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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The white dwarf luminosity function - A possible probe of the galactic halo

The dynamically inferred dark halo mass density, amounting to above 0.01 solar masses/cu pc at the sun's Galactocentric radius, can be composed of faint white dwarfs provided that the halo formed in a sufficiently early burst of star formation. The model is constrained by the observed disk white dwarf luminosity function which falls off below log (L/solar L) = -4.4, due to the onset of star formation in the disk. By using a narrow range for the initial mass function and an exponentially decaying halo star formation rate with an e-folding time equal to the free-fall time, all the halo dark matter is allowed to be in cool white dwarfs which lie beyond the falloff in the disk luminosity function. Although it is unlikely that all the dark matter is in these dim white dwarfs, a definite signature in the low-luminosity end of the white dwarf luminosity function is predicted even if they comprise only 1 percent of the dark matter. Current CCD surveys should answer the question of the existence of this population within the next few years.

Tamanaha, Christopher M.↗
NASA NTRS2006

Multiple Waveband Temperature Sensor (MWTS)

This slide presentation reviews the development of Multiple Waveband Temperature Sensor (MWTS). The MWTS project will result in a highly stable, monolithically integrated, high resolution infrared detector array sensor that records registered thermal imagery in four infrared wavebands to infer dynamic temperature profiles on a laser-irradiated ground target. An accurate surface temperature measurement of a target in extreme environments in a non-intrusive manner is required. The development challenge is to: determine optimum wavebands (suitable for target temperatures, nature of the targets and environments) to measure accurate target surface temperature independent of the emissivity, integrate simultaneously readable multiband Quantum Well Infrared Photodetectors (QWIPs) in a single monolithic focal plane array (FPA) sensor and to integrate the hardware/software and system calibration for remote temperature measurements. The charge was therefore to develop and demonstrate a multiband infrared imaging camera with the detectors simultaneously sensitive to multiple distinct color bands for front surface temperature measurements Wavelength ( m) measurements. Amongst the requirements are: that the measurement system will not affect target dynamics or response to the laser irradiation and that the simplest criterion for spectral band selection is to choose those practically feasible spectral bands that create the most contrast between the objects or scenes of interest in the expected environmental conditions. There is in the presentation a review of the modeling and simulation of multi-wave infrared temperature measurement and also a review of the detector development and QWIP capacities.

infrared photodector↗
NASA NTRS2010

The Origin of Dust in the Early Universe: Probing the Star Formation History of Galaxies by Their Dust Content

Two distinct scenarios for the origin of the approximately 4 x 10(exp 8) Solar Mass of dust observed in the high-redshift (z = 6.4) quasar J1148+5251 have been proposed. The first assumes that this galaxy is much younger than the age of the universe at that epoch so that only supernovae, could have produced this dust. The second scenario assumes a significantly older galactic age, so that the dust could have formed in lower-mass AGB stars. Presenting new integral solutions for the chemical evolution of metals and dust in galaxies, we offer a critical evaluation of these two scenarios. ^N;"(,, show that the AGB scenario is sensitive to the details of the galaxy's star formation history (SFH), which must consist of an early intense starburst followed by a period of low stellar activity. The presence or absence of massive amounts of dust in high-redshift galaxies can therefore be used to infer their SFH. However, a problem with the AGB scenario is that it produces a stellar mass that is significantly larger than the inferred dynamical mass of J1148+5251, an yet unresolved discrepancy. If this problem persists, then additional sites for the growth or formation of dust, such as molecular clouds or dense clouds around active galactic nuclei, must be considered.

Dwek, Eli↗
DOE OSTI2023

Structural inference of networked dynamical systems with universal differential equations

Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinearity drives populations of identical (or near-identical) units to exhibit a wide range of nontrivial behaviors, such as the emergence of coherent structures (e.g., waves and patterns) or otherwise notable dynamics (e.g., synchrony and chaos). Here, we seek to infer (i) the intrinsic physics of a base unit of a population, (ii) the underlying graphical structure shared between units, and (iii) the coupling physics of a given networked dynamical system given observations of nodal states. These tasks are formulated around the notion of the Universal Differential Equation, whereby unknown dynamical systems can be approximated with neural networks, mathematical terms known a priori (albeit with unknown parameterizations), or combinations of the two. We demonstrate the value of these inference tasks by investigating not only future state predictions but also the inference of system behavior on varied network topologies. The effectiveness and utility of these methods are shown with their application to canonical networked nonlinear coupled oscillators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Weak-form inference for hybrid dynamical systems in ecology

Species subject to predation and environmental threats commonly exhibit variable periods of population boom and bust over long timescales. Understanding and predicting such behaviour, especially given the inherent heterogeneity and stochasticity of exogenous driving factors over short timescales, is an ongoing challenge. A modelling paradigm gaining popularity in the ecological sciences for such multi-scale effects is to couple short-term continuous dynamics to long-term discrete updates. We develop a data-driven method utilizing weak-form equation learning to extract such hybrid governing equations for population dynamics and to estimate the requisite parameters using sparse intermittent measurements of the discrete and continuous variables. The method produces a set of short-term continuous dynamical system equations parametrized by long-term variables, and long-term discrete equations parametrized by short-term variables, allowing direct assessment of interdependencies between the two timescales. We demonstrate the utility of the method on a variety of ecological scenarios and provide extensive tests using models previously derived for epizootics experienced by the North American spongy moth ( Lymantria dispar dispar ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Large-scale Nonlinear Approaches for Inference of Reporting Dynamics and Unobserved SARS-CoV-2 Infections

This work focuses on estimation of unknown states and parameters in a discrete-time, stochastic, SEIR model using reported case counts and mortality data. An SEIR model is based on classifying individuals with respect to their status in regards to the progression of the disease, where S is the number individuals who remain susceptible to the disease, E is the number of individuals who have been exposed to the disease but not yet infectious, I is the number of individuals who are currently infectious, and R is the number of recovered individuals. For convenience, we include in our notation the number of infections or transmissions, T, that represents the number of individuals transitioning from compartment S to compartment E over a particular interval. Similarly, we use C to represent the number of reported cases.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Inferences from the dynamical history of Mercury's rotation

The history of Mercury's spin angular momentum is reviewed. It is shown that the current nonsynchronous but resonant spin and the nearly zero obliquity place almost no restrictions on the primordial spin state. The only exception comes about from a liquid core-solid mantle interaction which excludes a slow primordial spin concurrent with a large obliquity. The current occupancy of a final evolutionary spin state leads to the description of a scheme by which we can determine the extent of a currently liquid Mercurian core.

Peale, S. J.↗
NASA NTRS1983

Optimal inference with chaotic dynamics

Nonlinear mappings that exhibit chaotic, seemingly random, evolution have appeal as models of dynamic systems. Their deterministic evolution, vis-a-vis Markov evolutions, results in much simpler optimal detection and estimation algorithms. The variation of a chaotic parameter (mu) results in diverse evolutions, suggesting a simple but rich source of model variations. For the specific mapping examined, this latter possibility is problematic due to the extreme sensitivity on mu of the evolution in the chaotic regime.

Harger, R. O.↗
NASA NTRS1987

Inference of physical/biological dynamics from synthetic ocean colour images

High resolution numerical experiments with well resolved eddies are performed including advection of a biologically active plankton field. Shelf wave propagation and bottom topographic features are included. The resulting synthetic ocean color fields are examined for sensitivity to the (known) underlying physical dynamics.

Eert, J.↗
NASA NTRS2000

Inferring Small Scale Dynamics from Aircraft Measurements of Tracers

The millions of ER-2 and DC-8 aircraft measurements of long-lived tracers in the Upper Troposphere/Lower Stratosphere (UT/LS) hold enormous potential as a source of statistical information about subgrid scale dynamics. Extracting this information however can be extremely difficult because the measurements are made along a 1-D transect through fields that are highly anisotropic in all three dimensions. Some of the challenges and limitations posed by both the instrumentation and platform are illustrated within the context of the problem of using the data to obtain an estimate of the dissipation scale. This presentation will also include some tutorial remarks about the conditional and two-point statistics used in the analysis.

Sparling, L. C.↗
NASA NTRS2006

Parameter and Structure Inference for Nonlinear Dynamical Systems

A great many systems can be modeled in the non-linear dynamical systems framework, as x = f(x) + xi(t), where f() is the potential function for the system, and xi is the excitation noise. Modeling the potential using a set of basis functions, we derive the posterior for the basis coefficients. A more challenging problem is to determine the set of basis functions that are required to model a particular system. We show that using the Bayesian Information Criteria (BIC) to rank models, and the beam search technique, that we can accurately determine the structure of simple non-linear dynamical system models, and the structure of the coupling between non-linear dynamical systems where the individual systems are known. This last case has important ecological applications.

Morris, Robin D.↗
NASA NTRS2016

Diverse Eruptions at Approximately 2,200 Years B.P. on the Great Rift, Idaho: Inferences for Magma Dynamics Along Volcanic Rift Zones

Compositionally and morphologically diverse lava flows erupted on the Great Rift of Idaho approximately 2.2 ka (kilo-annum, 1000 years ago) during a volcanic "flare-up" of activity following an approximately 2 ky (kiloyear, 1000 years) hiatus in eruptions. Volcanism at Craters of the Moon (COTM), Wapi and Kings Bowl lava fields around this time included primitive and evolved compositions, separated over 75 kilometers along the approximately 85 kilometers-long rift, with striking variability in lava flow emplacement mechanisms and surface morphologies. Although the temporal associations may be coincidental, the system provides a planetary analog to better understand magma dynamics along rift systems, including that associated with lunar floor-fractured craters. This study aims to help bridge the knowledge gap between ancient rift volcanism evident on the Moon and other terrestrial planets, and active rift volcanism, e.g., at Hawai'i and Iceland.

RIFT ZONEs↗
DOE OSTI2022

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

Here, we propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., multiple “snapshots.” Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots. By training a physics-informed generative model that generates “fake” sample paths, we aim to fit the observed particle ensemble distributions with a curve in the probability measure space, which is induced from the inferred particle dynamics. We employ different metrics to quantify the differences between distributions, e.g., the sliced Wasserstein distances and the adversarial losses in generative adversarial networks. We refer to this method as generative “ensemble-regression” (GER), in analogy to the classic “point-regression,” where we infer the dynamics by performing regression in the Euclidean space. We illustrate the GER by learning the drift and diffusion terms of particle ensembles governed by SODEs with Brownian motions and Lévy processes up to 100 dimensions. We also discuss how to treat cases with noisy or truncated observations. Apart from systems consisting of independent particles, we also tackle nonlocal interacting particle systems with unknown interaction potential parameters by constructing a physics-informed loss function. Finally, we investigate scenarios of paired observations and discuss how to reduce the dimensionality in such cases by proving a convergence theorem that provides theoretical support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning

We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity partial differential equation (PDE) models governed by autonomous dynamical systems, leading to large-scale forward problems; (2) they solve a linear inverse problem to assimilate observational data to infer uncertain model components followed by a forward prediction of the evolving dynamics; and (3) the entire end-to-end, data-to-inference-to-prediction computation is carried out without approximation and in real time through a Bayesian framework that rigorously accounts for uncertainties. Several challenges must be overcome to realize this framework, including the large scale of the forward problem, the high dimensionality of the parameter space, and for a class of problems including those we target, the slow decay of the singular values of the parameter-to-observable map. Here we introduce a methodology to overcome these challenges by exploiting the autonomous structure of the forward model to decompose the solution of the inverse problem into a one-time-only offline phase in which the PDE model is solved a limited number of times (equal to the number of sensors), and an online phase that maps well onto GPUs and computes the parameter inference and prediction of quantities of interest in real time, given observational data. Our ultimate goal is to apply this framework to construct digital twins for subduction zones, including Cascadia, to provide early warning for tsunamis generated by megathrust earthquakes. To this end, we demonstrate how our methodology can be used to employ seafloor pressure observations, along with the coupled acoustic–gravity wave equations, to infer the earthquake-induced spatiotemporal seafloor motion (discretized with $\mathscr{O}$ (10 9 ) parameters) and forward predict the tsunami propagation. We present results of an end-to-end inference, prediction, and uncertainty quantification for a representative test problem with $\mathscr{O}$ (10 8 ) inversion parameters for which goal-oriented Bayesian inference is accomplished exactly and in real time, that is, in a matter of seconds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An iterative learning and inference approach to managing dynamic cyber-vulnerabilities of complex systems

As modern systems become increasingly reliant on cyber-technologies and continue to be integrated with physical systems, managing risks from deliberate and non-deliberate sources is a significant research challenge. Unlike strictly physical systems, cyber-enabled physical systems are influenced by dynamic and evolving technologies, environments, and attack mechanisms. As a result, vulnerabilities are rapidly changing and difficult to detect and manage. While there is recent interest in the dynamic properties of performance through resilience analysis, limited research addresses the dynamic nature of cyber-system vulnerability. This paper presents a dynamic iterative learning approach to evaluate the state of health for systems as related to cyber-vulnerabilities. These time-varying system health characteristics may not be visible, but can be inferred using observable characteristics. The approach recognizes that multiple types of vulnerabilities need to be included in a holistic system health assessment. The methods are applied to the Common Vulnerability Scoring System database containing over 100,000 documented cybersecurity vulnerabilities. This work is the first to acknowledge the dynamic properties of cyber-vulnerability, while also inferring system health using observable data and hidden states of system health. This work will be of interest to managers of large-scale cyber-enabled physical systems who are seeking to prioritize system health investments.

Chatterjee, Samrat↗
DOE OSTI2021

Renewable energy integration and system operation challenge: control and optimization of millions of devices

The electric power infrastructure, originally designed and built on large-scale power plants, is evolving into a more resilient power generation and delivery system in which millions of smaller units of distributed energy generation resources units will be installed in sub-transmission and distribution networks. In order to control, manage and optimize the future grid, a hierarchical design is presented in this chapter which enables the distributed control on grid edge while inheriting the existing centralized control structure. This layered design of large-scale power system operation and control uses the following principle: reactive power control is treated as a primary control for voltage stability, and the real power control is primarily a grid-level control but can also be a supplementary control for voltage support in the case of insufficient reactive power control capacity. For the purpose of active control and operation at the distribution level, a recursive power network model is derived from nodal injection and branch power flow models. Based on the model, the proposed algorithms of hierarchical control, grid-edge inference and dynamic hosting allowance are developed and presented for multi-level controlled operation. And, a co-simulation architecture of integrated T&D system is presented to validate and demonstrate the feasibility and scalability of proposed algorithms.

Xu, Ying↗