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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A pair of early- and late-forming galaxy cluster samples: A novel way of studying halo assembly bias assisted by a constrained simulation

The halo assembly bias, a phenomenon referring to dependencies of the large-scale bias of a dark matter halo other than its mass, is a fundamental property of the standard cosmological model. First discovered in 2005 from the Millennium Run simulation, it has been proven very difficult to be detected observationally, with only a few convincing claims of detection so far. The main obstacle lies in finding an accurate proxy of the halo formation time. In this study, by utilizing a constrained simulation that can faithfully reproduce the observed structures larger than 2 Mpc in the local universe, for a sample of 634 massive clusters at z ≤ 0.12, we found their counterpart halos in the simulation and used the mass growth history of the matched halos to estimate the formation time of the observed clusters. This allowed us to construct a pair of early- and late-forming clusters, with a similar mass as measured via weak gravitational lensing, and large-scale biases differing at the ≈3σ level, suggestive of the signature of assembly bias, which is further corroborated by the properties of cluster galaxies, including the brightest cluster galaxy and the spatial distribution and number of member galaxies. Our study paves a way to further detect assembly bias based on cluster samples constructed purely on observed quantities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Performance Results for Sensor Assignment Problem as Solved on a Multi-Node Cluster

An earlier report described a procedure for optimal sensor set selection and its implementation on a computational cluster. This new and innovative capability was developed to facilitate a reduction in operations staffing levels to improve plant economics. By automating surveillance and maintenance tasks through early detection of degrading sensors and equipment, staff can be more efficiently deployed. The method uses automated reasoning and domain knowledge in the form of the conservation equations to infer from plant measurements the state of equipment health. Inclusion of domain knowledge addresses the problem that exists with pure data-driven methods that there are no rigorous guidelines for determining what constitutes an adequate sensor set. Formalizing the procedure for sensor set selection as we have done results in a more reliable and explainable diagnosis of plant equipment health. Importantly, from the standpoint of the plant owner, personnel are provided with an early and explicit diagnosis of an equipment problem. That in principle automates the process and eliminates having to send personnel into the plant to find the cause as typically occurs when a data-driven method detects an anomaly. In this report we describe first results obtained using a computational cluster to solve the sensor set selection problem as framed above. The case described addresses the problem of equipment health monitoring in the high-pressure (HP) feedwater system of a pressurized light water reactor as seen through the eyes of our collaborating utility partner. Maintenance of this system can amount to millions of dollars per year if equipment health issues go undiagnosed and lead to loss of function. On examining the potential that is inherent in the installed sensor set for diagnosing equipment health degradation, it was found that greater fault resolution capability can be achieved using a sensor set that is 20 percent fewer in number. The take-away is that compared to the installed sensor set there exists a more strategic assignment of sensors that will furnish better health monitoring capability and with fewer sensors. Where the problem defies solution by manual inspection, as is the case here, one can be found by an algorithm. The solution was obtained in four hours using 30 computational cores. The HP feedwater problem as posed above illustrates the added value of approaching the sensor selection problem as one amenable to algorithmic solution. This problem is of interest to advanced reactor designers and to utilities that are setting up remote monitoring and diagnostic centers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Simultaneous global and local clustering in multiplex networks with covariate information

Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a new model, the hierarchical multiplex stochastic blockmodel, which simultaneously detects communities within individual layers of a multiplex network while inferring a global node clustering across the layers. A stochastic blockmodel is assumed in each layer, with probabilities of layer-level group memberships determined by a node’s global group assignment. Our model uses a Bayesian framework, employing a probit stick-breaking process to construct node-specific mixing proportions over a set of shared Griffiths–Engen–McCloseky distributions. These proportions determine layer-level community assignment, allowing for an unknown and varying number of groups across layers, while incorporating nodal covariate information to inform the global clustering. We propose a scalable variational inference procedure with parallelisable updates for application to large networks. Extensive simulation studies demonstrate our model’s ability to accurately recover both global and layer-level clusters in complicated settings, and applications to real data showcase the model’s effectiveness in uncovering interesting latent network structure.

community detection↗
DOE OSTI2024

Searching for parity violation in SDSS DR16 Lyman-α forest data

The four-point correlation function is the lowest order correlation function for scalar fields that can be used to probe statistical parity invariance in an isotropic universe. There are intriguing claims of detection of parity violation in the 4-point function of BOSS galaxy clustering data. We apply the same estimator to the public SDSS Data Release 16 Lyman-α forest data. Lyman-α forest data probes a different redshift range and is sensitive to a different density regime using a completely different technique. A detection would therefore be a strong indication of new physics. We identify an accurate covariance matrix as a crucial impediment to performing this measurement accurately, consistent with existing literature on galaxy 4-point function. Here, we discuss several approaches to estimating the covariance matrix, several of which produce spurious detection. Using a robust, but very suboptimal, covariance matrix derived from subsample bootstrapping, we find no evidence for parity violation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The active CGCG 077-102 NED02 galaxy within the Abell 2063 galaxy cluster

Context.Within the framework of investigating the link between the central super massive black holes in the cores of galaxies and the galaxies themselves, we detected a variable X-ray source in the center of CGCG 077-102 NED02, which is a member of the CGCG 077-102 galaxy pair within the Abell 2063 cluster of galaxies. Aims.Our goal is to combine X-ray and optical data to demonstrate that this object harbors an active super massive black hole in its core, and to relate this to the dynamical status of the galaxy pair within the Abell 2063 cluster. Methods.We usedChandraandXMM-Newtonarchival data to derive the X-ray spectral shape and variability. We also obtained optical spectroscopy to detect the expected emission lines that are typically found in active galactic nuclei. Finally, we used public ZTF imaging data to investigate the optical variability. Results.There is no evidence of multiple X-ray sources or extended components within CGCG 077-102 NED02. Single X-ray spectral models fit the source well. We detect significant, nonrandom inter-observation 0.5–10 keV X-ray flux variabilities, for observations separated by ∼4 days for short-term variations and by up to ∼700 days for long-term variations. Optical spectroscopy points toward a passive galaxy for CGCG 077-102 NED01 and a Seyfert for CGCG 077-102 NED02. The classification of CGCG 077-102 NED02 is also consistent with its X-ray luminosity of over 10 42 erg s −1 . We do not detect short-term variability in the optical ZTF light curves. However, we find a significant long-term stochastic variability in theg-band that can be well described by the damped random walk model with a best-fit characteristic damping timescale ofτ DRW = 30 −12 +28 days. Finally, the CGCG 077-102 galaxy pair is deeply embedded within the Abell 2063 potential, with a long enough history within this massive structure to have been affected by the influence of this cluster for a long time. Conclusions.Our observations point toward a moderately massive black hole in the center of CGCG 077-102 NED02 of ∼10 6 M ⊙ . As compared to another similar pair in the literature, CGCG 077-102 NED02 is not heavily obscured, perhaps because of the surrounding intracluster medium ram-pressure stripping.

Astronomy & Astrophysics↗
DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2023

Limits of Detection for EXAFS Characterization of Heterogeneous Single-Atom Catalysts

Single-atom catalysts (SACs), consisting of individual metal atoms dispersed on a support, attract attention due to their unique reactivity, efficient use of precious metals, and precise chemical tunability. Characterization of the metal species is crucial to substantiate structure–function relationships. Authors often use—and referees often require—X-ray absorption spectroscopy (XAS) data to prove the absence of clustered metal (or metal oxide) structures after pre-treatment and under in situ or operando conditions. However, there has been no critical assessment of the limitations of XAS in substantiating such conclusive statements, which is particularly important given the potential outsized influence of minority catalyst structures in dictating catalytic activity. Here, in this article, we quantitatively assess the detection limits of XAS to identify metal (or metal oxide) clusters in samples containing predominantly single atoms by modeling the extended X-ray absorption fine structure (EXAFS) of mixtures of structures. We identified that a significant fraction of clusters can coexist with SAC active sites (e.g., ~10% metallic Pt or ~40% oxidized Pt clusters in Pt/CeO 2 SACs), while eluding detection via EXAFS with any statistical significance. To generalize these conclusions, a descriptor-based screening of bulk metal oxides using a continuous Cauchy wavelet transform was proposed that suggests certain materials for which differentiating atomically dispersed metal species and metal oxide clusters would be infeasible by EXAFS (e.g., ReO x ). Based on this analysis, we suggest best practices for the study of SACs using EXAFS and provide recommendations to ensure that conclusions do not outpace the evidence used to support them. In this rapidly expanding research area, rigorous characterization will lead to greater understanding of the behavior of SACs and ultimately improved catalytic materials.

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

Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure

ABSTRACT A crucial aspect of mass mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been largely ignored to date. We present a new method to reconstruct maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem with Laplace-type l1-norm sparsity-promoting priors, which we solve via convex optimization. Approaching mass mapping in this manner allows us to exploit recent developments in probability concentration theory to infer theoretically conservative uncertainties for our MAP reconstructions, without relying on assumptions of Gaussianity. For the first time, these methods allow us to perform hypothesis testing of structure, from which it is possible to distinguish between physical objects and artefacts of the reconstruction. Here, we present this new formalism, and demonstrate the method on simulations, before applying the developed formalism to two observational data sets of the Abell 520 cluster. Initial reconstructions of the Abell 520 catalogues reported the detection of an anomalous ‘dark core’ – an overdense region with no optical counterpart – which was taken to be evidence for self-interacting dark matter. In our Bayesian framework, it is found that neither Abell 520 data set can conclusively determine the physicality of such dark cores at $99{{\ \rm per\ cent}}$ confidence. However, in both cases the recovered MAP estimators are consistent with both sets of data.

Price, M. A.↗
DOE OSTI2022

The alignment between brightest cluster galaxies and host clusters

The alignment between the brightest cluster galaxies (BCGs) and host clusters can reveal the mystery of the formation and evolution of galaxy clusters. We measure cluster orientations in optical based on the projected distribution of member galaxies and in X-ray by fitting the morphology of intracluster medium (ICM). Cluster orientations determined in the two wavelengths are generally consistent. The orientation alignment between BCGs and host clusters is confirmed and more significant than in previous works. We find that BCGs are more aligned with cluster orientations measured in X-ray than those from optical data. Clusters with a brighter BCG generally show a stronger alignment. We argue that the detected redshift evolution of the alignment is probably caused by observational bias rather than intrinsic evolution. The alignment is not related to the ellipticity of BCGs, and the richness, ellipticity, and dynamical state of host clusters. As a result, the strong alignment between BCGs and the morphology of ICMs may be the consequence of the co-evolution between the central massive galaxy and host clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Shape evolution of neutron-rich 106,108,110 Mo isotopes in the triaxial degree of freedom

Background: Neutron-rich nuclei with mass number between 100 and 110 attract much attention, since several kinds of shapes, such as spherical, prolate, oblate, and triaxial shapes, are predicted. In particular, for neutron-rich Mo isotopes, different models predict different magnitudes and rigidity of triaxial deformation. Previous interpretations of experimental results based solely on low-lying $2^+_2$ states are insufficient to distinguish between the rigid triaxial shape, $\gamma$ vibration, or $\gamma$-soft rotor. Purpose: The shape evolution of 106 Mo, 108 Mo, and 110 Mo is investigated through their $2^+_1$-state lifetimes, decay-branching ratios of the $2^+_2$ state, and energies of the low-lying collective excited states with $K^π = 0^+, 2^+$, and $4^+$. Method: $\beta$-delayed $\gamma$-ray spectroscopy was employed for neutron-rich Nb and Zr isotopes produced at the RIKEN RI Beam Factory to populate excited states in 106 Mo, 108 Mo, and 110 Mo. The EUroball-RIKEN Cluster Array was used for high-resolution $\gamma$-ray detection and lifetimes of the $2^+_1$ states were determined using the UK fast-timing array of LaBr 3 (Ce) detectors. Results: New $\gamma$-ray transitions and levels are reported, including newly assigned $0^+_2$ states in 108,110 Mo. Quadrupole deformations were obtained for 106,108,110 Mo from their $2^+_1$ energies and lifetimes. The $\beta$-delayed neutron-emission probabilities of 108 Nb and 110 Nb were determined by examining the $\gamma$ rays of their respective daughter decays. Conclusions: In this work, the even-odd energy staggering in the $2^+_2$ band was compared with typical patterns of the $\gamma$-vibrational band, rigid triaxial rotor, and $\gamma$-soft rotor. The very small even-odd staggering of 106 Mo, 108 Mo, and 110 Mo favors a $\gamma$-vibrational band assignment. The kinematic moment of inertia for the $2^+_2$ band showed a trend similar to the ground-state band, which is as expected for the $\gamma$-vibrational band. Beyond-mean-field calculations employing the constrained Hartree-Fock-Bogoliubov and local quasiparticle-random-phase approximation method using the SLy5 + T interaction reproduced the ground and $2^+_2$ bands in 106 Mo and 108 Mo. The collective wave functions are consistent with the interpretation of the $2^+_2$ band as the $\gamma$-vibrational band of the prolate shape. However, the staggering pattern observed in 110 Mo differs from the one suggested in the calculations which predict a $\gamma$-soft rotor. There was no experimental indication of the oblate shape or the $\gamma$-soft rotor predicted in these Mo isotopes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations

Abstract Background Peatlands are expected to experience sustained yet fluctuating higher temperatures due to climate change, leading to increased microbial activity and greenhouse gas emissions. Despite mounting evidence for viral contributions to these processes in peatlands underlain with permafrost, little is known about viruses in other peatlands. More generally, soil viral biogeography and its potential drivers are poorly understood at both local and global scales. Here, 87 metagenomes and five viral size-fraction metagenomes (viromes) from a boreal peatland in northern Minnesota (the SPRUCE whole-ecosystem warming experiment and surrounding bog) were analyzed for dsDNA viral community ecological patterns, and the recovered viral populations (vOTUs) were compared with our curated PIGEON database of 266,125 vOTUs from diverse ecosystems. Results Within the SPRUCE experiment, viral community composition was significantly correlated with peat depth, water content, and carbon chemistry, including CH 4 and CO 2 concentrations, but not with temperature during the first 2 years of warming treatments. Peat vOTUs with aquatic-like signatures (shared predicted protein content with marine and/or freshwater vOTUs) were significantly enriched in more waterlogged surface peat depths. Predicted host ranges for SPRUCE vOTUs were relatively narrow, generally within a single bacterial genus. Of the 4326 SPRUCE vOTUs, 164 were previously detected in other soils, mostly peatlands. None of the previously identified 202,371 marine and freshwater vOTUs in our PIGEON database were detected in SPRUCE peat, but 0.4% of 80,714 viral clusters (VCs, grouped by predicted protein content) were shared between soil and aquatic environments. On a per-sample basis, vOTU recovery was 32 times higher from viromes compared with total metagenomes. Conclusions Results suggest strong viral “species” boundaries between terrestrial and aquatic ecosystems and to some extent between peat and other soils, with differences less pronounced at higher taxonomic levels. The significant enrichment of aquatic-like vOTUs in more waterlogged peat suggests that viruses may also exhibit niche partitioning on more local scales. These patterns are presumably driven in part by host ecology, consistent with the predicted narrow host ranges. Although more samples and increased sequencing depth improved vOTU recovery from total metagenomes, the substantially higher per-sample vOTU recovery after viral particle enrichment highlights the utility of soil viromics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Chemical Compositions of Red Giant Stars from Habitable Zone Planet Finder Spectroscopy

We have used the Habitable Zone Planet Finder (HPF) to gather high-resolution, high signal-to-noise near-infrared spectra of 13 field red horizontal branch (RHB) stars, one open cluster giant, and one very metal-poor halo red giant. The HPF spectra cover the 0.81–1.28 μm wavelength range of the zyJ bands, partially filling the gap between the optical (0.4–1.0 μm) and infrared (1.5–2.4 μm) spectra already available for the program stars. We derive abundances of 17 species from LTE-based computations involving equivalent widths and spectrum syntheses, and estimate abundance corrections for the species that are most affected by departures from LTE in RHB stars. Generally good agreement is found between HPF-based metallicities and abundance ratios and those from the optical and infrared spectral regions. Light element transitions dominate the HPF spectra of these red giants, and HPF data can be used to derive abundances from species with poor or no representation in optical spectra (e.g., C i, P i, S i, K i). Attention is drawn to the HPF abundances in two field solar-metallicity RHB stars of special interest: one with an extreme carbon isotope ratio, and one with a rare, very large lithium content. The latter star is unique in our sample in exhibiting very strong He i 10830 Å absorption. The abundances of the open cluster giant concur with those derived from other wavelength regions. Detections of C i and S i in HD 122563 are reported, yielding the lowest metallicity determination of [S/Fe] from more than one multiplet.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

The Stellar Content of H72.97-69.39, a Potential Super Star Cluster in the Making

Young massive clusters and super star clusters (SSCs) represent an extreme mode of star formation. Far-infrared imaging of the Magellanic Clouds has identified one potential embedded SSC, HSO BMHERICC J72.971176-69.391112 (H72.97-69.39 in short), in the southwest outskirts of the Large Magellanic Cloud. We present Gemini Flamingos 2 and GSAOI near-infrared imaging of a 3′ × 3′ region around H72.97-69.39 in order to characterize the stellar content of the cluster. The stellar content is probed down to 1.5 M {sub ⊙}. We find substantial dust extinction across the cluster region, extending up to A {sub K} of 3. Deeply embedded stars are associated with ALMA-detected molecular gas suggesting that star formation is ongoing. The high spatial resolution of the GSAOI data allows identification of the central massive object associated with the {sup 13}CO ALMA observations and detection of fainter low-mass stars around the H30α ALMA source. The morphology of the molecular gas and the nebulosity from adjacent star formation suggest they have interacted covering a region of several parsecs. The total stellar content in the cluster is estimated from the intermediate- and high-mass stellar content to be at least 10,000 M {sub ⊙}, less than R136 with up to 100,000 M {sub ⊙} within 4.7 pc radius, but places it in the regime of an SSC. Based on the extinction determination of individual stars we estimate a molecular gas mass in the vicinity of H72.97-69.39 of 6600 M {sub ⊙}, suggesting more star formation can be expected.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

The z ∼ 1.03 Merging Cluster SPT-CL J0356–5337: New Strong Lensing Analysis with HST and MUSE

We present a strong lensing analysis and reconstruct the mass distribution of SPT-CL J0356−5337, a galaxy cluster at redshift z = 1.034. Our model supersedes previous models by making use of new multiband Hubble Space Telescope data and Multi-Unit Spectroscopic Explorer (MUSE) spectroscopy. We identify two additional lensed galaxies to inform a more well-constrained model using 12 sets of multiple images in five separate lensed sources. The three previously known sources were spectroscopically confirmed by G. Mahler et al. at redshifts of z = 2.363, z = 2.364, and z = 3.048. We measured the spectroscopic redshifts of two of the newly discovered arcs using MUSE data, at z = 3.0205 and z = 5.3288. We increase the number of cluster member galaxies by a factor of 3 compared to previous work. We also report the detection of extended Lyα emission from several background galaxies. We measure the total projected mass density of the two major subcluster components, one dominated by the brightest cluster galaxy and the other by a compact group of luminous red galaxies. We find ${M}_{{\rm{B}}{\rm{C}}{\rm{G}}}(\lt 80\,\rm{kpc})=3.9{3}_{-0.14}^{+0.21}\times 1{0}^{13}$ M⊙ and ${M}_{{\rm{LRG}}}(\lt 80\,\rm{kpc})=2.9{2}_{-0.23}^{+0.16}\times 1{0}^{13}$ M⊙, yielding a mass ratio of $1.3{5}_{-0.08}^{+0.16}$ . The strong lensing constraints offer a robust estimate of the projected mass density regardless of modeling assumptions; allowing more substructure in this line of sight does not change the results or conclusions. Our results corroborate the conclusion that SPT-CL J0356−5337 is dominated by two mass components and is likely undergoing a major merger on the plane of the sky.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Catalytic C 2 H 2 synthesis via low temperature CO hydrogenation on defect-rich 2D-MoS 2 and 2D-MoS 2 decorated with Mo clusters

Rational design of novel catalytic materials used to synthesize storable fuels via the CO hydrogenation reaction has recently received considerable attention. In this work, defect poor and defect rich 2D-MoS 2 as well as 2D-MoS 2 decorated with Mo clusters are employed as catalysts for the generation of acetylene (C 2 H 2 ) via the CO hydrogenation reaction. Temperature programmed desorption is used to study the interaction of CO and H2 molecules with the MoS 2 surface as well as the formation of reaction products. The experiments indicate the presence of four CO adsorption sites below room temperature and a competitive adsorption between the CO and H 2 molecules. The investigations show that CO hydrogenation is not possible on defect poor MoS 2 at low temperatures. However, on defect rich 2D-MoS 2 , small amounts of C 2 H 2 are produced, which desorb from the surface at temperatures between 170 K and 250 K. A similar C 2 H 2 signal is detected from defect poor 2D-MoS 2 decorated with Mo clusters, which indicates that low coordinated Mo atoms on 2D-MoS 2 are responsible for the formation of C 2 H 2 . Density functional theory investigations are performed to explore possible adsorption sites of CO and understand the formation mechanism of C 2 H 2 on MoS 2 and Mo 7 /MoS 2 . The theoretical investigation indicates a strong binding of C 2 H 2 on the Mo sites of MoS 2 preventing the direct desorption of C 2 H 2 at low temperatures as observed experimentally. Instead, the theoretical results suggest that the experimental data are consistent with a mechanism in which CHO radical dimers lead to the formation of C 2 H 2 that presents an exothermic desorption.

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

Lamb wave imaging with actuator network for damage quantification in aluminum plate structures

Lamb waves have been widely used for damage detection on plate-like structures. However, there are still considerable interests on quantifying damage with complex profile. In this article, quantification of complex damage in plate-like structures using a network of actuators and time-space Lamb wavefield is investigated. The actuator network inspection system is implemented with multiple PZT transducers for Lamb wave actuation in round robin pattern and scanning laser Doppler vibrometer for wavefield sensing. The PZT network is arranged in a way that the target area is fully enclosed and Lamb waves come to the damage from all directions. Waves induced by the damage are subsequently obtained through frequency-wavenumber filtering, using the experimentally acquired dispersion curves presented in the paper. The filtered waves from all wave actuators are then used to generate a synthetic image of the damage being inspected. Two cases of complex damage are evaluated on aluminum plates, mass loss with triangular profile and mass addition with a three-letter cluster profile. Our results show that the damages are not only detected but also their profiles are clearly outlined in the images. We believe the subject methods provide improved evaluation of damage profile for Lamb wavefield based damage quantification.

Materials Science↗
DOE OSTI2020

Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification

SUMMARY A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.

58 GEOSCIENCES↗