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DOE OSTI2018

Fast Learning for Immersive Engagement in Energy Simulations

The fast computation which is critical for immersive engagement with and learning from energy simulations would be furthered by developing a general method for creating rapidly computed simplified versions of NREL's computation-intensive energy simulations. Created using machine learning techniques, these 'reduced form' simulations can provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost with response times - typically less than one minute of wall-clock time - suitable for real-time human-in-the-loop design and analysis. Additionally, uncertainty quantification techniques can document the accuracy of the approximate models and their domain of validity. Approximation methods are applicable to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. These reduced-form representations cannot replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and quality assurance for large sets of simulations. We present an overview of the framework and methods we have implemented for developing these reduced-form representations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Flow field characteristics of a 127-pin rod bundle with hexagonal spacer grids

A determination of nominal flow phenomena in liquid metal fast reactor (LMFR) fuel assemblies is critical toward generation-IV reactor development. Axially positioned spacer grids are used to maintain the geometry of hexagonal rod bundles and simultaneously introduce perturbations in the flow. Three-dimensional (3D) printed asymmetric honeycomb spacer grids were installed in a prototypical 127-pin LMFR fuel assembly model to study complex fluid dynamics interactions induced by the spacer grid and rods. To characterize flow dynamics in this intricate geometry, time-resolved particle image velocimetry (TR-PIV) using the matched-index-of-refraction method was employed to obtain non-intrusive velocity measurements for three axial planes (one near-wall and two interior planes) at a Reynolds number of 6000. The statistical TR-PIV results compared sub-channel-dependent normalized time-averaged velocity, velocity fluctuations, Reynolds stress, vorticity, and turbulence kinetic energy distributions. TR-PIV line profiles characterized downstream spacer grid flow dynamics. Two-point spatial and spatial–temporal cross-correlation fields revealed local coherent structures and quantified convection velocities of traveling vortices. Spatial–temporal decomposition using dynamic mode decomposition (DMD) applied to the near-wall vorticity fields extracted turbulent structures and flow instabilities in the wake region of the spacer grid, along with their decay and frequency rates. Reduced-order velocity fields from DMD reconstructions identified the most energy-containing coherent structures persistent in the near-wall region. This research provides experimental data sets and analyses of flow behavior in rod bundles with hexagonal spacer grids. In conclusion, the results are critical toward LMFR design and geometry optimization, crucial for the validation of computational fluid dynamics and reduced-order flow models.

42 ENGINEERING↗

DOE OSTI2017

STELLAR, GAS, AND DARK MATTER CONTENT OF BARRED GALAXIES

We select a sample of galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7) where galaxies are classified, through visual inspection, as hosting strong bars, weak bars, or as unbarred galaxies, and make use of H i mass and kinematic information from the Arecibo Legacy Fast ALFA survey catalog, to study the stellar, atomic gas, and dark matter content of barred disk galaxies. We find, in agreement with previous studies, that the bar fraction increases with increasing stellar mass. A similar trend is found with total baryonic mass, although the dependence is not as strong as with stellar mass, due to the contribution of gas. The bar fraction shows a decrease with increasing gas mass fraction. This anticorrelation between the likelihood of a galaxy hosting a bar with the gas richness of the galaxy results from the inhibiting effect the gas has in the formation of bars. We also find that for massive galaxies with stellar masses larger than 10{sup 10} M {sub ⊙}, at fixed stellar mass, the bar fraction decreases with increasing global halo mass (i.e., halo mass measured up to a radius of the order of the H i disk extent).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Demographics of isolated galaxies along the Hubble sequence

Isolated galaxies in low-density regions are significant in the sense that they are least affected by the hierarchical pattern of galaxy growth and interactions with perturbers, at least for the last few gigayears. To form a comprehensive picture of the star-formation history of isolated galaxies, we constructed a catalog of isolated galaxies and their comparison sample in relatively denser environments. The galaxies are drawn from the Sloan Digital Sky Survey Data Release 7 in the redshift range of 0.025<z<0.044. We performed a visual inspection and classified their morphology following the Hubble classification scheme. For the spectroscopic study, we make use of the catalog provided by Oh et al. in 2011. We confirm most of the earlier understanding on isolated galaxies. The most remarkable additional results are as follows. Isolated galaxies are dominantly late type with the morphology distribution (E:S0:S:Irr) = (9.9:11.3:77.6:1.2)%. The frequency of elliptical galaxies among isolated galaxies is only a third of that of the comparison sample. Most of the photometric and spectroscopic properties are surprisingly similar between the isolated and comparison samples. However, early-type isolated galaxies are less massive by 50% and younger (by Hβ) by 20% than their counterparts in the comparison sample. This can be explained as a result of different merger and star-formation histories for differing environments in the hierarchical merger paradigm. We provide an online catalog for the list and properties of our sample galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗

DOE OSTI2022

Data-centric framework for crystal structure identification in atomistic simulations using machine learning

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. Further, it is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods—especially at high temperatures when lattices are the most distorted—while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Discovery of a visual T-dwarf triple system and binarity at the L/T transition

We present new high contrast imaging of eight L/T transition brown dwarfs (BDs) using the NIRC2 camera on the Keck II telescope. One of our targets, the T3.5 dwarf 2MASS J08381155+1511155, was resolved into a hierarchal triple with projected separations of 2.5 ± 0.5 AU and 27 ± 5 AU for the BC and A(BC) components, respectively. Resolved OSIRIS spectroscopy of the A(BC) components confirms that all system members are T dwarfs. The system therefore constitutes the first triple T-dwarf system ever reported. Using resolved photometry to model the integrated-light spectrum, we infer spectral types of T3 ± 1, T3 ± 1, and T4.5 ± 1 for the A, B, and C components, respectively. The uniformly brighter primary has a bluer J – K{sub s} color than the next faintest component, which may reflect a sensitive dependence of the L/T transition temperature on gravity, or alternatively divergent cloud properties among components. Relying on empirical trends and evolutionary models we infer a total system mass of 0.034-0.104 M {sub ☉} for the BC components at ages of 0.3-3 Gyr, which would imply a period of 12-21 yr assuming the system semimajor axis to be similar to its projection. We also infer differences in effective temperatures and surface gravities between components of no more than ∼150 K and ∼0.1 dex. Given the similar physical properties of the components, the 2M0838+15 system provides a controlled sample for constraining the relative roles of effective temperature, surface gravity, and dust clouds in the poorly understood L/T transition regime. For an age of 3 Gyr we estimate a binding energy of ∼20 × 10{sup 41} erg for the wide A(BC) pair, which falls above the empirical minimum found for typical BD binaries, and suggests that the system may have been able to survive a dynamical ejection during formation. Combining our imaging survey results with previous work we find an observed binary fraction of 4/18 or 22{sub −8}{sup +10}% for unresolved spectral types of L9-T4 at separations ≳ 0.''1. This translates into a volume-corrected frequency of 13{sub −6}{sup +7}%, which is similar to values of ∼9%-12% reported outside the transition. Our reported L/T transition binary fraction is roughly twice as large as the binary fraction of an equivalent L9-T4 sample selected from primary rather than unresolved spectral types (6{sub −4}{sup +6}%); however, this increase is not yet statistically significant and a larger sample is required to settle the issue.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Geometric Interpretation of the Cluster Location Problem Part I: Theory

We present a new framing of the seismic location problem using principles drawn from differential geometry. Our interpretation relies upon the common assumption that travel times observed across a network are continuous, differentiable functions of source location. In consequence, travel‐time functions constitute a differentiable map between the source region and a Riemannian manifold. The manifold is said to be the image of the source region embedded in a generally high‐dimension travel‐time vector space. A cluster of events in the source region has an image of discrete points on the manifold, that, except in the simplest cases, cannot be viewed directly. However, it is possible to project the image of a cluster into a tangent space of the manifold for direct visualization. The projection operator can be computed directly from the data without a velocity model, but produces a distorted rendering of the cluster geometry. With a model we can predict the distortions and correct them to estimate cluster geometry. We develop these points with the simplest possible example, one for which direct visualization of the manifold is possible, using the example as an introduction to the relevant concepts from differential geometry in a familiar setting. The tangent space, a local linearization of the manifold, plays a key role. We develop a metric to estimate the limits of linearization, that is, to determine when the curvature of the manifold invalidates the linear assumption. We also examine the interplay of model error, inadequate network geometry, and pick error. We then generalize our results from the simple case to the general case of 3D source regions observed by general networks. Although we do suggest a new “project and correct” method for location, we do not develop it into a practical algorithm. In conclusion, our intention rather is to highlight new analytical methods grounded in differential geometry.

East Pacific Ocean Islands↗

DOE OSTI2019

Finding high-redshift strong lenses in DES using convolutional neural networks

We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250 000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g - i < 5, 0.6 < g - r < 3, r_mag > 19, g_mag > 20, and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as 'probably' or 'definitely' lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations, we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.

Jacobs, C.↗

DOE OSTI2022

AGNs in post-mergers from the ultraviolet near infrared optical northern survey

The kinematic disturbances associated with major galaxy mergers are known to produce gas inflows, which in turn may trigger accretion onto the supermassive black holes (SMBH) of the participant galaxies. While this effect has been studied in galaxy pairs, the frequency of active galactic nuclei (AGNs) in fully coalesced post-merger systems is poorly constrained due to the limited size or impurity of extant post-merger samples. Previously, we combined convolutional neural network (CNN) predictions with visual classifications to identify a highly pure sample of 699 post-mergers in deep r-band imaging. In the work presented here, we quantify the frequency of AGNs in this sample using three metrics: optical emission lines, mid-infrared (mid-IR) colour, and radio detection of low-excitation radio galaxies (LERGs). We also compare the frequency of AGNs in post-mergers to that in a sample of spectroscopically identified galaxy pairs. We find that AGNs identified by narrow-line optical emission and mid-IR colour have an increased incidence rate in post-mergers, with excesses of ~4 over mass- and redshift-matched controls. The optical and mid-IR AGN excesses in post-mergers exceed the values found for galaxy pairs, indicating that AGN activity in mergers peaks after coalescence. Conversely, we recover no significant excess of LERGs in post-mergers or pairs. Finally, we find that the [O III ] luminosity (a proxy for SMBH accretion rate) in post-mergers that host an optical AGN is ~0.3 dex higher on average than in non-interacting galaxies with an optical AGN, suggesting that mergers generate higher accretion rates than secular triggering mechanisms.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

A Bayesian approach to strong lens finding in the era of wide-area surveys

ABSTRACT The arrival of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide-area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from $\mathcal {O}(10^3)$ to $\mathcal {O}(10^5)$. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong lens experts to remove false positives which is only set to increase with these surveys. In this work, we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science, and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false-positive rate of 10−3 can be achieved with a completeness of 46 per cent, compared to 34 per cent for the best individual classifier. Given the large number of galaxy–galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses and to help prioritize candidates for follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: Automated measurement development for full field digital mammography

Purpose: The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.Methods: A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR{sub pg} measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR{sub vc} and BR{sub vr} measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.Results: The three BI-RADS measures generated by method-1 had κ between 0.25–0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR{sub pg}; (b) OR = 1.93 (1.36, 2.74) for BR{sub vc}; and (c) OR = 1.37 (1.05, 1.80) for BR{sub vr}. The measures generated by method-2 had κ between 0.42–0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR{sub pg}; (b) OR = 1.42 (0.87, 2.32) for BR{sub vc}; and (c) OR = 2.13 (1.22, 3.72) for BR{sub vr}. The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.Conclusions: A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

A priori and a posteriori investigations for developing large eddy simulations of multi-species turbulent mixing under high-pressure conditions

A Direct Numerical Simulation (DNS) database was created representing mixing of species under high-pressure conditions. The configuration considered is that of a temporally evolving mixing layer. The database was examined and analyzed for the purpose of modeling some of the unclosed terms that appear in the Large Eddy Simulation (LES) equations. Several metrics are used to understand the LES modeling requirements. First, a statistical analysis of the DNS-database large-scale flow structures was performed to provide a metric for probing the accuracy of the proposed LES models as the flow fields obtained from accurate LESs should contain structures of morphology statistically similar to those observed in the filtered-and-coarsened DNS (FC-DNS) fields. To characterize the morphology of the large-scales structures, the Minkowski functionals of the iso-surfaces were evaluated for two different fields: the second-invariant of the rate of deformation tensor and the irreversible entropy production rate. To remove the presence of the small flow scales, both of these fields were computed using the FC-DNS solutions. It was found that the large-scale structures of the irreversible entropy production rate exhibit higher morphological complexity than those of the second invariant of the rate of deformation tensor, indicating that the burden of modeling will be on recovering the thermodynamic fields. Second, to evaluate the physical effects which must be modeled at the subfilter scale, an a priori analysis was conducted. This a priori analysis, conducted in the coarse-grid LES regime, revealed that standard closures for the filtered pressure, the filtered heat flux, and the filtered species mass fluxes, in which a filtered function of a variable is equal to the function of the filtered variable, may no longer be valid for the high-pressure flows considered in this study. The terms requiring modeling are the filtered pressure, the filtered heat flux, the filtered pressure work, and the filtered species mass fluxes. Improved models were developed based on a scale-similarity approach and were found to perform considerably better than the classical ones. These improved models were also assessed in an a posteriori study. Different combinations of the standard models and the improved ones were tested. At the relatively small Reynolds numbers achievable in DNS and at the relatively small filter widths used here, the standard models for the filtered pressure, the filtered heat flux, and the filtered species fluxes were found to yield accurate results for the morphology of the large-scale structures present in the flow. Analysis of the temporal evolution of several volume-averaged quantities representative of the mixing layer growth, and of the cross-stream variation of homogeneous-plane averages and second-order correlations, as well as of visualizations, indicated that the models performed equivalently for the conditions of the simulations. The expectation is that at the much larger Reynolds numbers and much larger filter widths used in practical applications, the improved models will have much more accurate performance than the standard one.

42 ENGINEERING↗

DOE OSTI2019

Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response

Disaster response preparedness, risk mitigation, and effective post-event response are critical for national security, public health, and economic integrity. For areas impacted by extreme events, timely and accurate impact assessments of lifeline infrastructures are critical. Traditional disaster management and situational awareness platforms are not capable of handling the massive influx of available information from traditional and non-traditional sources. Next-generation analytic assessment approaches, where use of dynamically sourced, heterogeneous data with near real-time availability are integrated into a continually-updated analytics framework. The objective of this research was to develop a foundational multi-formalism modeling platform to integrate novel data sources for use in improving situational awareness after a disaster event. Specifically, to 1) provide an architecture for machine-learning based data fusion various multi-sensor datasets; 2) classify, geolocate, and temporally bin novel data sources for use in spatially-enabled machine-learning based data fusion; and 3) derive confidence-based observation data with machine-learning based prediction to fill spatial and temporal observation gaps. This project developed a reusable cloud-based pipeline architecture for ingesting, storing, and analyzing relevant data, executing a range of deep-learning models, and visualize the information geographically. The pipeline development was informed by previous PNNL investments (StreamKit) and built upon leading-edge technologies consisting of: 1) a raw datastore; 2) a data enrichment pipeline with custom and third-party analytics; 3) enriched data storage; and 4) a custom UI to display the enriched data. Machine learning/deep learning models were selected and tested to classify social media images for relevancy and presence of flood waters. Our custom trained relevance models were run on a validation dataset and the overall accuracy of the relevance classifiers ranged from 65% to 90%. We found that convolutional neural network (CNN) models were both faster and more accurate than more traditional machine learning models. We applied the relevance classifier to remove non-relevant images and then undertook a limited training of CNN models to detect flood on ~1,800 labeled images. The testing accuracy of the flood detection models were ~88%. To improve the geographic location of both social media and oblique aerial images we tested a multi-step workflow built on a pre-trained deep-learning model. To identify similarity features from test and reference scenes, we applied the Xception and Faiss models to a set of test images with inaccurate spatial locations, as well and precisely-located reference scenes from the general area. Matching reference scenes to the test scenes with the closest similarity index had limited success in improving the precision of test scene location and additional methods have been identified for testing. Data fusion between authoritative geographic data (satellite imaging) and non-authoritative crowdsourced data is an underlying theme throughout this research and methods are tested bring these data to a common geographic form through feature matching and transformations. The automated extraction of supporting information for disaster response from oblique aerial images or ground-level social media images is accomplished through semantic image segmentation and transformation. The non-authoritative data are used to validate the authoritative data collections and processing and optionally, t+0 flood models. In addition, the common form geographic data are combined with other static landscape features to develop spatially-explicit training vectors used in deep generative statistical inference models to develop complex non-linear relationships in static and dynamic data and ultimately derive a spatially continuous and probabilistic assessment of flood damage with the event domain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Flow and heat transfer in the wake of a triangular arrangement of spheres

This research work seeks to investigate the influence of spacing and heat transfer on the wake behavior of a triangular arrangement of spheres. Four experimental configurations have been investigated at three Reynolds numbers, Re 1 = 350, Re 2 = 700, and Re 3 = 1050. Two isothermal cases were investigated with spacing between the spheres of zero and one sphere diameter, and two cases were investigated with an applied heat flux at the same spacing conditions. The time resolved particle image velocimetry results revealed various flow phenomena including flow separations, von Karman vortex shedding, and Kelvin–Helmholtz instabilities. The turbulent statistics reveal the effect of proximity and heat transfer on the time averaged values of the wake size, turbulent strengths, and Reynolds shear stress in the wake of each sphere, namely, the laminarization effects from the addition of heat and the suppression of the lead sphere wake from the proximity of the trailing spheres. These results are complemented by the application of proper orthogonal decomposition (POD) to the flow fields, which extracts the coherent structures from the flow. Here the modes that describe the coherent structures are extracted and described in detail, which provide further insight into effects of the experimental conditions on the temporal behavior of the flow. Many of the low order modes are found to be associated in pairs, corresponding to asymmetric structures or advection of a given structure downstream. The capability of POD to produce reduced order models of the flow is then utilized to facilitate vortex identification analysis. A turbulent kinetic energy based mode truncation criteria, which has been found to enhance vortex identification capability, is applied to select the POD modes and temporal coefficients to be used in the reduced order modeling. The reconstructed velocity fields are then analyzed with vortex identification algorithms to extract the vortex cores and boundaries. The combination of these approaches allows the study of the effect of proximity and heat transfer on the vortex characteristics, such as size, strength, and distribution.

42 ENGINEERING↗

DOE OSTI2019

FY19 Status of the Full Circumference Examination of the Inner Container Closure Weld Region for Selected 3013 DE Containers

One of the main focus areas of the 3013 Surveillance Program is a thorough evaluation of the inner container closure weld region (ICCWR) opened for destructive examination (DE). As part of the protocol to investigate the corrosion in the ICCWR a laser confocal microscope (LCM) is used to perform close visual examination of the surface and measurements of corrosion features on the surface. In FY17, DE containers from FY13 through FY16 were evaluated to select candidates for a full circumference analysis of the ICCWR. In FY18, the selected DE containers for full circumference evaluation (FCE) were processed according to the ICCWR protocol and LCM data collection was completed for the full circumference only for FY15 DE07. In FY19 the remaining DE containers were completed. The ICCWR protocol was completed as follows: (1) the FCE containers were sectioned into quarters and the weld removed to access the ICCWR; (2) a series of images of the ICCWR full circumference were taken using a stereo microscope and assembled into panoramic views; (3) SEM/EDS analysis performed on selected sections showed what is most likely corrosion products on the surface and chlorides randomly dispersed; (4) the presence of chlorides was also confirmed with ion chromatography of citric acid washes, in which FY15 DE07 and FY16 DE05 showed a significant amount of chlorides with FY15 DE08 showing the less amount of chlorides; (5) dye penetrant examination was performed on the interior and exterior surfaces of all the sidewall sections but reveled no relevant indication of surface-breaking defects, (6) finally, as described above, the full circumference examination of the ICCWR by LCM was completed. The three ICCWRs show general and localized corrosion on the surface. However, FY15 DE08 show more areas with agglomerated pits in Zones 2 and 3 than the other two DEs. The major suspect corrosion events were observed for FY15 DE07 on Sections C1 and C2 and for FY15 DE08 on Section C2. These events correspond to suspect cracks or crack-like features identified with a unique name to easily refer to each feature. FY15 DE07 shows a crack-like feature, identified as Acrux, in Section C1 and three features in Section C2 identified as Bellatrix, Cursa Minor and Cursa Major. Acrux and Bellatrix are located at the boundary of Zone 2 and Zone 3. Cursa Minor and Cursa Major are in Zone 2. FY15 DE08 shows two crack-like features, identified as Denebola and Draco, in Section C2. Both features are located at the boundary of Zone 2 and Zone 3. However, gas analysis and statistical evaluation of the gas compositions and pressures via GEST analyses suggest that leakage between the OI and IC volumes was unlikely. Additional characterization and analysis will be performed on the locations where the cracks were found.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

A catalog of visually classified galaxies in the local (z ∼ 0.01) universe

The morphological types of 5836 galaxies were classified by a visual inspection of color images using the Sloan Digital Sky Survey Data Release 7 to produce a morphology catalog of a representative sample of local galaxies with z<0.01. The sample galaxies are almost complete for galaxies brighter than r{sub pet}=17.77. Our classification system is basically the same as that of the Third Reference Catalog of Bright Galaxies with some simplifications for giant galaxies. On the other hand, we distinguish the fine features of dwarf elliptical (dE)-like galaxies to classify five subtypes: dE, blue-cored dwarf ellipticals, dwarf spheroidals (dSph), blue dwarf ellipticals (dE{sub blue}), and dwarf lenticulars (dS0). In addition, we note the presence of nucleation in dE, dSph, and dS0. Elliptical galaxies and lenticular galaxies contribute only ∼1.5 and ∼4.9% of local galaxies, respectively, whereas spirals and irregulars contribute ∼32.1 and ∼42.8%, respectively. The dE{sub blue} galaxies, which are a recently discovered population of galaxies, contribute a significant fraction of dwarf galaxies. There seem to be structural differences between dSph and dE galaxies. The dSph galaxies are fainter and bluer with a shallower surface brightness gradient than dE galaxies. They also have a lower fraction of galaxies with small axis ratios (b/a≲0.4) than dE galaxies. The mean projected distance to the nearest neighbor galaxy is ∼260 kpc. About 1% of local galaxies have no neighbors with comparable luminosity within a projected distance of 2 Mpc.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

58 GEOSCIENCES↗