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

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Current and Future γ-Ray Searches for Dark Matter Annihilation Beyond the Unitarity Limit

For decades, searches for electroweak-scale dark matter (DM) have been performed without a definitive detection. This lack of success may hint that DM searches have focused on the wrong mass range. A proposed candidate beyond the canonical parameter space is ultraheavy DM (UHDM). In this work, we consider indirect UHDM annihilation searches for masses between 30 TeV and 30 PeV—extending well beyond the unitarity limit at ~100 TeV—and discuss the basic requirements for DM models in this regime. We explore the feasibility of detecting the annihilation signature, and the expected reach for UHDM with current and future very-high-energy (VHE; >100 GeV) γ-ray observatories. Specifically, we focus on three reference instruments: two Imaging Atmospheric Cherenkov Telescope arrays, modeled on VERITAS and CTA-North, and one extended air shower array, motivated by HAWC. With reasonable assumptions on the instrument response functions and background rate, we find a set of UHDM parameters (mass and cross section) for which a γ-ray signature can be detected by the aforementioned observatories. Here, we further compute the expected upper limits for each experiment. With realistic exposure times, the three instruments can probe DM across a wide mass range. At the lower end, it can still have a point-like cross section, while at higher masses the DM could have a geometric cross section, indicative of compositeness.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Scalable Parallel Hypergraph Generator (HyGen)

Graphs are extensively used to model real-world complex systems. An edge in a graph can model pairwise relationships. However, multiway relationships (connections between three or more vertices) are common in many complex systems such as cellular process, image segmentation, and circuit design. A graph edge cannot model multiway relationships. A hypergraph, which can connect more than two vertices, is thus a better option to model multiway relationships. A large-scale hypergraph analysis has the potential to find useful insights from a complex system and assist in knowledge discovery. Currently a limited number of hypergraphs exists that are representative of real-world datasets. Moreover, real-world hypergraph datasets are small in size and inadequate to incorporate future needs. A graph generator that can produce large-scale synthetic hypergraphs can solve the above mentioned problems. In this paper, we present a scalable parallel hypergraph generator (HyGen) based on the Message Passing Interface (MPI) standard. To generate hypergraphs, HyGen takes the following parameter values as inputs: i) number of vertices, ii) number of hyperedges, iii) number of clusters, iv) vertex distribution, v) hyperedge distribution, vi) local cluster cardinality, and vii) global cluster cardinality. We have demonstrated that HyGen can generate hypergraphs of various sizes in a scalable fashion. HyGen takes approximately four minutes to generate a hypergraph with 4.8 million vertices, 1.6 million hyperedges, and 800 clusters using 1,024 processes on a leadership class computing platform. Our strong and weak scaling experiments on supercomputers demonstrate that HyGen can quickly create large-scale hypergraphs in a parallel manner, thus providing a useful capability for hypergraph analysis.

Hasan, S M Shamimul↗
DOE OSTI2023

An Update on the Colossus mK platform at Fermilab

As part of the efforts of the Superconducting Quantum Materials and Systems National Quantum Center at Fermilab, we will construct a large millikelvin refrigeration platform known as Colossus. The Colossus platform will be used for quantum computing applications, along with physics and sensing experiments. At the preceding CEC/ICMC meeting in 2021, we reported on the conceptual design of the platform. In the intervening time, the design of the system has been completed and passed through review, with construction now underway. This paper provides an update on the overall design of the system, and the status of and timeline for construction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Automated and efficient local adaptive regression for principal component-based reduced-order modeling of turbulent reacting flows

Principal Component Analysis can be used to reduce the cost of Computational Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the transported variables through projection of the thermochemical state onto a lower-dimensional manifold. However, because of the nonlinearity of the principal component source terms, nonlinear regression techniques must be utilized for the source terms in terms of the principal components. Unfortunately, widely available and utilized nonlinear regression techniques can have prohibitive computational requirements and/or accuracy that is highly dependent on user experience in ad hoc tuning of model architecture and hyperparameters. Here, in this work, a new nonlinear regression approach is proposed that is both computationally efficient and automated so does not require any user input. The approach is evaluated through a priori prediction of principal component source terms using data from a Direct Numerical Simulation of a turbulent nonpremixed n-heptane/air jet flame. In particular, the proposed framework consists of local regressions whose complexity is adapted according to the local nonlinearity of the data: local linear regression when accurate enough and local Artificial Neural Networks when nonlinear regression is required. The number of local clusters for local regression is determined automatically using the Davies-Bouldin index. In addition, Bayesian optimization is utilized for model training (i.e., to select the best architectures and hyperparameters of the nonlinear regressions in an unsupervised fashion), eliminating ad hoc hand-tuning and/or expensive grid searches. Overall, compared to a single, global neural network, the new local adaptive regression approach is shown to have comparable accuracy but 69% less training time due to the utilization of local linear regression and faster training of local neural networks.

42 ENGINEERING↗
DOE OSTI2026

Sensitivity of the CUPID experiment to $0\nu \beta \beta$ decay of 100 Mo

CUPID is a next-generation bolometric experiment to search for neutrinoless double-beta decay ($0\nu \beta \beta$ ) of 100 Mo using Li 2 MoO 4 scintillating crystals. It will operate 1596 crystals at ~10 mK in the CUORE cryostat at the Laboratori Nazionali del Gran Sasso in Italy. Each crystal will be facing two Ge-based bolometric light detectors for α rejection. We compute the discovery and the exclusion sensitivity of CUPID to $0\nu \beta \beta$ in a Frequentist and a Bayesian framework. This computation is done numerically based on pseudo-experiments. For the CUPID baseline scenario, with a background and an energy resolution of 1.0 x 10 –4 counts/keV/kg/yr and 5 keV FWHM at the Q-value, respectively, this results in a Bayesian exclusion sensitivity (90% c.i.) of $\hat{T}_{1/2} > 1.6 \times 10^{27} \ \textrm{yr}$, corresponding to the effective Majorana neutrino mass of $\hat{m}_{\beta \beta } < \ 9.6$ – 28 meV. The Frequentist discovery sensitivity (3σ ) is $\hat{T}_{1/2}= 1.0 \times 10^{27} \ \textrm{yr}$, corresponding to $\hat{m}_{\beta \beta }= \ 12$ – 36 meV.

Alfonso, K. [Virginia Polytechnic Institute and St↗
DOE OSTI2021

10 Years Later: Cloud Computing is Closing the Performance Gap

Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing high-performance computing. We present a multi-level approach to investigate the performance gap between HPC and cloud computing, isolating different variables that contribute to this gap. Our experiments are divided into (i) hardware and system microbenchmarks and (ii) user application proxies. The results show that today’s high-end cloud computing can deliver HPC-competitive performance not only for computationally intensive applications, but also for memory- and communication-intensive applications – at least at modest scales – thanks to the high-speed memory systems and interconnects and dedicated batch scheduling now available on some cloud platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Synergy of coupled kink and tearing modes in fast ion transport

One aspect of the interaction between fast ions and magnetohydrodynamic (MHD) instabilities is the fast ion transport. Coupled kink and tearing MHD instabilities have also been reported to cause fast ion transport. Recently, the 'kick' model has been developed to compute the evolution of the fast ion distribution from neutral beam injection using instabilities as phase-space resonance sources. Overall, the goal of this paper is to utilize the kick model to understand the physics of fast ion transport caused by the coupled kink and tearing modes. Soft x-ray diagnostics are used to identify the mode parameters in the National Spherical Torus Experiment. The comparison of neutron rates measured and computed from time-dependent TRANSP simulation with the kick model shows that the coupling of kink and tearing mode is important in determination of the fast ion transport. The numerical scan of the mode parameters shows that the relative phase of the kink and tearing modes and the overlapping of kink and tearing mode resonances in the phase space can affect the fast ion transport, suggesting that the synergy of the coupled modes may be causing the fast ion transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

High-throughput determination of Hubbard $U$ and Hund $J$ values for transition metal oxides via the linear response formalism

DFT+U provides a convenient, cost-effective correction for the self-interaction error (SIE) that arises when describing correlated electronic states using conventional approximate density functional theory (DFT). The success of a DFT+U(+J) calculation hinges on the accurate determination of its Hubbard U and Hund J parameters, and the linear response (LR) methodology has proven to be computationally effective and accurate for calculating these parameters. This study provides a high-throughput computational analysis of the U and J values for transition metal d-electron states in a representative set of over 1000 magnetic transition metal oxides (TMOs), providing a frame of reference for researchers who use DFT+U to study transition metal oxides. In order to perform this high-throughput study, an ATOMATE workflow is developed for calculating U and J values automatically on massively parallel supercomputing architectures. Here, to demonstrate an application of this workflow, the spin-canting magnetic structure and unit cell parameters of the multiferroic olivine LiNiPO4 are calculated using the computed Hubbard U and Hund J values for Ni-d and O-p states, and are compared with experiment. Both the Ni-d U and J corrections have a strong effect on the Ni-moment canting angle. Additionally, including a O-pU value results in a significantly improved agreement between the computed lattice parameters and experiment

36 MATERIALS SCIENCE↗
DOE OSTI2023

Efficient human activity recognition with spatio-temporal spiking neural networks

In this study, we explore Human Activity Recognition (HAR), a task that aims to predict individuals' daily activities utilizing time series data obtained from wearable sensors for health-related applications. Although recent research has predominantly employed end-to-end Artificial Neural Networks (ANNs) for feature extraction and classification in HAR, these approaches impose a substantial computational load on wearable devices and exhibit limitations in temporal feature extraction due to their activation functions. To address these challenges, we propose the application of Spiking Neural Networks (SNNs), an architecture inspired by the characteristics of biological neurons, to HAR tasks. SNNs accumulate input activation as presynaptic potential charges and generate a binary spike upon surpassing a predetermined threshold. This unique property facilitates spatio-temporal feature extraction and confers the advantage of low-power computation attributable to binary spikes. We conduct rigorous experiments on three distinct HAR datasets using SNNs, demonstrating that our approach attains competitive or superior performance relative to ANNs, while concurrently reducing energy consumption by up to 94%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. Here, the standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Assessment of Local Observation of Atomic Ordering in Alloys via the Radial Distribution Function: A Computational and Experimental Approach

As a powerful analytical technique, atom probe tomography (APT) has the capacity to acquire the spatial distribution of millions of atoms from a complex sample. However, extracting information at the Ångstrom-scale on atomic ordering remains a challenge due to the limits of the APT experiment and data analysis algorithms. The development of new computational tools enable visualization of the data and aid understanding of the physical phenomena such as disorder of complex crystalline structures. Here, we report progress towards this goal using two steps. We describe a computational approach to evaluate atomic ordering in the crystal structure by generating radial distribution functions (RDF). Atomic ordering is rendered as the Fractional Cumulative Radial Distribution Function (FCRDF) which allows for greater visibility of local compositions at short range in the structure. Further, we accommodate in the analysis additional parameters such as uncertainty in the atomic coordinates and the atomic abundance to ascertain short-range ordering in APT data sets. We applied the FCRDF analysis to synthetic and experimental APT data sets for Ni 3 Al. The ability to observe a signal of atomic ordering consistent with the known L1 2 crystal structure is heavily dependent on spatial uncertainty, irrespective of abundance. Detection of atomic ordering is subject to an upper limit of spatial uncertainty of atoms described with Gaussian distributions with a standard deviation of 1.3 Å. The FCRDF analysis was also applied to the APT data set for a six-component alloy, Al 1.3 CoCrCuFeNi. In this case, we are currently able to visualize elemental segregation at the nanoscale, though unambiguous identification of atomic ordering at the Ångstrom (nearest-neighbor) scale remains a goal.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MINE: maximally informative next experiment—toward a new GWAS experimental design and methodology

Abstract The computational methodology of Genome Wide Association Studies (GWAS) currently has several limitations: (i) the number of observations (rows) on a quantitative trait tends to be smaller than the number of single nucleotide polymorphisms (SNPs) (columns) in the design matrix; (ii) each SNP is usually modeled separately, failing to acknowledge interaction between each other (ie epistasis); (iii) there is implicit linkage disequilibrium (LD) between neighboring SNPs due to their linkage. To overcome these issues, we developed a tool that uses ensemble methods to fit mixed linear models to GWAS data, and these ensemble methods include the development of a new experimental design approach in GWAS, which uses the resultant models and data to select the next informative experiment over time. This new adaptive and staged approach for GWAS experimental design was developed and tested in a 3 yr adaptive model-guided discovery experiment against a fixed classical design. In Sorghum bicolor a total of 79, 86, and 78 accessions were tested in years 1, 2, and 3, respectively out of 343 accessions available in the Bioenergy Association Panel (BAP) each identified for 232,303 SNPs, 1 every 2–3 kb in the genomes. We demonstrated the feasibility of MINE enacted with 8 people in the field per year over 3 yr vs in 1 large classical design enacted with 20 people in 1 yr. The MINE results for chromosomal regions identified controlling dry weight were confirmed against results from previous sorghum GWAS experiments and 1 large classical design for the BAP panel.

Genetics & Heredity↗
DOE OSTI2023

Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files

First-principles computational spectroscopy is a critical tool for interpreting experiment, per- forming structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes and a diverse class of materials is a challeng- ing task with a very high barrier-to-entry, given the complexities and nuances of each individual simulation package. This task is non-trivial even for experts in the electronic structure field and nearly formidable for non-expert researchers. Lightshow solves this problem by providing a uniform abstraction for writing computational x-ray spectroscopy input files for multiple popular codes, including FEFF, VASP, OCEAN, exciting and XSpectra. Its extendable framework will also allow the community to easily add new functions and to incorporate new simulation codes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fast Algorithms for Scientific Data Compression

Many scientific simulations and experiments generate terabytes to petabytes of data daily, necessitating data compression techniques. Unlike video and image compression, scientists require methods that accurately preserve primary data (PD) and derived quantities of interest (QoIs). In our previous work, we demonstrated the effectiveness of hybrid compression techniques that combine machine learning with traditional approaches. This paper presents innovative computational techniques aimed at expediting the compression pipeline. Our experiments, conducted on two distinct platforms with a large-scale XGC-based fusion simulation, demonstrate that the overhead incurred by these new approaches is less than one percent of the computational resources needed for the simulation.

Banerjee, Tania↗
DOE OSTI2021

Quantum Workforce Development at Lawrence Livermore National Laboratory

The students applying to LLNL that are interested in quantum computing are being brought up with a universal quantum algorithm experience. The candidates we see have played with Qiskit or the IBM experience, they understand spin dynamics, and a few of them have attended hackathons by Rigetti or another company. These students have all been exposed to universal quantum computing, but not quantum engineering, quantum hardware, or even descriptions of noise. This is in a large part due to the simplified interface in current commercial quantum systems. To broaden the applicant pool, we have begun collaborating with several institutions to train quantum scientists and engineers. We are using the LLNL whitebox approach to testbed access in order to give students a look under the hood of quantum computing operations because that is what we desire in research scientists.

42 ENGINEERING↗
DOE OSTI2020

Atmospheric Convection and Air–Sea Interactions over the Tropical Oceans: Scientific Progress, Challenges, and Opportunities

Over the past thirty years, the scientific community has made considerable progress in understanding and predicting tropical convection and air-sea interactions, thanks to sustained investments in extensive in-situ and remote sensing observations, targeted field experiments, advances in numerical modeling, and vastly improved computational resources and observing technologies. Those investments would not have been fruitful as isolated advancements without the collaborative effort of the atmospheric convection and air-sea interaction research communities. In this spirit, a U.S. and International CLIVAR-sponsored workshop on “Atmospheric convection and air-sea interactions over the tropical oceans” was held May 7-9, 2019 in Boulder, CO. The 90 participants were observational and modeling experts from the atmospheric convection and air-sea interactions communities with varying degrees of experience, from early-career researchers and students to senior scientists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analysis of buffer-IPyC separation in TRISO fuel particles

During High Temperature Gas-cooled Reactor (HTGR) operation, and due to the neutron irradiation in the reactor core, damage of the nuclear fuel coating layers occurs. The mechanism of damage formation in the TRISO fuel is explored by the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program, in which the debonding process between coating layers was also investigated. The purpose of this paper is to report simulation results for two models. Firstly, the debonding restricted model, where no gap formation between buffer and IPyC layers is permitted. Secondly, a debonding enabled model, where the gap between those layers is created. The simulations were performed with the Bison code. The inputs of the simulated models are based on data from the AGR-1 experiment. Further, the research included simulations on spherical and aspherical fuel types. The simulations match results obtained by the AGR-1 experiment, which as such shows that the Bison code is a good computational method for simulating the behavior of the gap between buffer and IPyC layers in TRISO fuel. Based on the irradiation experiments, and the Bison simulations, it was concluded that the most common scenario is a gap formation along the buffer-IPyC interface, while the least possible scenario is the situation where there is no gap formation at the buffer-IPyC junction. The computational results confirmed that the sphericity of the fuel influences the thickness of the gap that occurs at the buffer-IPyC junction, in a way that with increasing aspect ratio the gap thickness increases. In addition, the fuel sphericity does not influence the Weibull failure probability. The results obtained for spherical and aspherical fuel are nearly identical.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗