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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking

We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoax, and propaganda to find linguistic cues for untruthful text. To probe the feasibility of automatic political fact-checking, we present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experimental results show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.

fact-checking↗
DOE OSTI2020

Scalable Knowledge Graph Analytics at 136 Petaflop/s

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (Distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136×1015 floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale.

Kannan, Ramakrishnan {ramki}↗
DOE OSTI2022

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis to Support Risk Model Parameter Estimations

This set of slides has been prepared for a talk at the INL AI/ML Symposium held on September 8, 2022. Presentation outline: Background - Nuclear power plant operating experience data sources • Research focus and motivation - Analyzing free-text operating experience data: present and future • Research method - Input - Methodological steps - Output • Conclusions and next steps

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysis

ABSTRACT We present a neural network-based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3$\sigma$ accuracy better than 0.5 per cent up to $k_{\mathrm{max}}$ = 0.25 $\, h\text{Mpc}^{-1}$ within Lambda-cold dark matter ($\Lambda$CDM) and around 0.5 per cent $w_0$–$w_a$CDM. Our surrogate model only emulates the galaxy bias-invariant terms of one-loop perturbation theory predictions, these terms are then combined analytically with galaxy bias terms, counter-terms, and stochastic terms in order to obtain the non-linear redshift-space galaxy power spectrum. This allows us to avoid any galaxy bias prescription in the training of the emulator, which makes it more flexible. Moreover, we include the redshift $z \in [0,1.4]$ in the training which further avoids the need for re-training the emulator. We showcase the performance of the emulator in recovering the cosmological parameters of $\Lambda$CDM by analysing the suite of 25 AbacusSummit simulations that mimic the Dark Energy Spectroscopic Instrument luminous red galaxies at $z=0.5$ and 0.8, together as the emission line galaxies at $z=0.8$. We obtain similar performance in all cases, demonstrating the reliability of the emulator for any galaxy sample at any redshift in $0 \lt z \lt 1.4$. We will make our emulator public at github repository.

Trusov, Svyatoslav (ORCID:0000000224146720)↗
NASA NTRS2024

SUPIN: A Tool for the Aerodynamic Design and Analysis of Supersonic Inlets

The Supersonic Inlet Design and Analysis Tool (SUPIN) is a computational program that performs geometric modeling and aerodynamic design and analysis of a set of supersonic inlets whose types include the axisymmetric pitot, three-dimensional pitot, axisymmetric, two-dimensional, two-dimensional bifurcated-duct, and streamline-traced inlets. The inlets are modeled by dividing the inlet flowpath into components that start at the freestream and ends at the engine face. The primary components of the inlet include the external supersonic diffuser, throat section, and subsonic diffuser. Each component is characterized by a set of inputs that include geometric and aerodynamic factors. Examples of the geometric factors are angles, lengths, area ratios, and coordinates that describe the geometry of the components. Examples of the aerodynamic factors are Mach numbers, flow ratios, and pressure ratios that set constraints or desired aerodynamic properties of the component. These input factors are specified within a text-based input data file that is read into SUPIN. SUPIN is a Fortran 95 program that performs the inlet design and analysis as a batch or non-interactive process. SUPIN writes output data files describing the inlet geometry and aerodynamic performance. SUPIN uses analytic, empirical, and numerical methods to perform the inlet design and analysis. The inlet geometry is constructed using planar and three-dimensional geometry constructs and is represented by coordinates, angles, areas, profiles, and surfaces. Surfaces are output in the form of a Plot3D surface grid file. SUPIN does have some capability to automatically generate planar and three-dimensional, multi-block, structured grids for computational fluid dynamics (CFD) analysis. The aerodynamic analysis performed by SUPIN computes quasi-one-dimensional properties, such as Mach numbers, pressures, and temperatures, through the inlet flowpath. Planar aerodynamic properties, such as those for shock and Mach waves, are computed for the supersonic compression of the external supersonic diffuser and throat section. The inlet performance is characterized by the inlet flow rates, total pressure recovery, and inlet drag. The primary intent of this document is to describe the usage of SUPIN. The methods used within SUPIN are documented and may serve as a useful reference for understanding the aerodynamic design and analysis of supersonic inlets.

supersonic inlets↗
DOE OSTI2019

Time-strain separability in medium-amplitude oscillatory shear

Here, we derive and study equations for the weakly nonlinear medium-amplitude oscillatory shear (MAOS) response of materials exhibiting time-strain separability. Results apply to constitutive models with arbitrary linear memory function $\textit{m}$(s) and for both viscoelastic liquids and viscoelastic solids. The derived equations serve as a reference to identify which models are time-strain separable (TSS) and which may appear separable but are not, in the weakly nonlinear limit. More importantly, we study how the linear viscoelastic (LVE) relaxation spectrum, H($_\tau$), affects the frequency dependence of the TSS MAOS material functions. Continuous relaxation spectra are considered that are associated with analytical functions (log-normal and asymmetric Lorentzian distributions), fractional mechanical models (Maxwell and Zener), and molecular theories (Rouse and Doi-Edwards). TSS MAOS signatures reveal much more than just the perturbation parameter A in the shear damping function small-strain expansion, $h(_γ) = 1 + \text{A}_γ$$^2 + \mathcal{O}(_γ$$^4)$. Specifically, the distribution of terminal relaxation times is significantly more apparent in the TSS MAOS functions than their LVE counterparts. We theoretically show that this occurs because TSS MAOS material functions are sensitive to higher-order moments of the relaxation spectrum, which leads to the definition of MAOS liquids. We also show the first examples of MAOS signatures that differ from the liquid-like terminal MAOS behavior predicted by the fourth-order fluid expansion. This occurs when higher moments of the relaxation spectrum are not finite. The famous corotational Maxwell model is a subset of our results here, for which A = -1/6, and any LVE relaxation spectrum could be used.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Understanding Brønsted-Acid Catalyzed Monomolecular Reactions of Alkanes in Zeolite Pores by Combining Insights from Experiment and Theory

The front cover artwork is provided by Bell and co-workers. The image shows a butane molecule adsorbed at a Brønsted acid site inside the pores of zeolite H-MFI, surrounded by the products of monomolecular cracking and dehydrogenation reactions catalyzed by these sites. Read the full text of the Minireview at 10.1002/cphc.201701084.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

On the Rate Constant for NH 2 +HO 2 and Third-Body Collision Efficiencies for NH 2 +H(+M) and NH 2 +NH 2 (+M)

In low-temperature flash photolysis of NH 3 /O 2 /N 2 mixtures, the NH 2 consumption rate and the product distribution is controlled by the reactions NH 2 + HO 2 → products (R1), NH 2 + H (+M) → NH 3 (+M) (R2), and NH 2 + NH 2 (+M) → N 2 H 4 (+M) (R3). In the present work, published flash photolysis experiments by, among others, Cheskis and co-workers, are re-interpreted using recent direct measurements of NH 2 + H (+N 2 ) and NH 2 + NH 2 (+N 2 ) from Altinay and Macdonald. To facilitate analysis of the FP data, relative third-body collision efficiencies compared to N 2 for R2 and R3 were calculated for O 2 and NH 3 as well as for other selected molecules. We report results were in good agreement with the limited experimental data. Based on reported NH 2 decay rates in flash photolysis of NH 3 /O 2 /N 2 , a rate constant for NH 2 + HO 2 → NH 3 + O 2 (R1a) of $k_{1\text{a}}$ = 1.5(±0.5) × 10 14 cm 3 mol –1 s –1 at 295 K was derived. This value is higher than earlier determinations based on the FP results but in good agreement with recent theoretical work. Kinetic modeling of reported N 2 O yields indicates that NH 2 + HO 2 → H 2 NO + O (R1c) is competing with R1a, but perturbation experiments with addition of CH4 indicate that it is not a dominating channel. Measured HNO profiles indicate that this component is formed directly by NH 2 + HO 2 → HNO + H 2 O (R1b), but theoretical work indicates that R1b is only a minor channel. Based on this analysis, we estimate $k_{1\text{c}}$ = 2.5 × 10 13 cm 3 mol –1 s –1 and $k_{1\text{b}}$ = 2.5 × 10 12 cm 3 mol –1 s –1 at 295 K, with significant uncertainty margins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1961

Radiant Heat Transfer Between Nongray Parallel Plates of Tungsten

Net radiant heat flow between two infinite, parallel, tungsten plates was computed by summing the monochromatic energy exchange; the results are graphically presented as a function of the temperatures of the two surfaces. In general these fluxes range from approximately a to 25 percent greater than the results of gray-body computations based on the same emissivity data. The selection of spectral emissivity data and the computational procedure are discussed. The present analytical procedure is so arranged that, as spectral emissivity data for a material become available, these data can be readily introduced into the NASA data-reduction equipment, which has been programmed to compute the net heat flux for the particular geometry and basic assumptions cited in the text. Nongray-body computational techniques for determining radiant heat flux appear practical provided the combination of select spectral emissivity data and the proper mechanized data-reduction equipment are brought to bear on the problem.

Branstetter, J. Robert↗
NASA NTRS1990

Inelastic response of metal matrix composites under biaxial loading

Elements of the analytical/experimental program to characterize the response of silicon carbide titanium (SCS-6/Ti-15-3) composite tubes under biaxial loading are outlined. The analytical program comprises prediction of initial yielding and subsequent inelastic response of unidirectional and angle-ply silicon carbide titanium tubes using a combined micromechanics approach and laminate analysis. The micromechanics approach is based on the method of cells model and has the capability of generating the effective thermomechanical response of metal matrix composites in the linear and inelastic region in the presence of temperature and time-dependent properties of the individual constituents and imperfect bonding on the initial yield surfaces and inelastic response of (0) and (+ or - 45)sub s SCS-6/Ti-15-3 laminates loaded by different combinations of stresses. The generated analytical predictions will be compared with the experimental results. The experimental program comprises generation of initial yield surfaces, subsequent stress-strain curves and determination of failure loads of the SCS-6/Ti-15-3 tubes under selected loading conditions. The results of the analytical investigation are employed to define the actual loading paths for the experimental program. A brief overview of the experimental methodology is given. This includes the test capabilities of the Composite Mechanics Laboratory at the University of Virginia, the SCS-6/Ti-15-3 composite tubes secured from McDonnell Douglas Corporation, a text fixture specifically developed for combined axial-torsional loading, and the MTS combined axial-torsion loader that will be employed in the actual testing.

Mirzadeh, F.↗
DOE OSTI2023

Kinetics and Energetics of Electron Transfer to Dimer Radical Cations

In this work, spectra of the dimer cations naphthalene (Nap 2 •+ ) and ethene (Ethene 2 •+ ) were measured in liquid dichloromethane (DCM). The spectra peak at very different energies, 1.2 and 3.3 eV. In DCM dimerization stabilizes Nap 2 •+ by Δ$G_\text{d}°$(Nap 2 •+ ) = -218 meV relative to the monomer Nap •+ as determined from the dimerization equilibrium constant. Both dimers can transfer a positive charge to hole acceptor molecules, but for both the rate constants rise more gradually with reaction energetics than do many charge transfer reactions previously studied. A striking observation finds that the rate constant for hole transfer from the Nap 2 •+ dimer to phenanthrene is smaller by two decades than that from biphenyl•+ monomer to Nap, although both reactions have the same –Δ$\textit{G}°$=0.05 eV. A plausible interpretation for these observations is the presence of an energy of reorganization, λ(M 2 ), for the dimer that involves movement apart of the two partners in the dimer. While the dimerization equilibrium cannot be measured for Ethene 2 •+ the charge transfer data imply that both Δ$G_\text{d}°$(Ethene 2 •+ ) and λ(Ethene 2 •+ ), are considerably larger, perhaps by factors of 2-4 than for Nap 2 •+ .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Convective carbon dioxide dissolution in a closed porous medium at low pressure

Motivated by the persistence of natural carbon dioxide ( $\text{CO}_{2}$ ) fields, we investigate the convective dissolution of $\text{CO}_{2}$ at low pressure (below 1 MPa) in a closed system, where the pressure in the gas declines as convection proceeds. This introduces a negative feedback that reduces the convective dissolution rate even before the brine becomes saturated. We analyse the case of an ideal gas with a solubility given by Henry’s law, in the limits of very low and very high Rayleigh numbers. The equilibrium state in this system is determined by the dimensionless dissolution capacity, $\unicode[STIX]{x1D6F1}$ , which gives the fraction of the gas that can be dissolved into the underlying brine. Analytic approximations of the pure diffusion problem with $\unicode[STIX]{x1D6F1}>0$ show that the diffusive base state is no longer self-similar and that diffusive mass transfer declines rapidly with time. Direct numerical simulations at high Rayleigh numbers show that no constant flux regime exists for $\unicode[STIX]{x1D6F1}>0$ ; nevertheless, the quantity $F/C_{s}^{2}$ remains constant, where $F$ is the dissolution flux and $C_{s}$ is the dissolved concentration at the top of the domain. Simple mathematical models are developed to predict the evolution of $C_{s}$ and $F$ for high-Rayleigh-number convection in a closed system. The negative feedback that limits convection in closed systems may explain the persistence of natural $\text{CO}_{2}$ accumulations over millennial time scales.

Mechanics↗
DOE OSTI2021

Diving into a holographic superconductor

Charged black holes in anti-de Sitter space become unstable to forming charged scalar hair at low temperatures T < T_\text{c} T < T c . This phenomenon is a holographic realization of superconductivity. We look inside the horizon of these holographic superconductors and find intricate dynamical behavior. The spacetime ends at a spacelike Kasner singularity, and there is no Cauchy horizon. Before reaching the singularity, there are several intermediate regimes which we study both analytically and numerically. These include strong Josephson oscillations in the condensate and possible `Kasner inversions’ in which after many e-folds of expansion, the Einstein-Rosen bridge contracts towards the singularity. Due to the Josephson oscillations, the number of Kasner inversions depends very sensitively on T T , and diverges at a discrete set of temperatures \{T_n\} { T n } that accumulate at T_c T c . Near these T_n T n , the final Kasner exponent exhibits fractal-like behavior.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

MArVD2: a machine learning enhanced tool to discriminate between archaeal and bacterial viruses in viral datasets

Abstract Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses. Benchmarking showed that a model trained with viral sequences from the hypersaline, marine, and hot spring environments correctly classified 85% of the archaeal viruses with a false detection rate below 2% using a random forest prediction threshold of 80% in a separate benchmarking dataset from the same habitats.

Vik, Dean (ORCID:000000027546899X)↗
DOE OSTI2020

A low-luminosity core-collapse supernova very similar to SN 2005cs

ABSTRACT We present observations and analysis of PSN J17292918+7542390, a low-luminosity Type II-P supernova (LL SN IIP). The observed sample of such events is still low, and their nature is still under debate. Such SNe are similar to SN 2005cs, a well-observed LL Type II-P event, having low expansion velocities, and small ejected 56Ni mass. We have developed a robust and relatively fast Monte Carlo code that fits semi-analytic models to light curves of core-collapse SNe. This allows the estimation of the most important physical parameters, like the radius of the progenitor star, the mass of the ejected envelope, the mass of the radioactive nickel synthesized during the explosion, among others. PSN J17292918+7542390 has $R_0 = 91_{-70}^{+119} \times 10^{11} \, \text{cm}$, $M_\text{ej} = 9.89_{-1.00}^{+2.10} \, \mathrm{ M}_{\odot }$, $E_{\mbox{kin}} = 0.65_{-0.18}^{+0.19} \, \text{foe}$, and $v_{\mbox{exp}} = 3332_{-347}^{+216}$ km s−1, for its progenitor radius, ejecta mass, kinetic energy, and expansion velocity, respectively. The initial nickel mass of the PSN J17292918+7542390 turned out to be $1.55_{-0.70}^{+0.75} \times 10^{-3} \, \mathrm{M}_{\odot }$. The measured photospheric velocity at the earliest observed phase is 7000 km s−1. As far as we can tell based on the small population of observed LL SNe IIP, the determined values are typical for these events.

Jäger, Jr, Zoltán↗
DOE OSTI2024

The expressivity of classical and quantum neural networks on entanglement entropy

Abstract Analytically continuing the von Neumann entropy from Rényi entropies is a challenging task in quantum field theory. While then-th Rényi entropy can be computed using the replica method in the path integral representation of quantum field theory, the analytic continuation can only be achieved for some simple systems on a case-by-case basis. In this work, we propose a general framework to tackle this problem using classical and quantum neural networks with supervised learning. We begin by studying several examples with known von Neumann entropy, where the input data is generated by representing$${\text {Tr}}\rho _A^n$$ Tr ρ A n with a generating function. We adopt KerasTuner to determine the optimal network architecture and hyperparameters with limited data. In addition, we frame a similar problem in terms of quantum machine learning models, where the expressivity of the quantum models for the entanglement entropy as a partial Fourier series is established. Our proposed methods can accurately predict the von Neumann and Rényi entropies numerically, highlighting the potential of deep learning techniques for solving problems in quantum information theory.

Physics↗