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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 OSTI2017

Soil microbial beta-diversity is linked with compositional variation in aboveground plant biomass in a semi-arid grassland

Exploring biodiversity linkages between above-ground and below-ground biota is a core topic in ecology, and can have implications on our understanding of ecosystem process stability. Yet, this topic still remains underexplored. Here, we explored diversity linkages, in terms of both alpha- and beta- diversity, between plant and top soil microbial communities in a semi-arid grassland ecosystem. Soil microbial community structure was assessed based on both 16S rRNA and functional genes, and plant community composition was evaluated by traditional “species composition” and a newly-defined “biomass composition”, which includes the information on the biomass of each species. The bacterial alpha-diversity, expressed as the richness and Shannon diversity of 16S rRNA genes, was significantly correlated with plant species richness and Shannon diversity, whereas the alpha-diversity of microbial functional genes showed marginal association with total plant biomass. Microbial beta-diversity, evaluated by 16S rRNA genes, showed close relationship with plant beta-diversity estimated by both “species composition” and “biomass composition”, while the microbial beta-diversity based on functional genes was only associated with the compositional variation in above-ground plant biomass. These results showed that the differences in metabolic potential of soil microbial communities, which is closely related with ecosystem functions, can be better predicted by the variation of plant-derived resources returned to soil, than merely by the species composition of the macro-organism communities.

16s rRNA↗
DOE OSTI2019

Ambient vapor sampling and selective cluster formation for the trace detection of tributyl phosphate via atmospheric flow tube mass spectrometry

In addition to serving as an f-element ligand and rare-earth method complexing agent, tributyl phosphate (TBP) is a compound containing core functional groups that mimic those routinely found in degradation products from industrial processes. Because detection of trace quantities of TBP can provide insight into the routes of contamination and degradation in the environment, there is a need to develop methods capable of detecting trace quantities of TBP. Vapor detection at atmospheric pressure is one approach that is both sensitive and rapid. We present here the use of atmospheric flow tube mass spectrometry (AFT-MS) for the ambient vapor sampling of TBP from headspace of ppb-level solutions in methanol. Gas phase clustering reactions were to enhance detection levels via the addition of small quantities of the dopants diethylamine (DEA), triethylamine (TEA), and pinacolyl methylphosphonate (PMP) in the vapor stream. Detection of the TBP vapor emanating from these solutions demonstrated a linear range for the protonated TBP species of 1-1000 ppb from solution. The clusters of TBP with DEA, TEA, and PMP each yielded linear ranges of 1-250 ppb for TBP in solution. Despite smaller linear ranges, the addition of these dopant species added a layer of analytical selectivity and reduced variability in signals from quality control samples. These data were obtained using an AFT source coupled to a linear ion trap mass spectrometer, which demonstrates the applicability of trapping systems to the AFT ionization technique while monitoring positive ions.

Ambient vapor sampling, Dielectric barrier dischar↗
DOE OSTI2019

Investigation of Accessible Pore Structure Evolution under Pressurization and Adsorption for Coal and Shale Using Small-Angle Neutron Scattering

Pore structure is an important parameter to quantify the reservoir rock adsorption capability and diffusivity, both of which are fundamental reservoir properties to evaluate the gas production and carbon sequestration potential for coalbed methane (CBM) and shale gas reservoirs. In this study, we applied small-angle neutron scattering (SANS) to characterize the total and accessible pore structures for two coal and two shale samples. We carried out in situ SANS measurements to probe the accessible pore structure differences under argon, deuterated methane (CD 4 ), and CO 2 penetrations. The results show that the total porosity ranges between 0.25 and 5.8% for the four samples. Less than 50% of the total pores are accessible to CD 4 for the two coals, while more than 75% of the pores were found to be accessible for the two shales. This result suggests that organic matter pores tend to be disconnected compared to mineral matter pores. Argon pressurization can induce pore contraction because of the mechanical compression of the solid skeleton in both the coal and shale samples. Hydrostatic compression has a higher effect on the nanopores of coal and shale with a higher accessible porosity. Both methane and CO 2 injection can reduce the accessible nanopore volume due to a combination of mechanical compression, sorption-induced matrix swelling, and adsorbed molecule occupation. CO 2 has higher effects on sorption-induced matrix swelling and pore filling compared to methane for both the coal and shale samples. Gas densification and pore filling could occur at higher pressures and smaller pore sizes. In conclusion, the compression and adsorption could create nanopores in the San Juan coal and Marcellus shale drilled core but could have an opposite effect in the other samples, namely, the processes could damage the nanopores in the Hazleton coal and Marcellus shale outcrop.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer’s Disease Across Distinct Brain Regions

Alzheimer’s disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) e4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes of AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 Alzheimer’s disease (AD) and 29 controls (Ctrl) were studied to balance the number of e4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, e4 allele carriers (APOE4+ for those carrying 1 or 2 e4s and APOE4- for non-e4 carriers), and differences in e3e3 and e3e4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). In conclusion, the region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

Alzheimer’s disease↗
DOE OSTI2018

Structure–Property of Lithium–Sulfur Nanoparticles via Molecular Dynamics Simulation

Lithium-sulfur (Li-S) batteries offer higher energy densities than most reported lithium-ion batteries. However, our understanding of Li-S battery is still largely unknown at the level of the nanoscale. Here, the structural properties of Li-S materials were investigated via molecular dynamics (MD) simulations using the ReaxFF force field. everal Li-S nanoparticles with different Li:S composition ratios (2:1 and 2:8) and various structures are studied. Our MD simulations show that, among the four structures we constructed for Li 2 S 8 nanoparticles, the core-shell structure is the most thermodynamically stable one during the charging (delithiation) process. In contrast to bulk crystal Li 2 S, we find the presence of mixed lithium sulfide and polysulfide species are common features for these Li-S (Li 2 S, Li 2 S 8 ) nanoparticles. The complex distribution of these sulfide and polysulfide speciation are dictated by both stoichiometry and local atomic structures in the nanoparticle. Finally, these findings will provide insight into further development of functionalized lithium-sulfur cathodes.

25 ENERGY STORAGE↗
DOE OSTI2024

Multithreading ATLAS offline software: A retrospective

For Run 3, ATLAS redesigned its offline software, Athena, so that the main workflows run completely multithreaded. The resulting substantial reduction in the overall memory requirements allows for better use of machines with many cores. This note will discuss the performance achieved by the multithreaded reconstruction, the process of migrating the large ATLAS code base, and tools and techniques that were useful in debugging threading-related problems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Femtosecond laser fabrication of nanograting-based distributed fiber sensors for extreme environmental applications

Abstract The femtosecond laser has emerged as a powerful tool for micro- and nanoscale device fabrication. Through nonlinear ionization processes, nanometer-sized material modifications can be inscribed in transparent materials for device fabrication. This paper describes femtosecond precision inscription of nanograting in silica fiber cores to form both distributed and point fiber sensors for sensing applications in extreme environmental conditions. Through the use of scanning electron microscope imaging and laser processing optimization, high-temperature stable, Type II femtosecond laser modifications were continuously inscribed, point by point, with only an insertion loss at 1 dB m −1 or 0.001 dB per point sensor device. High-temperature performance of fiber sensors was tested at 1000 °C, which showed a temperature fluctuation of ±5.5 °C over 5 days. The low laser-induced insertion loss in optical fibers enabled the fabrication of a 1.4 m, radiation-resilient distributed fiber sensor. The in-pile testing of the distributed fiber sensor further showed that fiber sensors can execute stable and distributed temperature measurements in extreme radiation environments. Overall, this paper demonstrates that femtosecond-laser-fabricated fiber sensors are suitable measurement devices for applications in extreme environments.

Wang, Mohan (ORCID:0000000166784993)↗
DOE OSTI2017

Fermi Large Area Telescope as a Galactic Supernovae Axionscope

In a Galactic core-collapse supernova (SN), axionlike particles (ALPs) could be emitted via the Primakoff process and eventually convert into γ rays in the magnetic field of the Milky Way. From a data-driven sensitivity estimate, we find that, for a SN exploding in our Galaxy, the Fermi Large Area Telescope (LAT) would be able to explore the photon-ALP coupling down to g aγ ≃ 2 × 10 -13 GeV -1 for an ALP mass m a ≲ 10 -9 eV. Also, these values are out of reach of next generation laboratory experiments. In this event, the Fermi LAT would probe large regions of the ALP parameter space invoked to explain the anomalous transparency of the Universe to γ rays, stellar cooling anomalies, and cold dark matter. Lastly, if no γ-ray emission were to be detected, Fermi-LAT observations would improve current bounds derived from SN 1987A by more than 1 order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Distributed Ledger Technology for Fault Tolerant Distribution Grid Operations

This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers' privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.001% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Fast and Accurate Support Vector Machines on Large Scale Systems

Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

Vishnu, Abhinav↗
DOE OSTI2022

Turbulent Rotating Rayleigh–Bénard Convection

Rotation with thermally induced buoyancy governs many astrophysical and geophysical processes in the atmosphere, ocean, sun, and Earth's liquid-metal outer core. Rotating Rayleigh–Bénard convection (RRBC) is an experimental system that has features of rotation and buoyancy, where a container of height H and temperature difference Δ between its bottom and top is rotated about its vertical axis with angular velocity Ω. The strength of buoyancy is reflected in the Rayleigh number (~ H 3 Δ) and that of the Coriolis force in the Ekman and Rossby numbers (~Ω -1 ). Rotation suppresses the convective onset, introduces instabilities, changes the velocity boundary layers, modifies the shape of thermal structures from plumes to vortical columns, affects the large-scale circulation, and can decrease or enhance global heat transport depending on buoyant and Coriolis forcing. RRBC is an extremely rich system, with features directly comparable to geophysical and astrophysical phenomena. Here we review RRBC studies, suggest a unifying heat transport scaling approach for the transition between rotation-dominated and buoyancy-dominated regimes in RRBC, and discuss non-Oberbeck–Boussinesq and centrifugal effects.

42 ENGINEERING↗
DOE OSTI2021

Exploring OpenSNAPI Use Cases and Evolving Requirements [Slides]

Emerging system architectures are rapidly transforming in order to meet shifting requirements. Motivated by expanding data volumes, energy efficiency concerns, and the omnipresent need to improve performance, architectures are increasingly adopting a data-centric approach. At the core of this concept is the goal of minimizing data motion and instead processing data in-situ to the greatest degree possible. Therefore, data-centric designs, in contrast to conventional CPU-centric models, typically distribute compute capabilities throughout the architecture. As part of this paradigm shift, a novel class of devices known as data processing units (DPUs), alongside CPUs and GPUs, are quickly forming a third pillar of data-centric systems. These devices, which include smart network adapters and switches, seek to offload computation on data at the network edge as well as in-flight within the network fabric. The Open Smart Network API (OpenSNAPI) project seeks to develop a unified API for DPU devices. In our previous talks, we introduced the OpenSNAPI project and detailed our investigations regarding the viability of offloading compute intensive kernels to BlueField DPUs. In contrast, in this talk we detail our efforts to offload application-level file I/O to the DPU. We also discuss plans and early efforts to explore in-network compute capabilities. Finally, we describe our observations with respect to the evolving design of OpenSNAPI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Astroparticle Physics at DUNE

The Deep Underground Neutrino Experiment (DUNE) will pursue a broad program on neutrino physics and astrophysics with a staged 40 kt LAr detector at the Sanford Underground Research Facility (SURF) 1300 km from Fermilab. The scientific goals include neutrino oscillations studies with a powerful neutrino beam from Fermilab leading to determining the neutrino mass ordering and the measurement of the CP violating phase. The underground location of the DUNE far detector will allow undertaking searches for new phenomena and the detection of astrophysical processes involving neutrino emission. The observation of the neutrino signal from a core-collapse supernova in the DUNE LAr TPCs will provide unique and unprecedented information on the mechanism of supernovae in addition to enabling the search for new physics. In addition, DUNE will search for proton decay in the range of proton lifetimes predicted by a wide range of GUT models by virtue of its high detection efficiency and low background rates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

An Ab Initio Molecular Dynamics Study of Key Thermodynamic Input Parameters for Computer Simulation of U-6Nb Solidification

The key to metallic fuel development is the fabrication of uranium metal and alloys into fuel forms. U-Nb alloys are one of the best candidates for a metallic fuel alloy with high-temperature strength sufficient to support the core, acceptable nuclear properties, good fabricability, and compatibility with usable coolant media. Melt processing has been a key component of the metallic fuel cycle, and process models require thermophysical parameters at elevated temperatures, particularly above the melting temperatures, regarding which experimental data are scarce, for accurate simulations and process development. By means of ab initio density-functional theory (DFT) quantum molecular dynamics (QMD), we have calculated the main thermophysical parameters—the density, thermal expansion coefficient, specific heat, thermal conductivity, melting temperature, latent heat of fusion, and viscosity—used in the modeling of the U-6 wt.% Nb alloy casting. The melting temperature of the U-6 wt.% Nb alloy at ambient pressure is obtained by means of QMD simulations using the Z-method. The ambient volume change and latent heat of melting of U-6 wt.% Nb are also derived from QMD simulations in conjunction with analytical fitting for the energy and pressure. The thermal conductivity for the solid U-Nb alloy is calculated from the semi-classical Boltzmann transport equation combined with an estimate of the electron relaxation time obtained from DFT simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A Fortran–Python interface for integrating machine learning parameterization into earth system models

Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models

Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface's modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Blood electrolytes and exercise in relation to temperature regulation in man.

It is shown that the body temperature rise during physical exercise is a regulated process and is not due to a failure of heat-dissipating mechanisms. Core and skin temperatures do not provide sufficient information to account for the control of sweating during exercise. Evidence is presented that suggests an association between equilibrium levels of rectal temperature and the osmotic concentration of the blood with essentially no influence from variations in plasma volume.-

Greenleaf, J. E.↗