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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantum computing in power systems

Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today's power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The Influence of Shallow Subsurface Properties on Particle Motion in Acoustic-Seismic Coupling

Atmospheric acoustic waves transmit energy into the solid Earth through air-to-ground coupling. These waves are recorded by seismic sensors and provide insight into both atmospheric phenomena and subsurface properties. Interpreting these signals is often challenging because they are modulated by subsurface structure and the incidence angle of the acoustic wave. This study examines acoustic--seismic coupling generated by the 2012 Camp Minden Explosion, which was recorded by hundreds of seismoacoustic stations. We apply a novel technique to quantify the seismic particle motion, model coupled waves with a propagator matrix approach, and apply a Bayesian inversion to infer properties of the shallow subsurface. Our analysis reveals that prograde motion is widespread and focused in low shear-wave velocity regions, such as the Mississippi Embayment, and retrograde motion is more common in higher shear-velocity areas. Inversion results at some stations produce plausible subsurface models with strong waveform fits, while inversions at other sites are less successful. These results indicate prograde particle motion in air-to-ground coupled waves is more prevalent than previously recognized and may serve as a diagnostic for shallow velocity structure. Our comprehensive modeling and inversion framework provides a potential method to extract layered near-surface properties from acoustic-seismic coupling observations.

58 GEOSCIENCES↗
DOE OSTI2022

Dehalogenation reactions between halide salts and phosphate compounds

Reactions between phosphoric acid [H 3 PO 4 ] or ammonium hydrogen phosphates [i.e., NH 4 H 2 PO 4 , (NH 4 ) 2 HPO 4 ] and halide salts can be used to dehalogenate (remove halides from) salt-based waste streams, where the process of removing halides yields products that have more efficient disposal pathways for repository storage. In this context, the term efficiency is defined as higher waste loadings and simplified immobilization processes with potential for recycle of certain salt components (e.g., 37 Cl as H 37 Cl or NH 4 37 Cl). The main streams identified for these processes are nuclear wastes generated during electrochemical reprocessing of used nuclear fuel as well as used halide salts from molten salt reactor operation. The potential byproducts of these reactions are fairly consistent across the range of halide species (i.e., F, Cl, Br, I) where the most common are hydrogen halides [e.g., HCl (g) ] or ammonium halides (e.g., NH 4 Cl). However, trihalide compounds (e.g., NCl 3 ), nitrogen triiodide ammine adducts [NI 3 ·(NH 3 ) x ], and ammonium triiodide (NH 4 I 3 ) are also possible. Several of these byproducts (i.e., NCl 3 , NBr 3 , NI 3 , and NH 4 I 3 ) are shock-sensitive contact explosives so their production in these processes must be tracked and carefully controlled, which includes methods of immediate neutralization upon production such as direct transport to a caustic scrubber for dissolution. Several benefits arise from utilizing H 3 PO 4 as the phosphate additive during dehalogenation reactions for making iron phosphate waste forms including more oxidized iron (higher Fe 3+ :Fe 2+ ratios), higher chemical durabilities, and the avoidance of trihalides, but the byproducts are hydrogen halides, which are corrosive and require special handling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Video frame prediction of microbial growth with a recurrent neural network

The recent explosion of interest and advances in machine learning technologies has opened the door to new analytical capabilities in microbiology. Using experimental data such as images or videos, machine learning, in particular deep learning with neural networks, can be harnessed to provide insights and predictions for microbial populations. This paper presents such an application in which a Recurrent Neural Network (RNN) was used to perform prediction of microbial growth for a population of two Pseudomonas aeruginosa mutants. The RNN was trained on videos that were acquired previously using fluorescence microscopy and microfluidics. Of the 20 frames that make up each video, 10 were used as inputs to the network which outputs a prediction for the next 10 frames of the video. The accuracy of the network was evaluated by comparing the predicted frames to the original frames, as well as population curves and the number and size of individual colonies extracted from these frames. Overall, the growth predictions are found to be accurate in metrics such as image comparison, colony size, and total population. Yet, limitations exist due to the scarcity of available and comparable data in the literature, indicating a need for more studies. Both the successes and challenges of our approach are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Orbital Selectivity in Electron Correlations and Superconducting Pairing of Iron-Based Superconductors

Electron correlations play a central role in iron-based superconductors. In these systems, multiple Fe 3 d -orbitals are active in the low-energy physics, and they are not all degenerate. For these reasons, the role of orbital-selective correlations has been an active topic in the study of the iron-based systems. In this article, we survey the recent developments on the subject. For the normal state, we emphasize the orbital-selective Mott physics that has been extensively studied, especially in the iron chalcogenides, in the case of electron filling n ∼ 6 . In addition, the interplay between orbital selectivity and electronic nematicity is addressed. For the superconducting state, we summarize the initial ideas for orbital-selective pairing and discuss the recent explosive activities along this direction. We close with some perspectives on several emerging topics. These include the evolution of the orbital-selective correlations, magnetic and nematic orders, and superconductivity as the electron filling factor is reduced from 6 to 5, as well as the interplay between electron correlations and topological band structure in iron-based superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8,000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Direct reactions for astrophysical p-capture rates with ORRUBA and GODDESS

Understanding the nucleosynthesis and energy generation in quiescent and explosive stellar burning requires a detailed understanding of reaction rates on many unstable nuclides. Such reaction rates are often governed by the properties of low-lying, isolated proton resonances. Though direct measurements of resonance strengths are ultimately desired, and are a focus of rare isotope beam facilities worldwide, such tour-de-force experiments must be guided by indirect techniques, in order to know resonance energies, J π assignments, and estimated widths, to inform targeted measurements. Furthermore, some important low-lying resonances may be too weak for direct measurements with radioactive beams, and indirect techniques provide the only practical constraints. Additionally, there has been growing interest in the astrophysical role of isomeric states, which can influence the reaction flow in nucleosynthetic reaction networks, and hence impact the quantitative interpretation of astronomical observables, such as γ-ray signatures, and elemental and isotopic ratios. Properties of single-proton resonances can be obtained by exploiting the selectivity of direct reactions, such as single-nucleon transfer and charge-exchange reactions. Constraining proton-capture rates via direct reactions has been a focus of the astrophysics program at ORNL for over two decades, spurring the development of the ORRUBA and GODDESS detector systems. Herein, a review of recent developments in instrumentation and radioactive beam delivery (including isomeric beam experiments) is presented, along with some specific examples of astrophysically interesting sd-shell nuclides, which have been a target of recent ORRUBA and GODDESS experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Multi-modal dynamic radiography using short-pulse laser-generated probe beams

Radiography is an important tool for the interrogation of dynamic experiments in the fields of dynamic properties of materials, and in condensed matter, high explosive, and high-energy-density physics. Multi-modal radiography advances the hypothesis that combining the information delivered by multiple radiographic modalities can lead to more constrained (improved) “reconstruction” of the scene than can be obtained from a single probe. We identify four modalities: multi-probe, time sequence, multi-view, and multi-messenger. Multi-probe radiography is a promising candidate for a next-generation dynamic radiographic facility. High-energy X-rays are the most frequently used probe for dynamic radiography, although recent developments show the utility of proton (pRad), electron (eRad), and neutron probe beams. Because each probing species interacts with material in the radiographic scene through quantitatively different mechanisms, each returns independent information about the scene, which can add extra constraints to the reconstruction process. How to conduct detailed, quantitative “co-analysis” of multiple data streams remains an area of active research. Multi-beam, short-pulse, laser-generated probes offer sufficient dose, an appropriate spectrum, and appropriate spatio-temporal resolution to produce high-quality dynamic radiographs. This paper reports on technology development to advance the state of the art of multi-modal/multi-probe radiography and the pursuit of both deterministic and inferential (AI/ML assisted) co-analysis methodologies to produce more constrained reconstructions from multi-modal data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Association of Mesoscale Auroral Structures and Breakups With Energetic Particle Injections at Geosynchronous Orbit

Geomagnetic substorms are associated with characteristic energetic particle injection signatures at geosynchronous orbit that are often dispersionless in both electrons and ions near the magnetic local time sector of auroral onset locations and are dispersed farther away from this region. Although the precise mechanism responsible for the coherent injection signatures at geosynchronous orbit have been the topic on considerable ongoing debate for decades, recent work on bursty bulk flows (BBFs) in the tail have led to the hypothesis that they may be the result of multiple, overlapping flow bursts penetrating into the inner magnetosphere from more distant downtail reconnection sites. Since auroral streamers are thought to be ionospheric signatures of BBFs in the tail, they can be used as proxies for testing this hypothesis. Using high resolution auroral imagery from the POLAR/VIS instrument combined with multi-spacecraft observations of energetic particle injections at geosynchronous orbit, we examine the association of mesoscale auroral structures with particle injection signatures over many hours during the 9 November 1998 storm. We find that the explosive types of auroral activations, such as pseudo-breakups and substorm onset breakups, are associated with the more intense and well-defined dispersed injection signatures, while intervals of isolated streamer activity appear to be associated with smaller dispersed “injectionlet” signatures. Furthermore, intervals of sustained, intense, and late expansion phase/recovery phase streamer activity appear to be associated with sustained elevated dispersed particle fluxes. These results are consistent with the hypothesis that it is the overlapping effects of sustained, intense multiple flow bursts penetrating toward the Earth that result in classical substorm particle injection signatures at geosynchronous orbit. However, it is also suggested that torches/omega-band tongues are the prime fate of braking isolated flow bursts (streamers) rather than the development of breakups, bulges, and substorm current wedge formation. A statistical analysis is presented showing that 93% of the observed torches evolved from streamers, 93% of streamers arriving in the equatorward regions of the bulge led to torches, 10.5% of such streamers led to breakups (either pseudo-breakups or substorm onsets), and only 3.5% of such streamers led to substorm onsets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The saturation mechanism of thermal instability

The literature on thermal instability (TI) reveals that even for a simple homogeneous plasma, the nonlinear outcome ranges from a gentle reconfiguration of the initial state to an explosive one, depending on whether the condensations that form evolve in an isobaric or nonisobaric manner. After summarizing the recent developments on the linear and nonlinear theory of TI, here we derive several general identities from the evolution equation for entropy that reveal the mechanism by which TI saturates; whenever the boundary of the instability region (the Balbus contour) is crossed, a dynamical change is triggered that causes the comoving time derivative of the pressure to change the sign. This event implies that the gas pressure force reverses direction, slowing the continued growth of condensation. For isobaric evolution, this “pressure reversal” occurs nearly simultaneously for every fluid element in condensation and a steady state is quickly reached. For nonisobaric evolution, the condensation is no longer in mechanical equilibrium and the contracting gas rebounds with greater force during the expansion phase that accompanies the gas reaching the equilibrium curve. The cloud then pulsates because the return to mechanical equilibrium becomes wave mediated. We show that both the contraction rebound event and subsequent pulsation behavior follow analytically from an analysis of the new identities. Our analysis also leads to the identification of an isochoric TI zone and makes it clear that unless this zone intersects the equilibrium curve, isochoric modes can only become unstable if the plasma is in a state of thermal non-equilibrium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Nuclear data resources and initiatives for nuclear astrophysics

Research into the cosmic synthesis of the elements, the evolution and explosion of stars, the nature of the early Universe, and other important topics in nuclear astrophysics are at the forefront of nuclear science. These studies are motivating laboratory measurements and theoretical calculations that, after significant investments, are pushing the boundaries of what is possible. The latest nuclear results, however, must be specially prepared before they can be used to advance our knowledge of the cosmos. This processing requires a set of resources unique to nuclear astrophysics, and an impressive collection of nuclear reaction and nuclear structure datasets, processing codes, thermonuclear reaction rate libraries, and simulation codes and services have been developed for the field. There are, however, some serious challenges to these efforts that will only worsen in the future, making it important to develop strategies and act now to ensure a sustainable future for this work. After detailing the specific data types needed for nuclear astrophysics and the available data resources, the major challenges in this work and their implications are discussed. A set of initiatives are proposed to meet those challenges along with suggested implementations and possible ways that they may advance our understanding of the Universe and strengthen the field of nuclear astrophysics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Machine learning opportunities for nucleosynthesis studies

Nuclear astrophysics is an interdisciplinary field focused on exploring the impact of nuclear physics on the evolution and explosions of stars and the cosmic creation of the elements. While researchers in astrophysics and in nuclear physics are separately using machine learning approaches to advance studies in their fields, there is currently little use of machine learning in nuclear astrophysics. We briefly describe the most common types of machine learning algorithms, and then detail their numerous possible uses to advance nuclear astrophysics, with a focus on simulation-based nucleosynthesis studies. We show that machine learning offers novel, complementary, creative approaches to address many important nucleosynthesis puzzles, with the potential to initiate a new frontier in nuclear astrophysics research.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The Anthropology of Aquaculture

Aquaculture is nothing new. It has a long, fascinating history that stretches from antiquity at least 8,000 years ago. What is new is the evolution of aquaculture in modern times into highly intensive monocultures which arose in the 1970–1980's. Modern aquaculture production has grown worldwide but remains concentrated in Asia due to the: (1) increased demands for aquatic foods as explosive population growth occurred in coastal cities with increasing affluence, (2) expansion of scientific and engineering breakthroughs, (3) high export values of aquatic foods, and (4) sharp decline of costs of global to local transport/shipping. The pioneering anthropologist Claude Levi-Strauss brought the idea of “structuralism” to anthropology: the concept that societies throughout history followed universal patterns of behavior. A qualitative document analysis of the key anthropological literature to assess aquaculture developments from antiquity to the beginning of the modern era was conducted to evaluate if there was adequate evidence to support a theory of anthropological “structuralism” for aquaculture in human history. Seven case studies of the cultural/environmental history of aquaculture were reviewed in diverse parts of the world (China, Australia, Egypt, Europe, South America, Canada/USA, Hawai'i). Analysis supports the structural theory that whenever the demands of aquatic/seafood-eating peoples exceeded the abilities of their indigenous fishery ecosystems to provide for them, they developed aquaculture. Modern aquaculture concepts and new communities of practice in “restoration aquaculture” have beginnings in Indigenous anthropology and archeology in aquaculture and point the way for Indigenous nations to engage as leaders of the United Nations Food and Agriculture Organization (FAO) ecosystem approach to aquaculture worldwide. Bringing ancient knowledge of Indigenous aquaculture into the modern context is an essential part of an alternative, “radical transformation” of modern aquaculture. There is an urgent need to develop and promote locally designed and culturally appropriate aquaculture systems that fit into the livelihoods of communities as part of a larger, diverse portfolio of food security.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Thermodynamics of Liquid Immiscibility in Iron-Silicate Melt Systems: A Study of Nuclear Fallout Glass

In a ground-interacting nuclear explosion, elements derived from environmental and anthropogenic material, such as iron, silicon, and aluminum, can be incorporated into the fireball. When significant amounts of metals are entrained, the resulting melt may display immiscible textures. The composition of these textures is a record of the temperature of formation and cooling rates (or thermodynamic stability) of the melts and can provide unique constraints on the early cooling conditions of these events. Here, a thermodynamic approach using calculated phase diagrams, the CALPHAD method, is used to predict temperature and composition ranges where stable liquid immiscibility might result in the textures observed in nuclear fallout glass. Sensitivity of the immiscibility to the presence of relative Al, Ca, and Mg content is also explored and compared to fallout samples, and partition coefficients are introduced to understand the preferred distribution of components into each liquid phase.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Large Eddy Simulations of Turbulent and Buoyant Flows in Urban and Complex Terrain Areas Using the Aeolus Model

Fast and accurate predictions of the flow and transport of materials in urban and complex terrain areas are challenging because of the heterogeneity of buildings and land features of different shapes and sizes connected by canyons and channels, which results in complex patterns of turbulence that can enhance material concentrations in certain regions. To address this challenge, we have developed an efficient three-dimensional computational fluid dynamics (CFD) code called Aeolus that is based on first principles for predicting transport and dispersion of materials in complex terrain and urban areas. The model can be run in a very efficient Reynolds average Navier–Stokes (RANS) mode or a detailed large eddy simulation (LES) mode. The RANS version of Aeolus was previously validated against field data for tracer gas and radiological dispersal releases. As a part of this work, we have validated the Aeolus model in LES mode against two different sets of data: (1) turbulence quantities measured in complex terrain at Askervein Hill; and (2) wind and tracer data from the Joint Urban 2003 field campaign for urban topography. As a third set-up, we have applied Aeolus to simulate cloud rise dynamics for buoyant plumes from high-temperature explosions. For all three cases, Aeolus LES predictions compare well to observations and other models. These results indicate that Aeolus LES can be used to accurately simulate turbulent flow and transport for a wide range of applications and scales.

42 ENGINEERING↗
DOE OSTI2019

Computational Strategies for Scalable Genomics Analysis

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Radiolytic Gas Production from Aluminum Coupons (Alloy 1100 and 6061) in Helium Environments—Assessing the Extended Storage of Aluminum Clad Spent Nuclear Fuel

Corrosion of aluminium alloy clad nuclear fuel, during reactor operation and under subsequent wet storage conditions, promotes the formation of aluminium hydroxide and oxyhydroxide layers. These hydrated mineral phases and the chemisorbed and physisorbed waters on their surfaces are susceptible to radiation-induced processes that yield molecular hydrogen gas (H 2 ), which has the potential to complicate the long-term storage and disposal of aluminium clad nuclear fuel through flammable and explosive gas mixture formation, alloy embrittlement, and pressurization. Here, we present a systematic study of the radiolytic formation of H 2 from aluminium alloy 1100 (AA1100) and 6061 (AA6061) coupons in “dry” (~0% relative humidity) and “wet” (50% relative humidity) helium environments. Cobalt-60 gamma irradiation of both aluminium alloy types promoted the formation of H 2 , which increased linearly up to ~2 MGy, and afforded G-values of 1.1 ± 0.1 and 2.9 ± 0.1 for “dry” and “wet” AA1100, and 2.7 ± 0.1 and 1.7 ± 0.1 for “dry” and “wet” AA6061. The negative correlation of H 2 production with relative humidity for AA6061 is in stark contrast to AA1100 and is attributed to differences in the extent of corrosion and varying amounts of adsorbed water in the two alloys, as characterized using optical profilometry, scanning electron microscopy, Raman spectroscopy, and X-ray diffraction techniques.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗