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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Application of convolutional neural networks for stellar spectral classification

ABSTRACT Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters Teff, $\rm {\log g}$, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using convolutional neural networks (CNNs). Traditional machine learning (ML) methods with ‘shallow’ architecture (usually up to two hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalization. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like artificial neural networks, Random Forest, and CNN to classify stellar spectra using the Jacoby Atlas, ELODIE, and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using CNNs, we are able to lower the error up to 1.23 spectral subclasses as compared to that of two subclasses achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS data base with SNR > 20.

Sharma, Kaushal↗
DOE OSTI2024

Structural and mechanical properties of monolayer amorphous carbon and boron nitride

Amorphous materials exhibit various characteristics that are not featured by crystals and can sometimes be tuned by their degree of disorder (DOD). Here, we report results on the mechanical properties of monolayer amorphous carbon (MAC) and monolayer amorphous boron nitride (maBN) with different DOD. The pertinent structures are obtained by kinetic-Monte-Carlo (kMC) simulations using machine-learning potentials (MLP) with density-functional-theory (DFT)-level accuracy. An intuitive order parameter, namely the areal fraction F x occupied by crystallites within the continuous random network, is proposed to describe the DOD. We find that F x captures the essence of the DOD: Samples with the same F x but different sizes and arrangements of crystallites, obtained using two distinct kMC procedures, have virtually identical radial distributions functions as well as bond-length and bond-angle distributions. Furthermore, by simulating the fracture process with molecular dynamics, we found that the mechanical responses of MAC and maBN before fracture are mainly determined by F x and are insensitive to the sizes and specific arrangements and to some extent the numbers and area distributions of the crystallites. The behavior of cracks in the two materials is analyzed and found to mainly propagate in meandering paths in the CRN region and to be influenced by crystallites in distinct ways that toughen the material. Furthermore, the present results reveal the relation between structure and mechanical properties in amorphous monolayers and may provide a universal toughening strategy for 2D materials.

2-dimensional systems↗
DOE OSTI2024

Simulation of open quantum systems via low-depth convex unitary evolutions

Simulating physical systems on quantum devices is one of the most promising applications of quantum technology. Current quantum approaches to simulating open quantum systems are still practically challenging on NISQ-era devices, because they typically require ancilla qubits and extensive controlled sequences. In this work, we propose a hybrid quantum-classical approach for simulating a class of open system dynamics called random-unitary channels. These channels naturally decompose into a series of convex unitary evolutions, which can then be efficiently sampled and run as independent circuits. The method does not require deep ancilla frameworks and thus can be implemented with lower noise costs. We implement simulations of open quantum systems up to dozens of qubits and with large channel ranks. Published by the American Physical Society 2024

Peetz, Joseph (ORCID:0000000274458028)↗
DOE OSTI2023

Statistical Generation of Ocean Forcing With Spatiotemporal Variability for Ice Sheet Models

Melting of ice at the base of floating ice shelves that fringe the Antarctic ice sheet has been identified as a significant source of uncertainty in sea level rise projections. Part of this uncertainty derives from chaotic internal variability of the coupled ocean-atmosphere system. For numerical ice sheet model projections, this uncertainty has not previously been quantified because of the prohibitive computational expense of running large climate model ensembles. Here, we develop and demonstrate a technique that generates independent realizations of internal climate variability from a single climate model simulation. Building on previous developments in model emulation, this technique uses empirical orthogonal function decomposition and Fourier-phase randomization to generate statistically consistent realizations of spatiotemporal variability fields for the target climate variable. The method facilitates efficient sampling of a wide range of climate trajectories, which can also be incorporated within ice sheet or other physical models to represent feedback processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combined Imaging and RNA-Seq on a Microfluidic Platform for Viral Infection Studies

The goal of this work was to pioneer a novel, low-overhead protocol for simultaneously assaying cell-surface markers and intracellular gene expression in a single mammalian cell. The purpose of developing such a method is to be able to understand the mechanisms by which pathogens engage with individual mammalian cells, depending on their cell surface proteins, and how both host and pathogen gene expression changes are reflective of these mechanisms. The knowledge gained from such analyses of single cells will ultimately lead to more robust pathogen detection and countermeasures. Our method was aimed at streamlining both the upstream cell sample preparation using microfluidic methods, as well as the actual library making protocol. Specifically, we wanted to implement a random hexamer-based reverse transcription of all RNA within a single cell (as opposed to oligo dT-based which would only capture polyadenylated transcripts), and then use a CRISPR-based method called scDash to deplete ribosomal DNAs (since ribosomal RNAs make up the majority of the RNA in a mammalian cell). After significant troubleshooting, we demonstrate that we are able to prepare cDNA from RNA using the random hexamer primer, and perform the rDNA depletion. We also show that we can visualize individually stained cells, setting up the pipeline for connecting surface markers to RNA-sequencing profiles. Finally, we test a number of devices for various parts of the pipeline, including bead generation, optical barcoding and cell dispensing, and demonstrate that while some of these have potential, more work is needed to optimize this part of the pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Fatigue strength reduction model: RANDOM3 and RANDOM4 user manual. Appendix 2: Development of advanced methodologies for probabilistic constitutive relationships of material strength models

FORTRAN programs RANDOM3 and RANDOM4 are documented in the form of a user's manual. Both programs are based on fatigue strength reduction, using a probabilistic constitutive model. The programs predict the random lifetime of an engine component to reach a given fatigue strength. The theoretical backgrounds, input data instructions, and sample problems illustrating the use of the programs are included.

Boyce, Lola↗
NASA NTRS1993

The large-scale morphology of IRAS galaxies

At present, visual inspection is the only method for comparing the large-scale morphologies in the distribution of galaxies to those in model universes generated by N-body simulations. To remedy the situation, we have developed a set of three structure functions (S1, S2, S3) that quantify the degree of large-scale prolateness, oblateness, and sphericity/uniformity of a 3-D particle distribution and have applied them to a volume-limited (less than = 4000 km/s) sample of 699 IRAS galaxies with f sub 60 greater than 1.2 Jy. To determine the structure functions, we randomly select 500 galaxies as origins of spherical windows of radius R sub w, locate the centroid of the galaxies in the window (assuming all galaxies have equal mass) and then, compute the principal moments of inertia (I sub 1, I sub 2, I sub 3) about the centroid. Each S sub i is a function of (I sub 2)/(I sub 1) and (I sub 3)/I sub 1). S1, S2, and S3 tend to unity for highly prolate, oblate, and uniform distributions, respectively and tend to zero otherwise. The resulting 500 values of S sub i at each scale R sub w are used to construct a histogram.

Babul, Arif↗
NASA NTRS1958

A Method for the Calculation of the Lateral Response of Airplanes to Random Turbulence

In this method, the gust velocities are represented as rolling gusts, yawing gusts, and side gusts. Random distributions of gust velocities across the span are taken into account in defining the rolling and yawing gusts. Complex stability derivatives are used to account for the random distribution of side gusts along the fuselage and vertical tail and the lag effects in gust penetration. A sample calculation procedure is presented for obtaining the response of the airplane in each degree of freedom.

LOADS, GUST - WINGS↗
NASA NTRS1993

Finite-size effects on long-range correlations: implications for analyzing DNA sequences

We analyze the fluctuations in the correlation exponents obtained for noncoding DNA sequences. We find prominent sample-to-sample variations as well as variations within a single sample in the scaling exponent. To determine if these fluctuations may result from finite system size, we generate correlated random sequences of comparable length and study the fluctuations in this control system. We find that the DNA exponent fluctuations are consistent with those obtained from the control sequences having long-range power-law correlations. Finally, we compare our exponents for the DNA sequences with the exponents obtained from power-spectrum analysis and correlation-function techniques, and demonstrate that the original "DNA-walk" method is intrinsically more accurate due to reduced noise.

NASA Discipline Cardiopulmonary↗
NASA NTRS2007

Derivation and Application of a Global Albedo yielding an Optical Brightness To Physical Size Transformation Free of Systematic Errors

Orbital object data acquired via optical telescopes can play a crucial role in accurately defining the space environment. Radar systems probe the characteristics of small debris by measuring the reflected electromagnetic energy from an object of the same order of size as the wavelength of the radiation. This signal is affected by electrical conductivity of the bulk of the debris object, as well as its shape and orientation. Optical measurements use reflected solar radiation with wavelengths much smaller than the size of the objects. Just as with radar, the shape and orientation of an object are important, but we only need to consider the surface electrical properties of the debris material (i.e., the surface albedo), not the bulk electromagnetic properties. As a result, these two methods are complementary in that they measure somewhat independent physical properties to estimate the same thing, debris size. Short arc optical observations such as are typical of NASA's Liquid Mirror Telescope (LMT) give enough information to estimate an Assumed Circular Orbit (ACO) and an associated range. This information, combined with the apparent magnitude, can be used to estimate an "absolute" brightness (scaled to a fixed range and phase angle). This absolute magnitude is what is used to estimate debris size. However, the shape and surface albedo effects make the size estimates subject to systematic and random errors, such that it is impossible to ascertain the size of an individual object with any certainty. However, as has been shown with radar debris measurements, that does not preclude the ability to estimate the size distribution of a number of objects statistically. After systematic errors have been eliminated (range errors, phase function assumptions, photometry) there remains a random geometric albedo distribution that relates object size to absolute magnitude. Measurements by the LMT of a subset of tracked debris objects with sizes estimated from their radar cross sections indicate that the random variations in the albedo follow a log-normal distribution quite well. In addition, this distribution appears to be independent of object size over a considerable range in size. Note that this relation appears to hold for debris only, where the shapes and other properties are not primarily the result of human manufacture, but of random processes. With this information in hand, it now becomes possible to estimate the actual size distribution we are sampling from. We have identified two characteristics of the space debris population that make this process tractable and by extension have developed a methodology for performing the transformation.

Mulrooney, Dr. Mark K.↗
NASA NTRS2013

Wind Retrieval Algorithms for the IWRAP and HIWRAP Airborne Doppler Radars with Applications to Hurricanes

Algorithms for the retrieval of atmospheric winds in precipitating systems from downward-pointing, conically-scanning airborne Doppler radars are presented. The focus in the paper is on two radars: the Imaging Wind and Rain Airborne Profiler(IWRAP) and the High-altitude IWRAP (HIWRAP). The IWRAP is a dual-frequency (Cand Ku band), multi-beam (incidence angles of 30 50) system that flies on the NOAAWP-3D aircraft at altitudes of 2-4 km. The HIWRAP is a dual-frequency (Ku and Kaband), dual-beam (incidence angles of 30 and 40) system that flies on the NASA Global Hawk aircraft at altitudes of 18-20 km. Retrievals of the three Cartesian wind components over the entire radar sampling volume are described, which can be determined using either a traditional least squares or variational solution procedure. The random errors in the retrievals are evaluated using both an error propagation analysis and a numerical simulation of a hurricane. These analyses show that the vertical and along-track wind errors have strong across-track dependence with values of 0.25 m s-1 at nadir to 2.0 m s-1 and 1.0 m s-1 at the swath edges, respectively. The across-track wind errors also have across-track structure and are on average, 3.0 3.5 m s-1 or 10 of the hurricane wind speed. For typical rotated figure four flight patterns through hurricanes, the zonal and meridional wind speed errors are 2 3 m s-1.Examples of measured data retrievals from IWRAP during an eyewall replacement cycle in Hurricane Isabel (2003) and from HIWRAP during the development of Tropical Storm Matthew (2010) are shown.

atmospheric winds↗
NASA NTRSDate not supplied

NASA's Ground-Based Microgravity Simulation Facility

Since opportunities to conduct experiments in space are scarce, various microgravity simulators and analogs have been widely used in space biology ground studies. Even though microgravity simulators do not produce all of the biological effects observed in the true microgravity environment, they provide alternative test platforms that are effective, affordable, and readily available to facilitate microgravity research. The Microgravity Simulation Support Facility (MSSF) at the National Aeronautics and Space Administration (NASA) John F. Kennedy Space Center (KSC) has been established for conducting short duration experiments, typically less than 1 month, utilizing a variety of microgravity simulation devices for research at different gravity levels. The simulators include, but are not limited to, 2D Clinostats, 3D Clinostats, Random Positioning Machines, and Rotating Wall Vessels. In this chapter, we will discuss current MSSF capabilities, development concepts, customized sample holder designs, and the physical characteristics of these microgravity simulators.

Ye Zhang↗
DOE OSTI2022

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Abstract Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric red- shifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We also apply the method to three photo-z codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhood Fitting (DNF), and Random Forest-based photo-z (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-z's. The systematic uncertainty in the mean redshift bias of the source galaxy sample is z ≲ 0.02, though the precise value depends on the redshift bin under consideration. Here, we discuss possible ways to mitigate the impact of our dominant systematics in future analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Phase Imaging: A Compressive Sensing Approach

Since Wolfgang Pauli posed the question in 1933, whether the probability densities |Ψ(r)|² (real-space image) and |Ψ(q)|² (reciprocal space image) uniquely determine the wave function Ψ(r) [1], the so called Pauli Problem sparked numerous methods in all fields of microscopy [2, 3]. Reconstructing the complete wave function Ψ(r) = a(r)e-iφ(r) with the amplitude a(r) and the phase φ(r) from the recorded intensity enables the possibility to directly study the electric and magnetic properties of the sample through the phase. In transmission electron microscopy (TEM), electron holography is by far the most established method for phase reconstruction [4]. Requiring a high stability of the microscope, next to the installation of a biprism in the TEM, holography cannot be applied to any microscope straightforwardly. Recently, a phase retrieval approach was proposed using conventional TEM electron diffractive imaging (EDI). Using the SAD aperture as reciprocal-space constraint, a localized sample structure can be reconstructed from its diffraction pattern and a real-space image using the hybrid input-output algorithm [5]. We present an alternative approach using compressive phase-retrieval [6]. Our approach does not require a real-space image. Instead, random complimentary pairs of checkerboard masks are cut into a 200 nm Pt foil covering a conventional TEM aperture (cf. Figure 1). Used as SAD aperture, subsequently diffraction patterns are recorded from the same sample area. Hereby every mask blocks different parts of gold particles on a carbon support (cf. Figure 2). The compressive sensing problem has the following formulation. First, we note that the complex-valued reciprocal-space wave-function is the Fourier transform of the (also complex-valued) real-space wave-function, Ψ(q) = F[Ψ(r)], and subsequently the diffraction pattern image is given by |Ψ(q)|2 = |F[Ψ(r)]|2. We want to find Ψ(r) given a few differently coded diffraction pattern measurements yn = |F[HnΨ(r)]|2, where the matrices Hn encode the mask structure of the aperture. This is a nonlinear inverse problem, but has been shown to be solvable even in the underdetermined case [6]. Since each diffraction pattern yn contains diffraction information from selected regions of the same sample, the differences in each pattern contain local phase information, which can be combined to form a full estimate of the real-space wave-function[7]. References: [1] W. Pauli in “Die allgemeinen Prinzipien der Wellenmechanik“, ed. H Geiger and W Scheel, (Julius Springer, Berlin). [2] A. Tonomura, Rev. Mod. Phys. 59 (1987), p. 639. [3] J. Miao et al, Nature 400 (1999), p. 342. [4] H. Lichte et al, Annu. Rev. Mater. Res. 37 (2007), p. 539. [5] J. Yamasaki et al, Appl. Phys. Lett. 101 (2012), 234105. [6] P Schniter and S Rangan. Signal Proc., IEEE Trans. on. 64(4), (2015), pp. 1043. [7] Supported by the Chemical Imaging, Signature Discovery, and Analytics in Motion initiatives at PNNL. PNNL is operated by Battelle Memorial Inst. for the US DOE; contract DE-AC05-76RL01830.

Schneider, Sebastian↗
DOE OSTI2019

Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs

Mass Spectrometry Imaging (MSI) characterizes changes in chemical composition between regions of biological samples such as tissues. This translates into a statistical goal of class comparison, i.e. determining analytes that change in abundance between conditions more systematically than as expected by random variation. To reach accurate and reproducible conclusions, statistical analysis must appropriately reflect the initial research question, the design of the MSI experiment, and all the associated sources of variation. This manuscript highlights the importance of following these general statistical principles. Using the example of two case studies with complex experimental designs, and with dierent strategies of data acquisition, we demonstrate the extent to which choices made at key points of this workflow impact the results, and provide suggestions for appropriate design and analysis of MSI experiments that aim at detecting dierentially abundant analytes.

NanoDESI, mass spectrometry, MSIQuickView↗