mechanical metamaterials with delayed buckling instability.

Applied Physical Sciences↗

DOE OSTI2023

Graph Sparsification by Approximate matrix Multiplication

Graphs arising in statistical problems, signal processing, large networks, combinatorial optimization, and data analysis are often dense, which causes both computational and storage bottlenecks. One way of sparsifying a weighted graph, while sharing the same vertices as the original graph but reducing the number of edges, is through spectral sparsification. We study this problem through the perspective of RandNLA. Specifically, we utilize randomized matrix multiplication to give a clean and simple analysis of how sampling according to edge weights gives a spectral approximation to graph Laplacians, without requiring spectral information. Through the CR–MM algorithm, we attain a simple and computationally efficient sparsifier whose resulting Laplacian estimate is unbiased and of minimum variance. Here, we define a new notion of additive spectral sparsifiers, which has not been considered in the literature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Active Learning Framework

Machine learning (ML) of interatomic potentials show great promise to accelerate scientific simulation, e.g., by emulating expensive computations at a high accuracy but much reduced computational cost. Training datasets are calculated from computationally expensive ab initio quantum mechanics methods, density functional theory (DFT). Trained on this data, an ML model can be very successful in predicting energy and forces for new atomic configurations. A critical factor is the quality and diversity of the training dataset. Thus, a highly automated approach to dataset construction based on active learning framework is designed suitable for material physics. The active learning scheme begins with fully randomized atomic configurations. Then, many Molecular Dynamics (MD) trajectories are simulated using current ML potentials, where each MD trajectory is initialized to a random disordered configuration. The temperature is varied in order to diversify the sampled configuration during these simulations. The variance of predictions for eight neural networks within an ensemble is analyzed to determine whether the model is operating as expected. This helps in determining whether collecting more data would be helpful to the model by checking the ensemble variance is greater than the threshold. In this case, the MD trajectory is terminated and the final atomic configuration is placed on a queue (SQL database) for DFT calculations and added to training dataset. Periodically, ML model is retrained to the updated training model. This Active Learning loop is iterated until the cost of MD simulations becomes prohibitively expensive. The MD simulations will hopefully be sufficiently robust to support nucleation after many active learning iterations. In this sense, active learning scheme must automatically discover the important low energy and nonequilibrium physics.

Nebgen, Benjamin↗

DOE OSTI2022

Data Science and Machine Learning for Genome Security

This report describes research conducted to use data science and machine learning methods to distinguish targeted genome editing versus natural mutation and sequencer machine noise. Genome editing capabilities have been around for more than 20 years, and the efficiencies of these techniques has improved dramatically in the last 5+ years, notably with the rise of CRISPR-Cas technology. Whether or not a specific genome has been the target of an edit is concern for U.S. national security. The research detailed in this report provides first steps to address this concern. A large amount of data is necessary in our research, thus we invested considerable time collecting and processing it. We use an ensemble of decision tree and deep neural network machine learning methods as well as anomaly detection to detect genome edits given either whole exome or genome DNA reads. The edit detection results we obtained with our algorithms tested against samples held out during training of our methods are significantly better than random guessing, achieving high F1 and recall scores as well as with precision overall.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1991

The effect of anisotropic emission from thick accretion disks on the luminosity functions of active galactic nuclei

High-luminosity active galactic nuclei (AGNs) powered by accretion onto a massive black hole (or other compact object) may have bolometric luminosities dominated by thermal emission from a geometrically thick accretion disk. Radiation from these disks is strongly anisotropic, which has important consequences for the observed luminosity distribution, and therefore for systematic biases in flux-limited samples. The effect of anisotropic emission from an ensemble of AGNs with random oriented thick disks radiating at or near the Eddington limit is calculated. Because of their higher luminosities, it is predicted face-on disks should constitute an increasing fraction of observed high-redshift, high-luminosity AGNs. Comparison of the results with observed quasar luminosity functions suggests a narrow mass distribution with an upper limit of about a billion solar masses for high-redshift quasars.

Urry, C. M.↗

NASA NTRS1994

Atomic oxygen durability evaluation of the flexible batten for the photovoltaic array mast on Space Station

A test program was conducted at the National Aeronautics and Space Administration's Lewis Research Center (LeRC) to evaluate the long term low Earth orbital (LEO) atomic oxygen (AO) durability of a flexible (fiberglass-epoxy composite) batten. The flexible batten is a component used to provide structural rigidity in the photovoltaic array mast on Space Station. The mast is used to support and articulate the photovoltaic array, therefore, the flexible batten must be preloaded for the 15 year lifetime of an array blanket. Development hardware and composite materials were evaluated in ground testing facilities for AO durability and dynamic retraction-deployment cyclic loading representative of expected full life in-space application. The CV1144 silicone (AO protective) coating was determined to provide adequate protection against AO degradation of the composite material and provided fiber containment, thus the structural integrity of the flexible batten was maintained. Both silicone coated and uncoated flexible battens maintained load carrying capabilities. Results of the testing did indicate that the CV1144 silicone protective coating was oxidized by AO reactions to form a brittle glassy (SiO2) skin that formed cracking patterns on all sides of the coated samples. The cracking was observed in samples that were mechanically stressed as well as samples in non-stressed conditions. The oxidized silicon was observed to randomly spall in small localized areas, on the flexible battens that underwent retraction-deployment cycling. Some darkening of the silicon, attributed to vacuum ultraviolet (VUV) radiation, was observed.

Stidham, Curtis R.↗

NASA NTRS2002

Effect of Random Geometric Uncertainty on the Computational Design of a 3-D Flexible Wing

The effect of geometric uncertainty due to statistically independent, random, normally distributed shape parameters is demonstrated in the computational design of a 3-D flexible wing. A first-order second-moment statistical approximation method is used to propagate the assumed input uncertainty through coupled Euler CFD aerodynamic / finite element structural codes for both analysis and sensitivity analysis. First-order sensitivity derivatives obtained by automatic differentiation are used in the input uncertainty propagation. These propagated uncertainties are then used to perform a robust design of a simple 3-D flexible wing at supercritical flow conditions. The effect of the random input uncertainties is shown by comparison with conventional deterministic design results. Sample results are shown for wing planform, airfoil section, and structural sizing variables.

Gumbert, C. R.↗

NASA NTRS2010

Robustness-Based Design Optimization Under Data Uncertainty

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

Zaman, Kais↗