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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Central Plasma Sheet Ion Properties as Inferred from Ionospheric Observations

A method of inferring central plasma sheet (CPS) temperature, density, and pressure from ionospheric observations is developed. The advantage of this method over in situ measurements is that the CPS can be studied in its entirely, rather than only in fragments. As a result, for the first time, comprehensive two-dimensional equatorial maps of CPS pressure, density, and temperature within the isotropic plasma sheet are produced. These particle properties are calculated from data taken by the Special Sensor for Precipitating Particles, version 4 (SSJ4) particle instruments onboard DMSP F8, F9, F10, and F11 satellites during the entire year of 1992. Ion spectra occurring in conjunction with electron acceleration events are specifically excluded. Because of the variability of magnetotail stretching, the mapping to the plasma sheet is done using a modified Tsyganenko [1989] magnetic field model (T89) adjusted to agree with the actual magnetotail stretch at observation time. The latter is inferred with a high degree of accuracy (correlation coefficient -0.9) from the latitude of the DMSP b2i boundary (equivalent to the ion isotropy boundary). The results show that temperature, pressure, and density all exhibit dawn-dusk asymmetries unresolved with previous measurements. The ion temperature peaks near the midnight meridian. This peak, which has been associated with bursty bulk flow events, widens in the Y direction with increased activity. The temperature is higher at dusk than at dawn, and this asymmetry increases with decreasing distance from the Earth. In contrast, the density is higher at dawn than at dusk, and there appears to be a density enhancement in the low-latitude boundary layer regions which increases with decreasing magnetic activity. In the near-Earth regions, the pressure is higher at dusk than at dawn, but this asymmetry weakens with increasing distance from the Earth and may even reverse so that at distances X less than approx. 10 to -12 R(sub E), depending on magnetic activity, the dawn sector has slightly higher pressure. The temperature and density asymmetries in the near-Earth region are consistent with the ion westward gradient/curvature drift as the ions ExB convect earthward. When the solar wind dynamic pressure increases, CPS density and pressure appear to increase, but the temperature remains relatively constant. Comparison with previously published work indicates good agreement between the inferred pressure, temperature, and density and those obtained from in situ data. This new method should provide a continuous mechanism to monitor the pressure, temperature, and density in the magnetotail with unprecedented comprehensiveness.

Wing, Simon↗
DOE OSTI2023

Deducing the EOS of dense neutron star matter with machine learning

Abstract The interior of a neutron star is a unique astrophysical laboratory for studying matter at extreme densities and pressures beyond what is replicable in terrestrial experiments. While there is no direct way to simulate the interior of these stars, one promising avenue to learning more about the equation of state (EOS) of such matter is through X‐rays emitted from the star's surface. The current state‐of‐the‐art method for inference of EOS from a star's X‐ray spectra uses piece‐wise, simulation‐based likelihoods that rely on theoretical assumptions complicated by systematic uncertainties. To reduce the dimensionality of the problem, this method infers macroscopic properties of the star (mass and radius) from emitted X‐ray spectra, and from those quantities infers the EOS. This work approaches the same problem using machine learning techniques, demonstrating a series of enhancements to the current state‐of‐the‐art by realistic uncertainty quantification and reducing the need for theoretical assumptions. We also demonstrate novel inference of the EOS directly from high‐dimensional simulated X‐ray spectra from neutron stars that negate the need for a piece‐wise approach. This inference allows for a natural propagation of uncertainties from the X‐ray spectra by conditioning the discussed networks on realistic sources of uncertainty for each star.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2005

Rheology of the Ronne Ice Shelf, Antarctica, Inferred from Satellite Radar Interferometry Data using an Inverse Control Method

The Antarctic Ice Sheet is surrounded by large floating ice shelves that spread under their own weight into the ocean. Ice shelf rigidity depends on ice temperature and fabrics, and is influenced by ice flow and the delicate balance between bottom and surface accumulation. Here, we use an inverse control method to infer the rigidity of the Ronne Ice Shelf that best matches observations of ice velocity from satellite radar interferometry. Ice rigidity, or flow law parameter B, is shown to vary between 300 and 900 kPa a(sup 1/3). Ice is softer along the side margins due to frictional heating, and harder along the outflow of large glaciers, which advect cold continental ice. Melting at the bottom surface of the ice shelf increases its rigidity, while freezing decreases it. Accurate numerical modelling of ice shelf flow must account for this spatial variability in mechanical characteristics.

ice velocity↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1974

On the use of Godhavn H-component as an indicator of the interplanetary sector polarity

An objective method of inferring the polarity of the interplanetary magnetic field using the H-component at Godhavn is presented. The objectively inferred polarities are compared with a subjective index inferred earlier. It is concluded that no significant difference exists between the two methods. The inferred polarities derived from Godhavn H is biased by the (slp) sub q signature in the sense that during summer prolonged intervals of geomagnetic calm will result in inferred Away polarity regardless of the actual sector polarity. This bias does not significantly alter the large scale structure of the inferred sector structure.

Svalgaard, L.↗
DOE OSTI2026

NN-OpInf

SAND2026-18878O The NN-OpInf tool is a PyTorch-based approach to operator inference that uses composable, structure-preserving neural networks to represent nonlinear operators. Operator inference is a machine learning method for inferring low-dimensional systems from data and polynomial models for system dynamics. However, many systems do not conform to polynomial structures, which NN-OpInf addresses by parameterizing operators with neural networks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
NASA NTRS1975

On the use of Godhavn H component as an indicator of the interplanetary sector polarity

An objective method of inferring the polarity of the interplanetary magnetic field using the H component at Godhavn is presented. The objectively inferred polarities are compared with a subjective index inferred earlier (Svalgaard, 1972b). It is concluded that no significant difference exists between the two methods. The inferred polarities derived from Godhavn H are biased by the Sq-p signature in the sense that during summer, prolonged intervals of geomagnetic calm will result in inferred away polarity regardless of the actual sector polarity. This bias does not significantly alter the large-scale structure of the inferred sector structure.

Svalgaard, L.↗
DOE OSTI2019

Use and abuse of correlation analyses in microbial ecology

Abstract Correlation analyses are often included in bioinformatic pipelines as methods for inferring taxon–taxon interactions. In this perspective, we highlight the pitfalls of inferring interactions from covariance and suggest methods, study design considerations, and additional data types for improving high-throughput interaction inferences. We conclude that correlation, even when augmented by other data types, almost never provides reliable information on direct biotic interactions in real-world ecosystems. These bioinformatically inferred associations are useful for reducing the number of potential hypotheses that we might test, but will never preclude the necessity for experimental validation.

Carr, Alex↗
DOE OSTI2018

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs and varying fidelities. Typically, a computationally expensive high-fidelity model describes the system with the accuracy required by the current application at hand, while lower-fidelity models are less accurate but computationally cheaper than the high-fidelity model. Outer-loop applications, such as optimization, inference, and uncertainty quantification, require multiple model evaluations at many different inputs, which often leads to computational demands that exceed available resources if only the high-fidelity model is used. This work surveys multifidelity methods that accelerate the solution of outer-loop applications by combining high-fidelity and low-fidelity model evaluations, where the low-fidelity evaluations arise from an explicit low-fidelity model (e.g., a simplified physics approximation, a reduced model, a data-fit surrogate) that approximates the same output quantity as the high-fidelity model. The overall premise of these multifidelity methods is that low-fidelity models are leveraged for speedup while the high-fidelity model is kept in the loop to establish accuracy and/or convergence guarantees. We categorize multifidelity methods according to three classes of strategies: adaptation, fusion, and filtering. The paper reviews multifidelity methods in the outer-loop contexts of uncertainty propagation, inference, and optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Detectability of Varied Hybridization Scenarios Using Genome-Scale Hybrid Detection Methods

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This phenomenon has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, as well as summary methods that predict individual hybridization events from a subset of taxa. However, a lack of empirical comparisons between methods – especially those pertaining to large networks with varied hybridization scenarios – hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson’s D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and HyDe, Patterson’s D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events between sets of three taxa. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow (γ). We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher relative false negative rates across all methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving extinct or unsampled ghost lineages, HyDe is the only method able to identify the direction of hybridization, distinguishing the source parental lineages from recipient hybrid lineages. Lastly, we test the methods on a dataset of ultraconserved elements from the bee subfamily Nomiinae, finding possible hybridization events between clades which correspond to regions of poor support in the species tree estimated in a previous study.

Bjorner, Marianne B.↗
NASA NTRS1987

The mechanisms of temporal inference

The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.

Fox, B. R.↗
DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗