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

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NASA NTRS2007

Magnetic Field Observations of Partial Ring Current during Storm Recovery Phase

We present results of an extensive survey of the magnetic field observations in the inner magnetosphere using 30 years of magnetospheric magnetic field data from Polar, Cluster, ISEE, and AMPTE/CCE missions. The purpose of this study is to understand the magnetic field evolution during the recovery phase of geomagnetic storms, and its implication to the ring current recovery and loss mechanisms of ring current particles. Our previous work on global ring current distribution [Le et al., 2004] has shown that a significant partial ring current is always present at all Dst levels (regardless of storm phases) even for quiet time ring current. The total current carried by the partial ring current is much stronger than (during stormtime) or at least comparable to (during quiet time) the symmetric ring current. It is now commonly believed that a strong partial ring current is formed during the storm main phase due to the enhanced earthward convection of energetic ions from nightside plasma sheet. But the presence of a strong partial ring current throughout the recovery phase remains controversial. The magnetic field generated by the ring current inflates the inner magnetosphere and causes magnetic field depressions in the equatorial magnetosphere. During the storm recovery phase, we find that the distribution of the equatorial magnetic field depression exhibits similar local time dependence as the ring current distribution obtained from the combined dataset in the earlier study. It shows that a strong partial ring current is a permanent feature throughout the recovery phase. In the early recovery phase, the partial ring current peaks near the dusk terminator as indicated by the peak of the magnetic field depression. As the recovery phase progresses, the partial ring current decays most quickly near the dusk and results in a dusk-to-midnight moving of the peak of the partial ring current. Thus the loss mechanisms work most effectively near the dusk. The magnetic field depression increases the gyroradius of ring current protons to a scale greater or comparable to the thickness of the magnetopause, which increases the chance of ion drift loss near the dusk magnetopause at larger L-shell (L>5). But the drift loss mechanism alone cannot explain the loss of ring current ions especially in the smaller L-shell (L<5). The precipitation loss due to wave-particle interaction is most likely the dominant loss mechanism in the small L-shell as it works most effectively at the same local time.

Le, Guan↗
NASA NTRS1995

On purported observations of partially ionized galactic cosmic rays

Galactic cosmic rays are generally believed to be bare nuclei, fully stripped of all orbital electrons during passage through several g/sq cm of interstellar matter. Over the years there have been numerous reports of so-called 'below-cutoff' Fe-group cosmic rays, with momentum below the minimum required for a fully ionized particle to reach Earth's inner magnetosphere. These reports comprised just a few tens of ions, but stimulated much speculation about partially ionized Galactic cosmic rays, suggesting a nearby source of Galactic cosmic rays. In this Letter we compare these previous reports to new observations of below-cutoff Fe-group ions from passive track detectors exposed for nearly 6 years in low-Earth orbit aboard NASA's Long Duration Exposure Facility (LDEF) satellite. The LDEF fluxes are only a few percent of the previously reported fluxes. This comparison suggests that the flux of below-cutoff ions must be out of phase with the observed solar-cycle variation of other (nonsolar) cosmic-ray sources and/or must strongly increase with decreasing altitude in low-Earth orbit. Both of these features are inconsistent with the notion of partially ionized Galactic cosmic rays as the source of the observed below-cutoff ions.

Tylka, A. J.↗
NASA NTRS2016

MAVEN Observations of Partially Developed Kelvin-Helmholtz Vortices at Mars.

We present preliminary results and interpretations for Mars Atmospheric and Volatile EvolutioN,(MAVEN) observations of magnetosheath-ionospheric boundary oscillations at Mars. Using centrifugal force arguments, we first predict that a signature of fully rolled up Kelvin-Helmholtz vortices at Mars is sheath ions that have a bulk motion toward the Sun. The sheath ions adjacent to a vortex should also accelerate to speeds higher than the mean sheath velocity. We also predict that while the ionospheric ions that are in the vortex accelerate antisunward, they never attain speeds exceeding that of the sheath ions, in stark contrast to KH vortices that arise at the Earths magnetopause. We observe accelerated sheath and ionospheric ions, but we do not observe sheath ions that have a bulk motion toward the Sun. Thus, we interpret these observations as KH vortices that have not fully rolled up.

Kelvin-Helmholtz↗
DOE OSTI2023

Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems

We consider a high-dimensional nonlinear computational model of a dynamical system, parameterized by a vector-valued control parameter, in the presence of uncertainties represented by an uncontrolled parameter modeled by a vector-valued random variable, and possibly with stochastic excitation. The objective is to construct a statistical surrogate model where the input is any deterministic value of the control parameter, and the output is a vector-valued observation of the computational model, which is a random vector whose probability measure is updated using a target dataset. To construct this statistical surrogate model, the stochastic response of the computational model must be built, which is a vector-valued time-discretized stochastic process in high dimension, depending on the control parameter. It is assumed that the computational cost of a single evaluation of the deterministic model is high. For the probabilistic updating, we consider a subset of the components of the observation of the computational model, defined as the “identification observation” of the computational model, for which a small target dataset is available. Therefore, the target dataset is associated with partial observability, corresponding to an incomplete data case. Given a prior probability model of the random control and uncontrolled parameters, a training dataset is constructed, consisting of realizations of the random triplet composed of the stochastic response, the random identification observation, and the random control parameter. Since the computational cost of a single evaluation of the deterministic model is assumed to be large, the training dataset is also of small size. The main challenges in this problem are the high dimensionality, partial observability leading to incomplete data in the target dataset for the identification observation of the computational model (which is not sufficient to identify the computational stochastic responses), and the availability of a small training dataset. To address these challenges, we propose a methodology based on statistical methods for constructing necessary reduced representations, direct probabilistic learning under constraints using probabilistic learning on manifolds (PLoM) constrained by the target dataset, and the use of a weak formulation of the Fourier transform of probability measures. Statistical conditioning is also employed to explore the learned dataset. The constructed predictive statistical surrogate model can be implemented in the context of online computation. Here, we apply this approach to a problem of nonlinear stochastic dynamics in high dimensions within the framework of deformable solids mechanics.

Engineering↗
DOE OSTI2024

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗
DOE OSTI2020

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, due to for example cyber attacks, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Early Inference of Nuclear Technology-Directed Research Activities of Authors from Scientific Publications

Nuclear research articles can provide information about early nuclear proliferation indicators such as influential research entities and technology capability levels of a country, but detection of nuclear activities typically occurs after they have started. We investigate the extent to which nuclear research articles can be used to infer whether a research entity will acquire or develop a nuclear technology before it happens. Early detection of nuclear proliferation or technology development indicators from data is challenging due to partial observability, sparse and unlabeled information, and confounding signals from multiple concurrent activities. This paper presents the early detection problem as a sequential decision-making, goal inference problem, where the objective is to characterize and predict an individual’s, organization’s, or a country’s intent (unobserved goal-directed behavior) towards developing a nuclear capability from partially observed sequences of their research publications, using inverse reinforcement learning and Bayesian goal inference methods. A computational framework is presented, and its application demonstrated using 29,196 Scopus records for a case study related to a civil nuclear capability. The case study results serve as a proof-of-concept demonstration for inference of technology-directed research activity of authors who publish in the nuclear domain. The inference method, combined with advanced computing, may be used to assess and monitor activities pertaining to early developmental stages of a nuclear technology or capability, which in turn can help to identify and prioritize activities with nuclear proliferation potential for further investigation.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1972

Metamorphism of Apollo 14 breccias.

All Apollo 14 breccias for which thin sections are available (27 rocks) were studied petrographically. From one sample in each of six groups, electron microprobe analyses were performed on matrix (less than 25 microns) plagioclase, pyroxene, and olivine and of rims and cores of plagioclase and pyroxene clasts (50 to 300 microns). Matrix plagioclase, pyroxene, and olivine in the high-grade rocks display a narrow range of compositions, suggesting an equilibrated or metamorphic origin. However, in the low-grade rocks a wide range of compositions, suggesting a detrital or unequilibrated origin, is observed. Partially equilibrated states are observed in medium-grade samples. It is concluded that the Apollo 14 breccias formed as part of the Imbrium Basin ejecta blanket - the Fra Mauro formation. The continuous nature of the metamorphic series suggests that the medium- and high-grade breccias were derived from unmetamorphosed equivalents by autometamorphism in the thick, hot Fra Mauro formation at the Apollo 14 site.

Warner, J. L.↗
NASA NTRS1991

Implications of the observations of partially ionised states in the low energy galactic cosmic rays

Of the low energy (30-100 MeV/N)Fe-group nuclei of Galactic cosmic rays (GCRs) a fraction of about 20 percent of these are in partially ionized states. The origin of these particles are not known at present. From the considerations of relevant cross-sections, the following two important processes of the phenomena are given: (1) these GCR Fe-group nuclei must have captured the orbital electrons at energies of 1-5 MeV/N, while traversing matter; and (2) that these GCR ions of 1-5 MeV/N are then reaccelerated to energies of at least 30-100 MeV/N, most probably in the heliospheric boundary region. Three possible scenarios of the electron-capture processes are briefly discussed: (1) electron capture in a large molecular cloud; (2) GCR equilibrium spectra in ISM and their equilibrium charge; and (3) electron capture in the Oort's cloud.

Mitra, Banashree↗
NASA NTRS2003

Gravity Effects Observed In Partially Premixed Flames

Partially premixed flames (PPFs) contain a rich premixed fuel air mixture in a pocket or stream, and, for complete combustion to occur, they require the transport of oxidizer from an appropriately oxidizer-rich (or fuel-lean) mixture that is present in another pocket or stream. Partial oxidation reactions occur in fuel-rich portions of the mixture and any remaining unburned fuel and/or intermediate species are consumed in the oxidizer-rich portions. Partial premixing, therefore, represents that condition when the equivalence ratio (phi) in one portion of the flowfield is greater than unity, and in another section its value is less than unity. In general, for combustion to occur efficiently, the global equivalence ratio is in the range fuel-lean to stoichiometric. These flames can be established by design by placing a fuel-rich mixture in contact with a fuel-lean mixture, but they also occur otherwise in many practical systems, which include nonpremixed lifted flames, turbulent nonpremixed combustion, spray flames, and unwanted fires. Other practical applications of PPFs are reported elsewhere. Although extensive experimental studies have been conducted on premixed and nonpremixed flames under microgravity, there is a absence of previous experimental work on burner stabilized PPFs in this regard. Previous numerical studies by our group employing a detailed numerical model showed gravity effects to be significant on the PPF structure. We report on the results of microgravity experiments conducted on two-dimensional (established on a Wolfhard-Parker slot burner) and axisymmetric flames (on a coannular burner) that were investigated in a self-contained multipurpose rig. Thermocouple and radiometer data were also used to characterize the thermal transport in the flame.

Puri, Ishwar K.↗
NASA NTRS2013

Carbon Monoxide in the Cold Debris of Supernova 1987A

We report spectroscopic and imaging observations of rotational transitions of cold CO and SiO in the ejecta of SN1987A, the first such emission detected in a supernova remnant. In addition to line luminosities for the CO J = 1-0, 2-1, 6-5, and 7-6 transitions, we present upper limits for all other transitions up to J = 13-12, collectively measured from the Atacama Large Millimeter Array, the Atacama Pathfinder EXperiment, and the Herschel Spectral and Photometric Imaging REceiver. Simple models show the lines are emitted from at least 0.01 M ☉ of CO at a temperature >14 K, confined within at most 35% of a spherical volume expanding at ~2000 km s-1. Moreover, we locate the emission within 1'' of the central debris. These observations, along with a partial observation of SiO, confirm the presence of cold molecular gas within supernova remnants and provide insight into the physical conditions and chemical processes in the ejecta. Furthermore, we demonstrate the powerful new window into supernova ejecta offered by submillimeter observations.

Cold↗
NASA NTRS1989

Identification of motion parameters of a rigid body from its orthogonal and perspective projections

An estimate is made of the motion parameters, namely, linear and angular velocities, of a rigid body rotating and translating in three-space. The authors assume that the velocities are constant and that the motion is not completely observable. They consider two separate cases of partial observations corresponding to the orthogonal and the perspective projections, respectively. If (x, y, z) is the Cartesian coordinate of the three-space, the authors assume in the first case that the projection of the motion on the x-y plane is observed. If (r, theta, phi) is the polar coordinates of the three-space, they assume in the second case that the parameter vector (theta, phi) is observed. The use of both of these cases to estimate the motion parameters is discussed.

Ganguly, S.↗
DOE OSTI2021

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Nonperturbative quantum gravity in a closed Lorentzian universe

We study how meaningful physical predictions can arise in nonperturbative quantum gravity in a closed Lorentzian universe. In such settings, recent developments suggest that the quantum gravitational Hilbert space is one-dimensional and real for each α-sector, as induced by spacetime wormholes. This appears to obstruct the conventional quantum-mechanical prescription of assigning probabilities via projection onto a basis of states. While previous approaches have introduced external observers or augmented the theory to resolve this issue, we argue that quantum gravity itself contains all the necessary ingredients to make physical predictions. We demonstrate that the emergence of classical observables and probabilistic outcomes can be understood as a consequence of partial observability: physical observers access only a subsystem of the universe. Tracing out the inaccessible degrees of freedom yields reduced density matrices that encode classical information, with uncertainties exponentially suppressed by the environment’s entropy. We develop this perspective using both the Lorentzian path integral and operator formalisms and support it with a simple microscopic model. Our results show that quantum gravity in a closed universe naturally gives rise to meaningful, robust predictions without recourse to external constructs.

AdS-CFT Correspondence↗
DOE OSTI2023

Missing data in multi-omics integration: Recent advances through artificial intelligence

Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across ‘omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems

The goal of this paper is to solve a class of stochastic optimal control problems numerically, in which the state process is governed by an Itô type stochastic differential equation with control process entering both in the drift and the diffusion, and is observed partially. The optimal control of feedback form is determined based on the available observational data. In this work, we call this type of control problems the data driven feedback control. The computational framework that we introduce to solve such type of problems aims to find the best estimate for the optimal control as a conditional expectation given the observational information. To make our method feasible in providing timely feedback to the controlled system from data, we develop an efficient stochastic optimization algorithm to implement our computational framework.

97 MATHEMATICS AND COMPUTING↗