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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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DOE OSTI2014

The SAGES Legacy Unifying Globulars and Galaxies survey (SLUGGS): sample definition, methods, and initial results

We introduce and provide the scientific motivation for a wide-field photometric and spectroscopic chemodynamical survey of nearby early-type galaxies (ETGs) and their globular cluster (GC) systems. The SAGES Legacy Unifying Globulars and GalaxieS (SLUGGS) survey is being carried out primarily with Subaru/Suprime-Cam and Keck/DEIMOS. The former provides deep gri imaging over a 900 arcmin{sup 2} field-of-view to characterize GC and host galaxy colors and spatial distributions, and to identify spectroscopic targets. The NIR Ca II triplet provides GC line-of-sight velocities and metallicities out to typically ∼8 R {sub e}, and to ∼15 R {sub e} in some cases. New techniques to extract integrated stellar kinematics and metallicities to large radii (∼2-3 R {sub e}) are used in concert with GC data to create two-dimensional (2D) velocity and metallicity maps for comparison with simulations of galaxy formation. The advantages of SLUGGS compared with other, complementary, 2D-chemodynamical surveys are its superior velocity resolution, radial extent, and multiple halo tracers. We describe the sample of 25 nearby ETGs, the selection criteria for galaxies and GCs, the observing strategies, the data reduction techniques, and modeling methods. The survey observations are nearly complete and more than 30 papers have so far been published using SLUGGS data. Here we summarize some initial results, including signatures of two-phase galaxy assembly, evidence for GC metallicity bimodality, and a novel framework for the formation of extended star clusters and ultracompact dwarfs. An integrated overview of current chemodynamical constraints on GC systems points to separate, in situ formation modes at high redshifts for metal-poor and metal-rich GCs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Data reduction considerations for the burning velocity of spherical constant volume flames of R32 (CH 2 F 2 ) with air

Here, the present work explores data reduction techniques for the measurement of the laminar burning velocities of R32(CH 2 F 2 )-air mixtures using a constant volume combustion device, in which the pressure-time history is the only measured parameter. To allow clear assessment of the accuracy of the data reduction methods, the pressure-time histories used for analysis are synthetically generated via a detailed numerical simulation employing full kinetics and with and without an optically-thin radiation model. Various data reduction models are employed, including a two-zone model and two multi-zone models, and these are compared with the results from the burning velocity obtained from the output of the numerical simulation. The data reduction schemes are shown to be accurate if the same radiation model is employed in the data reduction as was used in the flame simulation to generate the pressure trace used for post-processing. If the incorrect radiation model is employed, however, the errors can be quite large. The effects of stretch, radiation, and different data post-processing methodologies are explored and the errors quantified. Stretch is shown to be important for the early stages and the selected data range that is used for extrapolation has a significant effect on the extrapolated burning velocity. However, with an appropriate choice of data considered for extrapolation, the prediction of the unstretched burning velocity can be quite accurate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Magnesium isotopes of the bulk solar wind from Genesis diamond-like carbon films

–NASA’s Genesis Mission returned solar wind (SW) to the Earth for analysis to derive the composition of the solar photosphere from solar material. SW analyses control the precision of the derived solar compositions, but their ultimate accuracy is limited by the theoretical or empirical models of fractionation due to SW formation. Mg isotopes are “ground truth” for these models since, except for CAIs, planetary materials have a uniform Mg isotopic composition (within ≤1&) so any significant isotopic fractionation of SW Mg is primarily that of SW formation and subsequent acceleration through the corona. This study analyzed Mg isotopes in a bulk SW diamond-like carbon (DLC) film on silicon collector returned by the Genesis Mission. A novel data reduction technique was required to account for variable ion yield and instrumental mass fractionation (IMF) in the DLC. The resulting SW Mg fractionation relative to the DSM-3 laboratory standard was ( 14.4&, 30.2&) (4.1&, 5.5&), where the uncertainty is 2σ SE of the data combined with a 2.5& (total) error in the IMF determination. Two of the SW fractionation models considered generally agreed with our data. Their possible ramifications are discussed for O isotopes based on the CAI nebular composition of McKeegan et al. (2011).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The public health exposome and pregnancy-related mortality in the United States: a high-dimensional computational analysis

Racial inequities in maternal mortality in the U.S. continue to be stark. The 2015–2018, 4-year total population, county-level, pregnancy-related mortality ratio (PRM; deaths per 100,000 live births; National Center for Health Statistics (NCHS), restricted use mortality file) was linked with the Public Health Exposome (PHE). Using data reduction techniques, 1591 variables were extracted from over 62,000 variables for use in this analysis, providing information on the relationships between PRM and the social, health and health care, natural, and built environments. Graph theoretical algorithms and Bayesian analysis were applied to PHE/PRM linked data to identify latent networks. PHE variables most strongly correlated with total population PRM were years of potential life lost and overall life expectancy. Population-level indicators of PRM were overall poverty, smoking, lack of exercise, heat, and lack of adequate access to food. In this high-dimensional analysis, overall life expectancy, poverty indicators, and health behaviors were found to be the strongest predictors of pregnancy-related mortality. This provides strong evidence that maternal death is part of a broader constellation of both similar and unique health behaviors, social determinants and environmental exposures as other causes of death.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm

Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency and subjective human factors. We developed a novel data-driven geological feature characterization approach based on pre-stack seismic measurements. Our characterization method employs an efficient and accurate machine learning method to extract useful subsurface geologic features automatically. Specifically, we use kernel ridge regression to account for the nonlinear relationship between seismic data and geological features. We moreover employ kernel tricks to avoid the explicit nonlinear mapping and infinite dimension of feature space. Yet, the conventional kernel ridge regression can be computationally prohibitive because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nyström method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate characterization. We provide thorough computational cost analysis to show the efficiency of our new geological feature characterization methods. We validate the performance of our method in characterizing geologic fault zones because faults play an important role in various subsurface applications. Our numerical examples demonstrate that our new characterization method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we demonstrate that our approach yields a speedup ratio on the order of ~102 to ~103 in a multicore computational environment.

58 GEOSCIENCES↗
DOE OSTI2022

Neural-network learning of SPOD latent dynamics

Here, we aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary systems using model order reduction via the spectral proper orthogonal decomposition (SPOD). The proposed method is based on three fundamental steps: in the first, once that the mean flow field has been subtracted from the realizations (also referred to as snapshots), we compress the data from a high-dimensional representation to a lower dimensional one by constructing the SPOD latent space; in the second, we build the time-dependent coefficients by projecting the snapshots containing the fluctuations onto the SPOD basis and we learn their evolution in time with the aid of recurrent neural networks; in the third, we reconstruct the high-dimensional data from the learnt lower -dimensional representation. The proposed method is demonstrated on two different test cases, namely, a compressible jet flow, and a geophysical problem known as the Madden-Julian Oscillation. An extensive comparison between SPOD and the equivalent POD-based counterpart is provided and differences between the two approaches are highlighted. The numerical results suggest that the proposed model is able to provide low rank predictions of complex statistically stationary data and to provide insights into the evolution of phenomena characterized by specific range of frequencies. The comparison between POD and SPOD surrogate strategies highlights the need for further work on the characterization of the interplay of error between data reduction techniques and neural network forecasts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Simulations of Heat Transfer Using Tight-Fitting Twisted Tape Inserts for First Wall Cooling in Molten Salt Breeder Blankets

One of the major components in fusion energy systems is the fusion blanket, which has a vacuum vessel to contain the plasma. As part of the fusion blanket/vacuum vessel, the first wall and plasma-facing components require sufficient cooling to prevent material degradation during operation from the superheated plasma. Most fusion blanket concepts involve first wall and divertor coolant channels with heat transfer enhancements (HTEs) that are intended to withstand the incident high heat fluxes of 1 to 5 MW/m 2 . Twisted tape inserts are a proposed HTE that have been investigated previously for first wall cooling and monoblock divertor cooling channels and in other nonfusion heat transfer components. By inserting twisted tapes into straight pipes, the amount of turbulence in the system can be increased at lower Reynolds numbers by swirling the flow. This results in better heat transfer characteristics with marginal increases in frictional pressure losses. In particular, simulations of high-Prandtl-number fluids such as the proposed molten salt FLiBe in twisted tapes, which is prototypic to liquid immersion blankets, have not been previously explored. Here, in this study, we simulate various Prandtl numbers in pipes with twisted tape inserts using large eddy simulations to determine the effects of increasing Prandtl numbers on heat transfer performance. The quantities of particular interest are the Nusselt number and the friction factor, which were recovered using data reduction techniques to determine impacts on heat transfer and pressure losses. This work serves as a starting point for determining the feasibility of twisted tape inserts for liquid immersion blanket concepts.

LES↗
DOE OSTI2013

SIGGMA: A SURVEY OF IONIZED GAS IN THE GALAXY, MADE WITH THE ARECIBO TELESCOPE

A Survey of Ionized Gas in the Galaxy, made with the Arecibo telescope (SIGGMA), uses the Arecibo L-band Feed Array (ALFA) to fully sample the Galactic plane (30 Degree-Sign {<=} l {<=} 75 Degree-Sign and -2 Degree-Sign {<=} b {<=} 2 Degree-Sign ; 175 Degree-Sign {<=} l {<=} 207 Degree-Sign and -2 Degree-Sign {<=} b {<=} 1 Degree-Sign ) observable with the telescope in radio recombination lines (RRLs). Processed data sets are being produced in the form of data cubes of 2 Degree-Sign (along l) Multiplication-Sign 4 Degree-Sign (along b) Multiplication-Sign 151 (number of channels), archived and made public. The 151 channels cover a velocity range of 600 km s{sup -1} and the velocity resolution of the survey changes from 4.2 km s{sup -1} to 5.1 km s{sup -1} from the lowest frequency channel to the highest frequency channel. RRL maps with 3.'4 resolution and a line flux density sensitivity of {approx}0.5 mJy will enable us to identify new H II regions, measure their electron temperatures, study the physics of photodissociation regions with carbon RRLs, and investigate the origin of the extended low-density medium. Twelve Hn{alpha} lines fall within the 300 MHz bandpass of ALFA; they are resampled to a common velocity resolution to improve the signal-to-noise ratio (S/N) by a factor of three or more and preserve the line width. SIGGMA will produce the most sensitive fully sampled RRL survey to date. Here, we discuss the observing and data reduction techniques in detail. A test observation toward the H II region complex S255/S257 has detected Hn{alpha} and Cn{alpha} lines with S/N > 10.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Case study of a magnetic transient in NOAA 11429 observed by SDO/HMI during the M7.9 flare on 2012 march 13

NOAA 11429 was the source of an M7.9 X-ray flare at the western solar limb (N18° W63°) on 2012 March 13 at 17:12 UT. Observations of the line-of-sight magnetic flux and the Stokes I and V profiles from which it is derived were carried out by the Solar Dynamics Observatory Helioseismic and Magnetic Imager (SDO/HMI) with a 45 s cadence over the full disk, at a spatial sampling of 0.''5. During flare onset, a transient patch of negative flux can be observed in SDO/HMI magnetograms to rapidly appear within the positive polarity penumbra of NOAA 11429. We present here a detailed study of this magnetic transient and offer interpretations as to whether this highly debated phenomenon represents a 'real' change in the structure of the magnetic field at the site of the flare, or is instead a product of instrumental/algorithmic artifacts related to particular SDO/HMI data reduction techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Survey of Antihomology Research

Homology has been gaining traction as a tool for studying patterns in real-world datasets. During the past year, I have sought to understand a slight variant on the typical computation of homology of a metric space which we term antihomology . This exposition will survey that work. In section 2, I begin with findings from computations on samples from common spaces (the circle, the disc, and the square). Section 3 presents one conjecture and one lemma regarding 1-dimensional cycles on the circle with evenly space points and the circle with random sampling. In section 4, I applied antihomology to power grid datasets and compared results with more traditional data reduction techniques. Lastly, section 5 reveals the connection between antihomology and location covering problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Extracting LANSCE Macrobunch Charge from WNR Fast Pickoff [Slides]

Over several days around December 18, 2023, high-charge minipulses and macropulses were sent to Target 2, also known as the Blue Room. This report presents the calibration of a stripline-type current monitor known as the Fast Pickoff, and subsequent charge data for many of these shots. This effort utilized a fast oscilloscope, a nearby Bergoz current monitor, and some data-reduction techniques.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

NULLING DATA REDUCTION AND ON-SKY PERFORMANCE OF THE LARGE BINOCULAR TELESCOPE INTERFEROMETER

The Large Binocular Telescope Interferometer (LBTI) is a versatile instrument designed for high angular resolution and high-contrast infrared imaging (1.5–13 μ m). In this paper, we focus on the mid-infrared (8–13 μ m) nulling mode and present its theory of operation, data reduction, and on-sky performance as of the end of the commissioning phase in 2015 March. With an interferometric baseline of 14.4 m, the LBTI nuller is specifically tuned to resolve the habitable zone of nearby main-sequence stars, where warm exozodiacal dust emission peaks. Measuring the exozodi luminosity function of nearby main-sequence stars is a key milestone to prepare for future exo-Earth direct imaging instruments. Thanks to recent progress in wavefront control and phase stabilization, as well as in data reduction techniques, the LBTI demonstrated in 2015 February a calibrated null accuracy of 0.05% over a 3 hr long observing sequence on the bright nearby A3V star β Leo. This is equivalent to an exozodiacal disk density of 15–30 zodi for a Sun-like star located at 10 pc, depending on the adopted disk model. This result sets a new record for high-contrast mid-infrared interferometric imaging and opens a new window on the study of planetary systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Multilevel Techniques for Compression and Reduction of Scientific Data---The Multivariate Case

We develop a technique for multigrid adaptive reduction of data (MGARD). Special attention is given to the case of tensor product grids, where our approach permits the use of nonuniformly spaced grids in each direction, which can prove problematic for many types of data reduction methods. An important feature of our approach is the provision of guaranteed, computable bounds on the loss incurred by the reduction of the data. Many users are leery of lossy algorithms and will only consider using them provided that numerical bounds on the pointwise difference between the original and the reduced datasets are given. Accordingly, we develop techniques for bounding the loss measured in the $L^{\infty}(\Omega)$ norm, and we show that these bounds are realistic in the sense that they do not significantly overestimate the actual loss. Here, the resulting loss indicators are used to guide the adaptive reduction of the data so that the reduced dataset meets a user-prescribed tolerance or memory constraint. Illustrative numerical examples, including the reduction of data arising from the simulation of a nonlinear reaction-diffusion problem, a turbulent channel flow, and a climate simulation, are provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Multilevel techniques for compression and reduction of scientific data—the univariate case

We present a multilevel technique for the compression and reduction of univariate data and give an optimal complexity algorithm for its implementation. A hierarchical scheme offers the flexibility to produce multiple levels of partial decompression of the data so that each user can work with a reduced representation that requires minimal storage whilst achieving the required level of tolerance. The algorithm is applied to the case of turbulence modelling in which the datasets are traditionally not only extremely large but inherently non-smooth and, as such, rather resistant to compression. Here, we decompress the data for a range of relative errors, carry out the usual analysis procedures for turbulent data, and compare the results of the analysis on the reduced datasets to the results that would be obtained on the full dataset. The results obtained demonstrate the promise of multilevel compression techniques for the reduction of data arising from large scale simulations of complex phenomena such as turbulence modelling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multilevel Techniques for Compression and Reduction of Scientific Data---The Unstructured Case

Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reaction-diffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found poor agreement in patient characterization from genomic versus imaging data. Interpretation of radiogenomic associations was found to be dependent on clustering algorithms and sensitive to data reduction techniques.

60 APPLIED LIFE SCIENCES↗