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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 1,045 records · Page 58

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NASA NTRS2008

Multiple-Instance Regression with Structured Data

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

learning↗

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↗

NASA NTRS1996

The Lunar Crust: Global Structure and Signature of Major Basins

New lunar gravity and topography data from the Clementine Mission provide a global Bouguer anomaly map corrected for the gravitational attraction of mare fill in mascon basins. Most of the gravity signal remaining after corrections for the attraction of topography and mare fill can be attributed to variations in depth to the lunar Moho and therefore crustal thickness. The large range of global crustal thickness (approx. 20-120 km) is indicative of major spatial variations in melting of the lunar exterior and/or significant impact-related redistribution. The 6l-km average crustal thickness, constrained by a depth-to-Moho measured during the Apollo 12 and 14 missions, is preferentially distributed toward the farside, accounting for much of the offset in center-of-figure from the center-of-mass. While the average farside thickness is 12 km greater than the nearside, the distribution is nonuniform, with dramatic thinning beneath the farside, South Pole-Aitken basin. With the global crustal thickness map as a constraint, regional inversions of gravity and topography resolve the crustal structure of major mascon basins to half wavelengths of 150 km. In order to yield crustal thickness maps with the maximum horizontal resolution permitted by the data, the downward continuation of the Bouguer gravity is stabilized by a three- dimensional, minimum-slope and curvature algorithm. Both mare and non-mare basins are characterized by a central upwarped moho that is surrounded by rings of thickened crust lying mainly within the basin rims. The inferred relief at this density interface suggests a deep structural component to the surficial features of multiring lunar impact basins. For large (greater than 300 km diameter) basins, moho relief appears uncorrelated with diameter, but is negatively correlated with basin age. In several cases, it appears that the multiring structures were out of isostatic equilibrium prior to mare emplacement, suggesting that the lithosphere was strong enough to maintain their state of stress to the present.

Neumann, Gregory A.↗

DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗

NASA NTRS2007

High-LET Patterns of DSBs in DNA Loops, the HPRT Gene and Phosphorylation Foci

We present new results obtained with our model based on the track structure and chromatin geometry that predicts the DSB spatial and genomic distributions in a cell nucleus with the full genome represented. The model generates stochastic patterns of DSBs in the physical space of the nucleus filled with the realistic configuration of human chromosomes. The model was re-used to find the distribution of DSBs in a physical volume corresponding to a visible phosphorylation focus believed to be associated with a DSB. The data shows whether there must more than one DSB per foci due to finite size of the visible focus, even if a single DSB is radiochemically responsible for the phosphorylation of DNA in its vicinity. The same model can predict patterns of closely located DSBs in a given gene, or in a DNA loop, one of the large-scale chromatin structures. We demonstrated for the example of the HPRT gene, how different sorts of radiation lead to proximity effect in DSB locations, which is important for modeling gene deletions. The spectrum of intron deletions and total gene deletions was simulated for the HPRT gene. The same proximity effect of DSBs in a loop can hinder DSB restitutions, as parts of the loop between DSBs is deleted with a higher likelihood. The distributions of DSBs and deletions of DNA in a loop are presented.

Ponomarev, Artem L.↗

DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Probing cellulose structures with vibrational spectroscopy

This paper reviews principles, data interpretations, and applications of vibrational spectroscopic methods used for analysis of cellulose in the isolated state and in plant cell walls or lignocellulose biomass. The paper begins with reviewing the crystalline structures of crystalline cellulose polymorphs and the principles of three different vibrational spectroscopy methods—infrared (IR), Raman, and sum frequency generation (SFG)—complemented with density functional theory calculations. Then, it discusses the vibrational modes of crystalline celluloses, how the chain orientation in crystalline domain is analyzed in each method, and how the concentration and spatial distribution of crystalline cellulose domains interspersed in amorphous matrices are manifested or analyzed differently in these three methods. Finally, the paper discusses examples of analyzing crystalline cellulose in plant cell walls or lignocellulose biomass with IR, Raman, and SFG including spectroscopic imaging. One review cannot cover all vibrational spectroscopy literatures on cellulose; this review aims at providing tutorial information, using selected literatures and experimental data, needed to interpret nano-, meso-, and micro-scale structures of cellulose in plant cell walls and lignocellulose biomass.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1981

Sampling problems: The small scale structure of precipitation

The quantitative measurement of precipitation characteristics for any area on the surface of the Earth is not an easy task. Precipitation is rather variable in both space and time, and the distribution of surface rainfall data given location typically is substantially skewed. There are a number of precipitation process at work in the atmosphere, and few of them are well understood. The formal theory on sampling and estimating precipitation appears considerably deficient. Little systematic attention is given to nonsampling errors that always arise in utilizing any measurement system. Although the precipitation measurement problem is an old one, it continues to be one that is in need of systematic and careful attention. A brief history of the presently competing measurement technologies should aid us in understanding the problem inherent in this measurement task.

Crane, R. K.↗

NASA NTRS1983

Analytical and Experimental Evaluation of the Heat Transfer Distribution over the Surfaces of Turbine Vanes

Three airfoil data sets were selected for use in evaluating currently available analytical models for predicting airfoil surface heat transfer distributions in a 2-D flow field. Two additional airfoils, representative of highly loaded, low solidity airfoils currently being designed, were selected for cascade testing at simulated engine conditions. Some 2-D analytical methods were examined and a version of the STAN5 boundary layer code was chosen for modification. The final form of the method utilized a time dependent, transonic inviscid cascade code coupled to a modified version of the STAN5 boundary layer code featuring zero order turbulence modeling. The boundary layer code is structured to accommodate a full spectrum of empirical correlations addressing the coupled influences of pressure gradient, airfoil curvature, and free-stream turbulence on airfoil surface heat transfer distribution and boundary layer transitional behavior. Comparison of pedictions made with the model to the data base indicates a significant improvement in predictive capability.

Hylton, L. D.↗

NASA NTRS1991

Aircraft laser derived chlorophyll distribution across the Iceland-Faeroe front

The ocean surface color (water-leaving radiances) and thermal structure across the Iceland-Faeroe Front under both clear and cloudy conditions were measured from a NASA research aircraft on 25 May 1989. The measurements were made along four north-south lines that were 125 km in length and spaced 35 km apart. The color measurements were made with a 14 channel, non-polarized Multispectral Airborne Radiometer System (MARS) while the thermal data were collected by a thermal radiometer and aircraft bathythermographs. The satellite imagery (NOAA AVHRR) sequence show the development of meanders through the frontal region. These aircraft ocean color and thermal data characterize the biological distribution and are closely coupled to the physical processes occurring in the frontal systems. The ratio of several channels of the ocean color data are used to determine the surface chlorophyll. The retrieved data correlate well with laser-induced chlorophyll fluorescence obtained at the same time and historical chlorophyll data. The observed chlorophyll patchiness appearing across the Iceland-Faeroe Front is believed to be directly and indirectly related to primary and secondary circulation processes in ocean frontal systems. High chlorophyll concentrations were observed on the north side of the front and are inferred to be the result from the advection of Icelandic Coastal Water into the region. The sharp chlorophyll decline at and south of the thermal frontal boundary is clearly related to the subsurface thermal structure.

Arnone, Robert A.↗

NASA NTRSDate not supplied

Large-Scale Simulation of a Distributed Sensing Network Supporting Regional Urban Air Mobility Operations

Urban Air Mobility (UAM) is set to transform transportation in densely populated regions like the San Francisco Bay Area. This paper introduces an innovative simulation approach to explore large-scale UAM scenarios, emphasizing the use of distributed sensing to enhance operational efficiency and safety. The Revolutionary Vertical Lift Technology (RVLT) model is employed as the framework for simulating complex interactions among multiple vehicles within urban landscapes. Strategically deployed ground sensor nodes enable distributed sensing, enhancing situational awareness and operational effectiveness. By integrating empirical data and geographical realism, the simulations provide a systematic analysis of the feasibility, efficiency, and safety considerations associated with UAM deployment in urban environments. Factors such as air traffic density and infrastructural requirements are thoroughly examined, offering actionable insights for policymakers and industry stakeholders. This paper aims to refine the structure and scenarios for large-scale simulations based on distributed sensing, thereby contributing to the advancement of UAM operations.

UAM↗

NASA NTRSDate not supplied

Large-Scale Simulation of a Distributed Sensing Network Supporting Regional Urban Air Mobility Operations

Aircraft Mobility↗

DOE OSTI2025

Integrated Framework of Multisource Data Fusion for Outage Location in Looped Distribution Systems

Accurate outage location is essential for expediting post-outage power restoration, minimizing outage duration, and enhancing the resilience of distribution networks. With the advent of advanced metering infrastructure, data-driven outage location methods have significantly advanced beyond traditional approaches that rely on manual inspections. However, existing methods still face critical challenges, like reliance on single-source data, limited ability to handle partially observable systems or difficulties with loop networks. To the best of our knowledge, no single approach has comprehensively addressed all of these challenges at once. To this end, this paper proposes a comprehensive multisource data fusion framework for outage locations via probabilistic graph networks. The framework consists of three key phases. First, a novel method for reconstituting distribution networks with loops is developed, transforming looped networks into multiple radial subnetworks that retain all outage causalities of the original network. Second, Bayesian network (BN) models are established for each subnetwork, integrating multiple data sources and network structures. Finally, a joint Gibbs sampling mechanism, featuring forward and backward information flow, is designed to merge data from separate BN models and maximize the utilization of limited evidence, ensuring accurate outage location identification. In conclusion, the framework was validated on two modified public test systems, and comparative studies confirmed its effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1991

Experimental measurement of structural power flow on an aircraft fuselage

An experimental technique is used to measure structural intensity through an aircraft fuselage with an excitation load applied near one of the wing attachment locations. The fuselage is a relatively large structure, requiring a large number of measurement locations to analyze the whole of the structure. For the measurement of structural intensity, multiple point measurements are necessary at every location of interest. A tradeoff is therefore required between the number of measurement transducers, the mounting of these transducers, and the accuracy of the measurements. Using four transducers mounted on a bakelite platform, structural intensity vectors are measured at locations distributed throughout the fuselage. To minimize the errors associated with using the four transducer technique, the measurement locations are selected to be away from bulkheads and stiffeners. Furthermore, to eliminate phase errors between the four transducer measurements, two sets of data are collected for each position, with the orientation of the platform with the four transducers rotated by 180 degrees and an average taken between the two sets of data. The results of these measurements together with a discussion of the suitability of the approach for measuring structural intensity on a real structure are presented.

Cuschieri, J. M.↗

DOE OSTI2020

How good are polarizable and flexible models for water: Insights from a many-body perspective

We present a systematic analysis of state-of-the-art polarizable and flexible water models from a many-body perspective, with a specific focus on their ability to represent the Born–Oppenheimer potential energy surface of water from the gas to the liquid phase. Using coupled cluster data in the completed basis set limit as a reference, we examine the accuracy of the polarizable models in reproducing individual many-body contributions to interaction energies and harmonic frequencies of water clusters and compare their performance with that of MB-pol, an explicit many-body model that has been shown to correctly predict the properties of water across the entire phase diagram. Based on these comparisons, we use MB-pol as a reference to analyze the ability of the polarizable models to reproduce the energy landscape of liquid water under ambient conditions. We find that, while correctly reproducing the energetics of minimum-energy structures, the polarizable models examined in this study suffer from inadequate representations of many-body effects for distorted configurations. To investigate the role played by geometry-dependent representations of 1-body charge distributions in reproducing coupled cluster data for both interaction and many-body energies, we introduce a simplified version of MB-pol that adopts fixed atomic charges and demonstrate that the new model retains the same accuracy as the original MB-pol model. Based on the analyses presented in this study, we believe that future developments of both polarizable and explicit many-body models should continue in parallel and would benefit from synergistic efforts aimed at integrating the best aspects of the two theoretical/computational frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Bayesian operator inference for data-driven reduced-order modeling

This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by the structure of the governing equations, the task of learning a reduced-order model from data is posed as a Bayesian inverse problem with Gaussian prior and likelihood. The resulting posterior distribution characterizes the operators defining the reduced-order model, hence the predictions subsequently issued by the reduced-order model are endowed with uncertainty. The statistical moments of these predictions are estimated via a Monte Carlo sampling of the posterior distribution. Since the reduced models are fast to solve, this sampling is computationally efficient. Furthermore, the proposed Bayesian framework provides a statistical interpretation of the regularization term that is present in the deterministic operator inference problem, and the empirical Bayes approach of maximum marginal likelihood suggests a selection algorithm for the regularization hyperparameters. The proposed method is demonstrated on two examples: the compressible Euler equations with noise-corrupted observations, and a single-injector combustion process.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A parallel and performance portable implementation of a full-field crystal plasticity model

We have developed a parallel implementation of an Elasto-Viscoplastic Fast Fourier Transform-based (EVPFFT) micromechanical solver to enable computationally efficient crystal plasticity modeling for polycrystalline materials. Our primary focus lies in achieving performance portability, allowing a single EVPFFT implementation to run optimally on various homogeneous architectures, including multi-core Central Processing Units (CPUs), as well as on heterogeneous computer architectures comprising multi-core CPUs and Graphics Processing Units (GPUs) from different vendors. To accomplish this goal, we have leveraged MATAR, a C++ software library that simplifies the creation and utilization of multidimensional dense or sparse matrix and array data structures. These data structures are designed to be portable across diverse architectures through the use of Kokkos, a performance-portable library. Additionally, we have employed the Message Passing Interface (MPI) to efficiently distribute the computational workload among processors. The heFFTe (Highly Efficient FFT for Exascale) library is used to facilitate the performance portability of the fast Fourier transforms (FFTs) computation. The computational performance of EVPFFT is evaluated and presented in terms of parallel scalability and simulation runtime on different high-performance computing (HPC) architectures. As a result, the utility of the developed framework to efficiently simulate the micro-mechanical fields in polycrystalline microstructures in engineering applications is discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Subduction, Underplating, and Return Flow Recorded in the Cycladic Blueschist Unit Exposed on Syros, Greece

Exhumed high-pressure/low-temperature (HP/LT) metamorphic rocks provide insights into deep (~20–70 km) subduction interface dynamics. On Syros Island (Cyclades, Greece), the Cycladic Blueschist Unit preserves blueschist-to-eclogite facies oceanic- and continental-affinity rocks that record the structural and thermal evolution linked to Eocene subduction. Despite decades of research, the metamorphic and deformation history (P-T-D) and timing of subduction and exhumation are matters of ongoing discussion. We suggest that Syros comprises three coherent tectonic slices and that each slice underwent subduction, underplating, and syn-subduction return flow along similar P-T trajectories, but at progressively younger times. Subduction and exhumation are distinguished by lineations and ductile fold axis orientations, and are kinematically consistent with previous studies that document top-to-the-S-SW shear (prograde-to-peak subduction), top-to-the-NE shear (blueschist facies exhumation), and then E-W coaxial stretching (greenschist facies exhumation). Amphibole zonations record cooling during decompression, indicating return flow above a cold slab. Multi-mineral Rb-Sr isochrons and compiled metamorphic geochronology show that the three slices record distinct stages of peak subduction (53–52, ~50, and 45 Ma) that young with structural depth. Retrograde blueschist and greenschist facies fabrics span ~50–40 and ~43–20 Ma, respectively, and also young with structural depth. Synthesized data sets support a revised tectonic framework for Syros, involving subduction of structurally distinct coherent slices and simultaneous return flow of previously accreted tectonic slices in the subduction channel shear zone. Distributed, ductile, dominantly coaxial return flow in an Eocene-Oligocene subduction channel proceeded at rates of ~1.5–5 mm/yr and accommodated ~80% of the total exhumation of this HP/LT complex.

58 GEOSCIENCES↗