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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 OSTI2024

Power Profile Monitoring and Tracking Evolution of System-Wide HPC Workloads

The power & energy demands of HPC machines have grown significantly. Modern exascale HPC systems require tens of megawatts of combined power for computing resources and cooling facilities at full capacity. The current energy trend is not sustainable for future HPC systems, and there is a need to work toward the energy efficiency aspect of HPC performance. Energy awareness of the HPC applications at the job level is essential for running an efficient HPC system. This work aims to develop a pipeline to provide a production-level system-wide overview of the HPC workloads' power profile while handling evolving workloads exhibiting new power trends. We developed an open-set classification model for HPC jobs based on the properties of power profiles to continuously provide a system-wide holistic view of recently completed jobs. The pipeline helps continuously monitor the job-level power usage pattern of HPC and enables us to capture the new trends in applications' power behavior. We employed a comprehensive set of techniques to generate job-level data, custom-designed feature extraction methods to extract critical features from jobs' power profiles, clustering techniques powered by generative modeling, and open-set classification for identifying job profiles into known classes or an unknown set. With extensive evaluations, we demonstrate the effectiveness of each component in our pipeline. We provide an analysis of the resulting clusters that characterize the power profile landscape of the Summit supercomputer from more than 60K jobs executed in a year. The open-set classification classifies the known data sets into known classes with high accuracy and identifies unknown data noints with over 85% accuracy.

Karimi, Ahmad Maroof↗
DOE OSTI2020

ENSIGN

ENSIGN is a data analytics software package offering a modern unsupervised machine learning solution for scalable discovery in Big Data. The analytics in ENSIGN are based on an advanced mathematical tool called tensor decomposition and they are optimized to run efficiently on a range of computing platforms (from small multicore Desktop platforms to large Supercomputing clusters and novel high-end memory-driven computing platforms such as HPE Superdome Flex). ENSIGN enables the user to extract deep insights from the entirety of massive-scale (100s of Gigabytes or Terabytes scale) multidimensional data. ENSIGN uncovers latent patterns in data without the user having to specify or describe what the patterns are; the user, in the first place, may not even know such patterns existed and that they have to look for such patterns. The insights gained from ENSIGN could be trailheads that can be used as starting points for deeper forensic investigation.

Baskaran, Muthu↗
DOE OSTI2022

ENSIGN

ENSIGN is a data analytics software package offering a modern unsupervised machine learning solution for scalable discovery in Big Data. The analytics in ENSIGN are based on an advanced mathematical tool called tensor decomposition and they are optimized to run efficiently on a range of computing platforms (from small multicore Desktop platforms to large Supercomputing clusters and novel high-end memory-driven computing platforms such as HPE Superdome Flex). ENSIGN enables the user to extract deep insights from the entirety of massive-scale (100s of Gigabytes or Terabytes scale) multidimensional data. ENSIGN uncovers latent patterns in data without the user having to specify or describe what the patterns are; the user, in the first place, may not even know such patterns existed and that they have to look for such patterns. The insights gained from ENSIGN could be trailheads that can be used as starting points for deeper forensic investigation.

Baskaran, Muthu↗
DOE OSTI2018

Mobilizing the anti-duck brigade: technology and market pathways for load-shifting demand response in California

With rapidly increasing renewable power generation, the California electricity system is experiencing the impacts of the so-called “duck curve,” with occasional over-generation in the middle of the day, followed by rapid ramping of net demand producing a sharp evening peak. Mitigating this pattern will require novel demand-side management approaches to shifting demand away from high net demand periods and toward high renewables generation periods. In this paper, drawing from more than 200,000 individual residential, commercial, and industrial customers’ hourly smart meter data, we disaggregate load shapes for certain key end-uses and then average the results across a highly granular set of customer segments by sector, building type, demand profile and geographic region, to yield a library of characteristic daily load profiles (“clusters”) for different end-uses and seasons. By categorizing the library into particularly desirable or undesirable load shapes in the context of mitigating the duck curve, we can gain insight into specific technologies, seasonal effects, or site-level attributes that may serve as helpful levers to use in developing future load-shifting programs. Based on these insights, we develop recommendations on technologies and new market structures and incentives that can create a future demand curve that more closely matches renewable generation patterns.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Guide to Conquer the Biological Network Era Using Graph Theory

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Constraining the Galactic bar using the M92 stellar stream

Stellar streams are excellent probes of the gravitational potential in which they evolve. In the Milky Way (MW), globular cluster (GC) streams are routinely used to infer properties about time-dependent perturbations of the underlying potential. This implies that streams with Galactocentric radii small enough to be perturbed by the MW bar should offer constraints on it, such as its pattern speed, which currently has a wide range of values reported in the literature and is important when studying stellar kinematics. The GC M92 has a small pericentre and should be affected by the bar. It has a diffuse stellar stream, but confirming stream members has previously been hindered by a lack of spectroscopic data. In this paper, we use Dark Energy Spectroscopic Instrument (DESI) observations together with photometric and astrometric data to obtain spectroscopic members of the M92 stream for the first time. We identify a clear spatial distribution and gradients in distance moduli, proper motions, and radial velocities that confirm the stream's existence. We compare the observed stream to mock streams generated in different barred potentials and estimate the MW bar's pattern speed $Ω= 29.1^{+0.7}_{-0.4}$ km s$^{-1}$ kpc$^{-1}$ and $\dot Ω= 0.7^{+3.5}_{-2.3}$ km s$^{-1}$ kpc$^{-1}$ Gyr$^{-1}$. This is the first time a stellar stream is used to probabilistically infer these bar properties, and it opens up an exciting realm of inner Galactic potential characterisation using stellar streams.

Byström, Amanda [Edinburgh U., Inst. Astron.] (ORC↗
DOE OSTI2017

Induction of α6 and β1 integrins by acupuncture stimulation in rats

Acupuncture therapy is performed by applying the needle insertion at discrete cutaneous locations and used for the treatments of diverse symptoms and disorders. In order to elucidate mechanistic basis on how acupuncture stimulation (AS) produces therapeutic effects, it is primarily important to understand tissue responses locally at the acupuncture site (acupoint). Here, we investigated integrin protein as molecular target responding to and integrating AS. Signals of α6 and β1 integrins were clearly induced at zusanli acupoint 24 h after AS in areas of nuclear clusters around the needle track. Induction levels of integrin were largely reduced by needle insertion at non-acupuncture point or without needle rotation. Phospho-Erk1/2 was initially decreased below the basal level after AS but increased 24 h later. Induction pattern of phospho-Erk1/2 was as similar as that of α6 integrin in its selectivity to needling procedure and tissue distribution. We further found that mRNA expression of P2X3 purinergic receptor was upregulated in the dorsal root ganglion (DRG) after AS, but decreased by the inhibition of Erk1/2 activity at the acupuncture area. Moreover, AS-mediated integrin activation was required for Erk1/2 activation at the acupuncture site and regulation of pain sensitivity in the hind paw. The present results provide a new evidence on acupuncture-specific tissue response in terms of integrin induction, and further suggest that integrin activation may be involved in transmitting mechanosensory signals from the acupoint to afferent nerve fiber. - Highlights: • We investigated local tissue responses after acupuncture stimulation (AS) in rats. • AS strongly induced α6 and β1 integrins and activated Erk1/2. • Integrin Induction was primarily specific to acupuncture site and needle rotation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Synthesis and characterisation of the novel double perovskites La{sub 2}CrB{sub 2/3}Nb{sub 1/3}O{sub 6}, B = Mg, Ni, Cu

Highlights: ► La{sub 2}CrB{sub 2/3}Nb{sub 1/3}O{sub 6}, B = Mg, Ni, Cu perovskites adopt the GdFeO{sub 3} type structure. ► HREM and ED study indicating week ordering of B-cations for B = Mg. ► Antiferromagnetics with T{sub N} 90 (Mg), 125 (Ni) and 140 K (Ni). -- Abstract: The novel perovskites La{sub 2}CrB{sub 2/3}Nb{sub 1/3}O{sub 6}, B = Mg, Ni, and Cu have been synthesised at 1350 °C in air via the citrate route. Rietveld refinements using neutron powder diffraction (NPD) data showed that the compounds adopt the GdFeO{sub 3} type structure with space group Pbnm, and unit cell parameters a≈b≈√(2)×a{sub p} and c ≈ 2 × a{sub p}, where a{sub p} ≈ 3.8 Å. Selected area electron diffraction (SAED) of B = Ni and Cu samples confirmed space group Pbnm. However, distinct reflections forbidden in Pbnm symmetry, but allowed in the monoclinic sub-group P2{sub 1}/n and unit cell parameters a≈b≈√(2)×a{sub p} and c ≈ 2 × a{sub p}, β ≈ 90° were present in SAED patterns of B = Mg sample. This indicates an ordering of the B-cations within the crystal structure of La{sub 2}CrMg{sub 2/3}Nb{sub 1/3}O{sub 6}. High-resolution electron microscopy (HREM) study indicating uniform, without formation of clusters, ordering of B-cations in the crystallites of La{sub 2}CrMg{sub 2/3}Nb{sub 1/3}O{sub 6}. Magnetic susceptibility measurements show that the compounds are antiferromagnetic (with some glass or spin clustering effects due to additional ferromagnetic interactions between the B-cations) with T{sub N} for La{sub 2}CrB{sub 2/3}Nb{sub 1/3}O{sub 6}, B = Mg, Ni, Cu being 90, 125 and 140 K, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration

In this study, we quantified the relationship between atmospheric rivers (ARs) and occurrence and magnitude of extreme precipitation in western U.S. watersheds, using AR tracking results of Atmospheric River Tracking Method Intercomparison Project and precipitation from a high-resolution regional climate simulation. Our analysis indicates that ARs have the potential of predicting extreme precipitation events at daily scale, with Gilbert Skill Score of ~0.2, and monthly extreme precipitation amount in the west coast watershed is closely related to AR intensity, with correlation coefficients of up to 0.6. The relationship between ARs and precipitation is most significant in the Pacific Northwest and California. Lastly, using K-means clustering algorithm, AR events can be classified into three categories with distinct features: weak ARs, flash ARs, and prolonged ARs. Flash ARs and prolonged ARs, though accounting for less than 50% of total AR events, are more important in controlling regional extreme precipitation patterns, and should be prioritized for future studies of hydrological extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Analysis of intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites

The evolution of recognition specificities by the immune system depends on the generation of receptor diversity and on connecting the binding of new antigens with the initiation of downstream signaling. In plant immunity, the innate Nucleotide-Binding Leucine-Rich Repeat (NLR) receptor family enables antigen binding and immune signaling. In this study, we surveyed the NLR complements of 62 ecotypes of Arabidopsis thaliana and 54 lines of Brachypodium distachyon and identified a limited number of NLR subfamilies that show high allelic diversity. We show that the predicted specificity-determining residues cluster on the surfaces of Leucine-Rich Repeat domains, but the locations of the clusters vary among NLR subfamilies. By comparing NLR phylogeny, allelic diversity, and known functions of the Arabidopsis NLRs, we formulate a hypothesis for the emergence of direct and indirect pathogen-sensing receptors and of the autoimmune NLRs. These findings reveal the recurring patterns of evolution of innate immunity and can inform NLR engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

On the effect of Ga and In substitutions in the Ca 11 Bi 10 and Yb 11 Bi 10 bismuthides crystallizing in the tetragonal Ho 11 Ge 10 structure type

The Ga- and In-substituted bismuthides Ca 11 Ga x Bi 10– x , Ca 11 In x Bi 10– x , Yb 11 Ga x Bi 10– x , and Yb 11 In x Bi 10– x ( x < 2) can be readily synthesized employing molten Ga or In metals as fluxes. They crystallize in the tetragonal space group I 4/ mmm and adopt the Ho 11 Ge 10 structure type (Pearson code tI 84; Wyckoff sequence n 2 m j h 2 e 2 d ). The structural response to the substitution of Bi with smaller and electron-poorer In or Ga has been studied by single-crystal X-ray diffraction methods for the case of Ca 11 In x Bi 10– x [ x = 1.73 (2); octabismuth undecacalcium diindium]. The refinements show that the In atoms substitute Bi only at the 8 h site. The refined interatomic distances show an unconventional – for this structure type – bond-length distribution within the anionic sublattice. The latter can be viewed as consisting of isolated Bi 3- anions and [In 4 Bi 8 20- ] clusters for the idealized Ca 11 In 2 Bi 8 model. Formal electron counting and first-principle calculations show that the peculiar bonding in this compound drives the system toward an electron-precise state, thereby stabilizing the observed bond-length pattern.

Chemistry↗
DOE OSTI2024

Investigating the relationship between microbial network features of giant kelp “seedbank” cultures and subsequent farm performance

Microbial inoculants can increase the yield of cultivated crops and are successful in independent trials; however, efficacy drops in large-scale applications due to insufficient consideration of microbial community dynamics. The structure of microbiomes, in addition to the impact of individual taxa, is an important factor to consider when designing growth-promoting inoculants. Here, we investigate the microbial network and community assembly patterns of Macrocystis pyrifera gametophyte germplasm cultures (collectively referred to as a “seedbank”) used to cultivate an offshore farm in Santa Barbara, California, and identify network features associated with increased biomass of mature sporophytes. We found that [1] several network features, such as clustering coefficient and edge ratios, significantly vary with biomass outcomes; [2] gametophytes that become low- or high-biomass sporophytes have different hub taxa; and [3] microbial community assembly of gametophyte germplasm cultures is niche-driven. Overall, this study describes microbial community dynamics in M . pyrifera germplasm cultures and ultimately supports the development of early life stage inoculants that can be used on seaweed cultivars to increase biomass yield.

Osborne, Melisa G. (ORCID:0000000154980057)↗
NASA NTRS2003

Intermediate Element Abundances In Galaxy Clusters

We present the average abundances of the intermediate elements obtained by performing a stacked analysis of all the galaxy clusters in the archive of the X-ray telescope AKA. We determine the abundances of Fe, Si, S, and Ni as a function of cluster temperature (mass) from 1 - 10 keV, and place strong upper limits on the abundances of Ca and Ar. In general, Si and Ni are overabundant with respect to Fe, while Ar and Ca are very underabundant. The discrepancy between the abundances of Si, S , Ar, and Ca indicate that the alpha-elements do not behave homogeneously as a single group. We show that the abundances of the most well-determined elements Fe, Si, and S in conjunction with recent theoretical supernovae yields do not give a consistent solution for the fraction of material produced by Type Ia and Type II supernovae at any temperature or mass. The general trend is for higher temperature clusters to have more of their metals produced in Type II supernovae than in Type Ias. The inconsistency of our results with abundances in the Milky Way indicate that spiral galaxies are not the dominant metal contributors to the intracluster medium (ICM). The pattern of elemental abundances requires an additional source of metals beyond standard SNIa and SNII enrichment. The properties of this new source are well matched to those of Type II supernovae with very massive, metal-poor progenitor stars. These results are consistent with a significant fraction of the ICM metals produced by an early generation of population III stars.

White, Nicholas E.↗