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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 289 records · Page 16

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NASA NTRS1992

Spectroscopy of emission-line nebulae in powerful radio galaxies - Interpretation

Long-slit optical spectra of the emission-line nebulae associated with 21 low-redshift (less than 0.2) radio galaxies are analyzed. Nebulae are classified kinematically into three types: rotators, calm nonrotators, and violent nonrotators; these types are characterized. It is proposed that the rotators have dynamically young disks of gas recently acquired by the radio galaxy in an interaction or merger with a gas-rich galaxy. This is consistent with the data on the morphologies, colors, and stellar dynamics of radio galaxies with strong emission lines. It is inferred from the association of the large-scale gas kinematics with the radio and optical properties of an active galaxy that the angular momentum of the gas which fuels the AGN may be an important parameter in the determinant of how activity is manifest in an AGN.

Baum, S. A.↗

NASA NTRS1993

The Galileo Earth encounter - Magnetometer and allied measurements

An overview of the Galileo magnetometer observations from the crossing of the tail magnetopause at an antisolar distance of close to 100 R(E) through exit into the solar wind on the dayside is presented. These measurements are linked with correlative data from ground stations and from IMP 8 which was ideally located to serve as a monitor of the solar wind upstream of the bow shock. A time line of the important geomagnetic events of the day that provides a framework for the full multiinstrument analysis of the flyby data is presented. The observations are used to investigate apsects of the relationship between magnetotail dynamics and the separate intensifications of a multiple onset substorm inferred from ground-based data. It is proposed that the signatures associated with individual substorm intensifications are localized in the dawn-to-dusk extent even at remote locations in the magnetotail, just as they are in the ionosphere, and that the tail disturbances associated with successive substorm intensifications step across the tail towards the dusk flank.

Kivelson, M. G.↗

NASA NTRS1994

Tectonic plates, D (double prime) thermal structure, and the nature of mantle plumes

It is proposed that subducting tectonic plates can affect the nature of thermal mantle plumes by determining the temperature drop across a plume source layer. The temperature drop affects source layer stability and the morphology of plumes emitted from it. Numerical models are presented to demonstrate how introduction of platelike behavior in a convecting temperature dependent medium, driven by a combination of internal and basal heating, can increase the temperature drop across the lower boundary layer. The temperature drop increases dramatically following introduction of platelike behavior due to formation of a cold temperature inversion above the lower boundary layer. This thermal inversion, induced by deposition of upper boundary layer material to the system base, decays in time, but the temperature drop across the lower boundary layer always remains considerably higher than in models lacking platelike behavior. On the basis of model-inferred boundary layer temperature drops and previous studies of plume dynamics, we argue that generally accepted notions as to the nature of mantle plumes on Earth may hinge on the presence of plates. The implication for Mars and Venus, planets apparently lacking plate tectonics, is that mantle plumes of these planets may differ morphologically from those of Earth. A corollary model-based argument is that as a result of slab-induced thermal inversions above the core mantle boundary the lower most mantle may be subadiabatic, on average (in space and time), if major plate reorganization timescales are less than those acquired to diffuse newly deposited slab material.

Lenardic, A.↗

NASA NTRS1997

The Joint Damping Experiment (JDX)

The Joint Damping Experiment (JDX), flown on the Shuttle STS-69 Mission, is designed to measure the influence of gravity on the structural damping of a high precision three bay truss. Principal objectives are: (1) Measure vibration damping of a small-scale, pinjointed truss to determine how pin gaps give rise to gravity-dependent damping rates; (2) Evaluate the applicability of ground and low-g aircraft tests for predicting on-orbit behavior; and (3) Evaluate the ability of current nonlinear finite element codes to model the dynamic behavior of the truss. Damping of the truss was inferred from 'Twang' tests that involve plucking the truss structure and recording the decay of the oscillations. Results are summarized as follows. (1) Damping, rates can change by a factor of 3 to 8 through changing the truss orientation; (2) The addition of a few pinned joints to a truss structure can increase the damping by a factor as high as 30; (3) Damping is amplitude dependent; (4) As gravity induced preloads become large (truss long axis perpendicular to gravity vector) the damping is similar to non-pinjointed truss; (5) Impacting in joints drives higher modes in structure; (6) The torsion mode disappears if gravity induced preloads are low.

Folkman, Steven L.↗

NASA NTRS1995

Aerothermodynamic Facilities And Measurement: Flow Characterization in Shock Tunnels

This presentation will examine the key performance aspects of shock tunnels as they relate to their use as aerothermodynamic flow simulation facilities. Assessment of shock tube reservoir conditions and flow contaminants generated in the shock tube will be presented along with their limiting impact on viable test envelopes, Facility nozzle performance as it pertains to test time assessment and nozzle exit flow quality (survey of pressure, temperature, and species) will be addressed. Also included will be a discussion of free stream flow diagnostics, both intrusive and nonintrusive, for measurement of critical flow properties not directly inferred from surface mounted transducers. The use of computational fluid dynamics for purposes of validating experimental measurements as well as predicting performance in regimes where measurements are not feasible or possible will be discussed. The use of CFD for facility research and design will also be presented.

Cavolowsky, John A.↗

NASA NTRS2003

Dayside Proton Aurora: Comparisons between Global MHD Simulations and Image Observations

The IMAGE mission provides a unique opportunity to evaluate the accuracy of current global models of the solar wind interaction with the Earth's magnetosphere. In particular, images of proton auroras from the Far Ultraviolet Instrument (FUV) onboard the IMAGE spacecraft are well suited to support investigations of the response of the Earth's magnetosphere to interplanetary disturbances. Accordingly, we have modeled two events that occurred on June 8 and July 28, 2000, using plasma and magnetic field parameters measured upstream of the bow shock as input to three-dimensional magnetohydrodynamic (MHD) simulations. This paper begins with a discussion of images of proton auroras from the FUV SI-12 instrument in comparison with the simulation results. The comparison showed a very good agreement between intensifications in the auroral emissions measured by FUV SI-12 and the enhancement of plasma flows into the dayside ionosphere predicted by the global simulations. Subsequently, the IMAGE observations are analyzed in the context of the dayside magnetosphere's topological changes in magnetic field and plasma flows inferred from the simulation results. Finding include that the global dynamics of the auroral proton precipitation patterns observed by IMAGE are consistent with magnetic field reconnection occurring as a continuous process while the iMF changes in direction and the solar wind dynamic pressure varies. The global simulations also indicate that some of the transient patterns observed by IMAGE are consistent with sporadic reconnection processes. Global merging patterns found in the simulations agree with the antiparallel merging model. though locally component merging might broaden the merging region, especially in the region where shocked solar wind discontinuities first reach the magnetopause. Finally, the simulations predict the accretion of plasma near the bow shock in the regions threaded by newly open field lines on which plasma flows into the dayside ionosphere are enhanced. Overall the results of these initial comparisons between global MHD simulation results and IMAGE observations emphasize the interplay between reconnection and dynamic pressure processes at the dayside magnetopause. as well as the intricate connection between the bow shock and the auroral region.

Berchem, J.↗

DOE OSTI2023

Design and Characterization of a Lens-Coupled System for Dynamic X-Ray Diffraction

X-ray diffraction (XRD) is a necessary technique for understanding states of materials under static and dynamic loading conditions. The higher-pressure Equation of State (EOS) of many materials can only be explored via shock or ramp compression at temperatures and pressures of interest. While static XRD work has yielded EOS measurements in the 100 - 200 GPa regime, dynamic X-ray diffraction (DXRD) can explore EOS phases in the TPa regime, which closely resembles inner-core planetary conditions. DXRD hinges on the ability to measure the exact phase or phase change of a material while under dynamic loading conditions. Macroscopic diagnostic systems (e.g. velocimetry and pyrometry) can infer a phase change but not identify the specific phase entered by a material. While microscopic (atomic-level) diagnostic systems (e.g. DXRD) have been designed and implemented in Department of Energy’s (DOE) National Laboratories complex, the unique nature of Sandia National Laboratories’ Pulsed Power Facility (Z Machine) prohibits the use of such devices. The destructive nature of Z experiments presents a challenge to data capture and retrieval. Furthermore there are electromagnetic interference, X-ray background, and mechanical constraints to consider. Thus, a multi-part X-ray diagnostic for use on the Z Machine and Z-Beamlet Laser system has been designed and analyzed. Portions of this new DYnamic SCintillator Optic (DYSCO) have been built, tested and fielded. A data analysis software has been written. Finally, the radiance profile of the DYSCO’s scintillator has been characterized through experiments performed at the University of Arizona.

36 MATERIALS SCIENCE↗

NASA NTRS2005

Large-Scale Experimental Planetary Science Meets Planetary Defense: Deorbiting an Asteroidal Satellite

Other than remote-sensing and spacecraft-derived data, the only information that exists regarding the physical and chemical properties of asteroids is that inferred through calculations, numerical simulations, extrapolation of experiments, and meteorite studies. Our understanding of the dynamics of accretion of planetesimals, collisional disruption of asteroids, and the macroscopic, shock-induced modification of the surfaces of such small objects is also, for the most part, founded on similar inferences. While considerable strides have been made in improving the state of asteroid science, too many unknowns remain to assert that we understand the parameters necessary for the more practical problem of deflecting an asteroid or asteroid pair on an Earth-intersecting trajectory. Many of these deficiencies could be reduced or eliminated by intentionally deorbiting an asteroidal satellite and monitoring the resulting collision between it and the primary asteroid, a capability that is well within the limitations of current technology.

Cintala, M. J.↗

DOE OSTI2020

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Abstract Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Neural Ordinary Differential Equations for Nonlinear System Identification

Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state-of-the-art nonlinear and classical linear methods. In particular, we present a quantitative study comparing NODE's performance against neural state-space models and classical linear system identification methods and evaluate their inference speed and prediction performance on open-loop errors across eight different dynamical systems. The experiments show that NODEs can consistently improve the prediction accuracy by order of magnitude compared to benchmark methods. Besides improved accuracy, we also observed that NODEs are less sensitive to hyperparameters compared to neural state-space models by paying the cost of increased computation at the inference time.

machine leaning, system identification, physics in↗

NASA NTRS2022

Advances and Challenges in Observations and Modeling of the Global-Sun Dynamics and Dynamo

Computational heliophysics has shed light on the fundamental physical processes inside the Sun, such as the differential rotation, meridional circulation, and dynamo-generation of magnetic fields. However, despite the substantial advances, the current results of 3D MHD simulations are still far from reproducing helioseismic inferences and surface observations. The reason is the multi-scale nature of the solar dynamics, covering a vast range of scales, which can-not be solved with the current computational resources. In such a situation, significant progress has been achieved by the mean-field approach, based on the separation of small-scale turbulence and large-scale dynamics. The mean-field simulations can reproduce solar observations, qualitatively and quantitatively, and uncover new phenomena. However, they do not reveal the complex physics of large-scale convection, solar magnetic cycles, and the magnetic self-organization that causes sunspots and solar eruptions. Thus, developing a synergy of these approaches seems to be a necessary but very challenging task.

SMD↗

NASA NTRS2022

Direct-imaging Discovery and Dynamical Mass of a Substellar Companion Orbiting an Accelerating Hyades Sun-like Star with SCExAO/CHARIS

We present the direct-imaging discovery of a substellar companion in orbit around a Sun-like star member of the Hyades open cluster. So far, no other substellar companions have been unambiguously confirmed via direct imaging around main-sequence stars in Hyades. The star HIP 21152 is an accelerating star as identified by the astrometry from the Gaia and Hipparcos satellites. We detected the companion, HIP 21152 B, in multiple epochs using the high-contrast imaging from SCExAO/CHARIS and Keck/NIRC2. We also obtained the stellar radialvelocity data from the Okayama 188 cm telescope. The CHARIS spectroscopy reveals that HIP 21152 B’s spectrum is consistent with the L/T transition, best fit by an early T dwarf. Our orbit modeling determines the semimajor axis and the dynamical mass of HIP 21152 B to be 17 +7.2 −3.8 and 27.8 +8.4 −5.4 M Jub , respectively. The mass ratio of HIP 21152 B relative to its host is ≈2%, near the planet/brown dwarf boundary suggested by recent surveys. Mass estimates inferred from luminosity-evolution models are slightly higher (33–42 MJup). With a dynamical mass and a well-constrained age due to the system’s Hyades membership, HIP 21152 B will become a critical benchmark in understanding the formation, evolution, and atmosphere of a substellar object as a function of mass and age. Our discovery is yet another key proof of concept for using precision astrometry to select direct-imaging targets.

Masayuki Kuzuhara↗

DOE OSTI2022

Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms

Machine learning methods have been extensively used to study the dynamics of complex fluid flows. One such algorithm, known as adaptive neural fuzzy inference system (ANFIS), can generate data-driven predictions for flow fields, but has not been applied to natural geophysical flows in large-scale rivers. Herein, we demonstrate the potential of ANFIS to produce three-dimensional (3D) realizations of the instantaneous flood flow field in several large-scale, virtual meandering rivers. The 3D dynamics of flood flow in large-scale rivers were obtained using large-eddy simulation (LES). The LES results, i.e., the 3D velocity components, were employed to train the learnable coefficients of an ANFIS. Further, the trained ANFIS, along with a few time-steps of LES results (precursor data) were then used to produce 3D realizations of flood flow fields in large-scale rivers with geometries other than the one the ANFIS was trained with. We also used the trained ANFIS to generate 3D realizations of river flow at a discharge other than that the ANFIS was trained with. The flow field results obtained from ANFIS were validated using separate LES runs to assess the accuracy of the 3D instantaneous realizations of the machine learning algorithm. An error analysis was conducted to quantify the discrepancies among the ANFIS and LES results for various flood flow predictions in large-scale rivers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? In conclusion, the analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2023

From Remotely-Sensed SIF to Ecosystem Structure, Function, and Service: Part I - Harnessing Theory

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5–10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question—How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

carbon fluxes↗

DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗