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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Grid resolution and solution convergence for Mars Pathfinder forebody

As part of the Discovery Program, NASA Plans to launch a series of probes to Mars. The Mars Pathfinder project is the first of this series with a scheduled Mars arrival in July 1997. The entry vehicle will perform a direct entry into the atmosphere and deliver a lander to the surface. Predicting the entry vehicle's flight performance and designing the forebody heatshield requires knowledge of the expected aerothermodynamic environment. Much of this knowledge can be obtained through computational fluid dynamic (CFD) analysis.

Nettelhorst, Heather L.↗
NASA NTRS2000

Colloidal Gelation-2 and Colloidal Disorder-Order Transition-2 Investigations Conducted on STS-95

The Colloidal Gelation-2 (CGEL 2) and Colloidal Disorder-Order Transition-2 (CDOT 2) investigations flew on Space Shuttle Discovery mission STS-95 (also known as the John Glenn Mission). These investigations were part of a series of colloid experiments designed to help scientists answer fundamental science questions and reduce the trial and error involved in developing new and better materials. Industries dealing with semiconductors, electro-optics, ceramics, and composites are just a few that may benefit from this knowledge. The goal of the CGEL 2 investigation was to study the fundamental properties of colloids to help scientists better understand their nature and make them more useful for technology. Colloids consist of very small (submicron) particles suspended in a fluid. They play a critical role in the technology of this country, finding uses in materials ranging from paints and coatings to drugs, cosmetics, food, and drink. Although these products are routinely produced and used, there are still many aspects of their behavior about which scientists know little. Understanding their structures may allow scientists to manipulate the physical properties of colloids (a process called "colloidal engineering") to produce new materials and products. Colloid research may even improve the processing of known products to enhance their desirable properties.

Hoffmann, Monica T.↗
DOE OSTI2020

History of gradient advances in SRF

Radio frequency (RF) superconductivity has become a key technology for many modern particle accelerators. One of its most salient features of this technology is the ability of superconducting RF cavities to deliver high accelerating gradients in continuous-wave and long-pulse modes of operation. However, reaching the current state of the technology was not an easy fit. Over many years scientists and engineers had to overcome several serous performance limitations. In this paper, I attempt to the best of my knowledge to trace the history of accelerating gradients evolution in the field of superconducting radio frequency. I will restrict the scope to primary innovations along with some of the ensuing developments in developing cavities made of bulk niobium. But I will not cover all the many applications and findings over the subsequent decades of progress that were based on the primary discoveries and inventions. I will also not cover a number of other important topics in the history of cavity developments, such as the drive for higher Q values, or the push for lower cavity costs via Nb/Cu cavities or large grain Nb cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Thin film combinatorial sputtering of TaTiHfZr refractory compositionally complex alloys for rapid materials discovery

Many applications from advanced nuclear reactors to aerospace and automotive industries require materials to operate in extreme environments. In search of new materials that can operate in these extremes, the present work explores this space whereby: (1) guided by atomistic and thermodynamic calculations we utilize thin film combinatorial synthesis to rapidly explore mechanical and thermal properties in a broad range of refractory compositionally complex alloys, and (2) observe transformation induced plasticity via oscillations in the thin film nanoindentation load depth curves that are attributed to, (3) a stress-induced HCP-to-BCC phase transformation in the resulting nanogranular microstructure, which to our knowledge has not been observed before in this alloy system; and finally (4) scale to bulk materials to compare the thin film results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chemically distinct regions of Venus’s atmosphere revealed by measured N 2 concentrations

A defining characteristic of the planet Venus is its thick, CO 2 -dominated atmosphere. Despite over 50 years of robotic exploration of Venus, including thirteen successful atmospheric probes and landers, our knowledge of N 2 , the second most abundant compound in the atmosphere, is highly uncertain. We report the first measurement of the nitrogen content of Venus’s atmosphere at altitudes between 60 and 100 km. Our result, 5.0 ± 0.4 v% N 2 , is 40% higher than the value of 3.5 v% N 2 reported for the lower atmosphere (<50 km altitude). Our discovery of altitude-dependent variations in the N 2 content of the atmosphere defies early expectations of homogeneous composition below 100 km (for example, see ref. 1), and necessitates complete chemical modelling to investigate chemical versus physical explanations for the enhanced N 2 in the upper atmosphere. Furthermore, the existence of chemically distinct lower and upper atmosphere regions complicates the use of remote sensing measurements of the upper atmosphere to infer the properties of the lower atmosphere and surface, an important lesson that also extends to the growing field of exoplanet astronomy.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.

Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
NASA NTRS1995

LDEF contributions to cosmic ray and radiation environments research

LDEF-1 carried three experiments which are producing significant advances in our knowledge of ultra heavy and anomalous cosmic rays, solar flare particles, and heavy nuclei in the trapped belts. Nine other experiments made measurements on the radiation environments or performed dosmetric monitoring. Data from those experiments, and from measurements of induced radioactivity in LDEF components have significantly improved our knowledge of LEO radiation environment. Measurements at various locations shielding depths of radiation absorbed dose, linear energy transfer spectra, proton, neutron and heavy ion fluences, and induced radioactivity have been made, and many of these results have been compared to models. This has allowed the assessment of accuracy, and the potential for improvement, of the models. Serendipitous results from the radiation measurements include the discovery of atmospheric Be-7 plated on the front surface of LDEF, which has motivated a series of new investigations. A sample of measurements and modeling results will be presented, as well as the status of archiving the measurements and models.

Parnell, Thomas A.↗
NASA NTRS2007

Serving Fisheries and Ocean Metadata to Communities Around the World

NASA's Global Change Master Directory (GCMD) assists the oceanographic community in the discovery, access, and sharing of scientific data by serving on-line fisheries and ocean metadata to users around the globe. As of January 2006, the directory holds more than 16,300 Earth Science data descriptions and over 1,300 services descriptions. Of these, nearly 4,000 unique ocean-related metadata records are available to the public, with many having direct links to the data. In 2005, the GCMD averaged over 5 million hits a month, with nearly a half million unique hosts for the year. Through the GCMD portal (http://gcmd.nasa.gov/), users can search vast and growing quantities of data and services using controlled keywords, free-text searches, or a combination of both. Users may now refine a search based on topic, location, instrument, platform, project, data center, spatial and temporal coverage, and data resolution for selected datasets. The directory also offers data holders a means to advertise and search their data through customized portals, which are subset views of the directory. The discovery metadata standard used is the Directory Interchange Format (DIF), adopted in 1988. This format has evolved to accommodate other national and international standards such as FGDC and IS019115. Users can submit metadata through easy-to-use online and offline authoring tools. The directory, which also serves as the International Directory Network (IDN), has been providing its services and sharing its experience and knowledge of metadata at the international, national, regional, and local level for many years. Active partners include the Committee on Earth Observation Satellites (CEOS), federal agencies (such as NASA, NOAA, and USGS), international agencies (such as IOC/IODE, UN, and JAXA) and organizations (such as ESIP, IOOS/DMAC, GOSIC, GLOBEC, OBIS, and GoMODP).

Meaux, Melanie F.↗
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

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

A New Era of Discovery: The 2023 Long-Range Plan for Nuclear Science (V.1.2)

Nuclear science is the investigation of how protons and neutrons are formed from elementary particles and how the forces between those particles produce both nuclei and the vast variety of nuclear phenomena that occur in the universe. It has evolved into a broad field that addresses profound scientific questions: Where does the mass of visible matter come from? How do stars ignite, live, and die? How do nuclei illuminate the search for new laws of nature? This science points the way to using nuclei to build new technologies that benefit society. The 2015 Nobel Prize in physics was shared by nuclear physicists Art McDonald and Takaaki Kajita for the discovery of neutrino oscillations, which confirmed that neutrinos have mass. Our progress on big questions like this one since 2015 has been remarkable owing to new experimental tools, theoretical breakthroughs, powerful computational techniques, and the talented people who make these innovations possible. Focusing on these new tools, the Facility for Rare Isotope Beams (FRIB) at Michigan State University is already producing exciting results on decays of never-before-produced isotopes a year after it was completed on time and on budget. The energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) was also completed on schedule and on budget—new data from this facility are revealing the spectrum, structure, and dynamics of protons, neutrons, nuclei, and mesons. On the theory front, we can now calculate the distribution of quarks inside the proton from first principles. The implementation of artificial intelligence (AI) and machine learning (ML) techniques has led to improved data analysis and increased efficiency in running experiments and theoretical calculations. The impact of nuclear science goes beyond expanding the frontiers of knowledge about matter in the universe. We simultaneously develop a STEM work force that advances the security, technology, health, and wealth of our nation. Some connections are obvious. Expert scientists trained to work with radioactive nuclei are in demand in nuclear security arenas and are highly sought after by various government agencies and private industries. Graduate students and postdoctoral fellows (postdocs) obtain extensive computational, modeling, and data science skills that are similarly in high demand. Less obvious but equally important is the connection between these trained scientists and success in other professions, including medicine, energy, and entrepreneurial pursuits. The workforce that enables discovery in nuclear science also makes breakthroughs in technologies with tremendous impact on the nation’s economic advancement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Snowmass Computational Frontier: Topical Group Report on Quantum Computing

Quantum computing will play a pivotal role in the High Energy Physics (HEP) science program over the early parts of the 21$^{st}$ Century, both as a major expansion of our capabilities across the Computational Frontier, and in synthesis with quantum sensing and quantum networks. This report outlines how Quantum Information Science (QIS) and HEP are deeply intertwined endeavors that benefit enormously from a strong engagement together. Quantum computers do not represent a detour for HEP, rather they are set to become an integral part of our discovery toolkit. Problems ranging from simulating quantum field theories, to fully leveraging the most sensitive sensor suites for new particle searches, and even data analysis will run into limiting bottlenecks if constrained to our current computing paradigms. Easy access to quantum computers is needed to build a deeper understanding of these opportunities. In turn, HEP brings crucial expertise to the national quantum ecosystem in quantum domain knowledge, superconducting technology, cryogenic and fast microelectronics, and massive-scale project management. The role of quantum technologies across the entire economy is expected to grow rapidly over the next decade, so it is important to establish the role of HEP in the efforts surrounding QIS. Fully delivering on the promise of quantum technologies in the HEP science program requires robust support. It is important to both invest in the co-design opportunities afforded by the broader quantum computing ecosystem and leverage HEP strengths with the goal of designing quantum computers tailored to HEP science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Standard and Non-Standard Neutrino Oscillations at the NOvA Experiment

Initially believed to be massless, the discovery of neutrino oscillations proved that neutrinos have masses, a feature not anticipated by the Standard Model of Elementary Particles. Investigations of the existence of the so-called physics Beyond the Standard Model is a subject of active effort in the field of particle physics. Interactions of neutrinos with matter are well described by the Standard Model, however in the presence of new physics our knowledge and ability to measure the standard neutrino oscillation parameters may be affected. Non-Standard Interactions (NSI) may arise as a subleading effect to the neutrino oscillation framework by introducing additional parameters resulting from a generalized matter potential.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

CORE: A Global Aggregation Service for Open Access Papers

This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Electrochemistry of PUREX: R is for Reduction and Ion Transfer

The PUREX (Plutonium, Uranium, Reduction, EXtraction) process is the foundation of all industrial activities that involve the recycling of plutonium and uranium from used nuclear fuel. With over 70 years of research, engineering, and operations, it can be argued that little opportunity is left for further discovery and new fundamental understandings regarding PUREX, in general, and tri-n-butyl phosphate (TBP), in particular. Through use of an advanced electroanalytical approach, molecular-level insights regarding the back-extraction of Pu(IV) by reductive separation is reported. Studies of model PUREX systems, with lanthanide ions extracted into 20 % TBP in n-dodecane, are used to demonstrate the approach. These studies reveal knowledge pertinent to mass transfer aspects of the biphasic reductive stripping reaction central to the separation of U and Pu in PUREX. Two lanthanide ions—Ce(IV) and Yb(III)—were extracted from their concentrated solutions in aqueous 3 M HNO 3 to expressly provide electrically conducting third phases to facilitate electrochemical data acquisition. To simplify the diabolically-complicated reductive separation aspects of PUREX, the controlled polarization of electrode surfaces was used to reduce Ce(IV) and Yb(III) in place of chemical reductants. Electroanalyses of the biphasic systems with Ce(IV) and Yb(III) using three-phase electrode differential pulse voltammetry demonstrate the effects of mass transfer across water-oil interfaces on the electrode potentials for the reduction to Ce(III) and Yb(II). These reductions trigger nitrate transfer out of the oil phase for Ce and proton transfer into the organic phase for Yb. This is in correlation with the transfer of the charge-neutral ion pairs Ce·3NO 3 and Yb·2NO 3 out of the oil phases. Here, the differences in ion transfer steps reflect significant variations in the equilibrium speciation of multinuclear Ce(IV) and mononuclear Yb(III) solvates of TBP in the oil phases and the loading of the organic phase.

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

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗