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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 OSTI2022

Zirate INL Welcome

Overview of INL and all the capabilities at the lab.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Turbulence theories and statistical closure approaches

When discussing research in physics and in science more generally, it is common to ascribe equal importance to the three components of the scientific trinity: theoretical, experimental, and computational studies. This review will explore the future of modern turbulence theory by tracing its history, which began in earnest with Kolmogorov’s 1941 analysis of turbulence cascade and inertial range [A.N. Kolmogorov, Dokl. Akad. Nauk SSSR, 30, 299, (1941); 32, 19, (1941)]. The 80th Anniversary of Kolmogorov’s landmark study is a welcome opportunity to survey the achievements and evaluate the future of the theoretical approach of turbulence research. Over the years, turbulence theories have been critically important in laying the foundation of our understanding of the nature of turbulent flows. In particular, the Direct Interaction Approximation (DIA) [R.H. Kraichnan, J. Fluid Mech., 5, 497 (1959)] and its subsequent development, known as the statistical closure approach, can be identified as perhaps the most profound single advancement. The remarkable success of the statistical closure has furnished a platform to study such essential concepts as the energy transfer process and interacting scales, and the roles of the straining and sweeping motions. More recently, the quasi-Lagrangian formulation of V. L’vov & I. Procaccia and Kraichnan’s solvable passive scalar model provided powerful ways to explore another fundamental aspect of turbulent flows, the phenomena of intermittency, and the associated anomalous scaling exponents. In the meantime, the theory of fluid equilibria has been developed to describe the large-scale structures that can emerge from turbulent cascades of two-dimensional and geophysical flows at a later time. And yet, despite all these successes, analytical treatments suffer from mathematical complexities. As a result, the utility of theoretical approaches has been limited to relatively idealized flows. On the other hand, in recent decades, computational abilities and experimental facilities have reached an unprecedented scale. Looking beyond the horizon, the imminent deployment of exascale supercomputers will generate complete datasets of the entire flow field of key benchmark flows, allowing researchers to extract additional measurements concerning fully developed, complex turbulent flow fields far beyond those available from the statistical closure theories. Some other developments that could potentially influence the future course of turbulence theories include the advancement of machine learning, artificial intelligence, and data science; likely disruptions arising from the advent of quantum computation; and the increasingly prominent role of turbulence research in providing more accurate climate scientific data. Finally, turbulence theorists can leverage these developments by asking the right questions and developing advanced, sophisticated frameworks that will be able to predict and correlate vast amounts of data from the other two components of the trinity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Design and Synthesis of Annulated Benzothiadiazoles via Dithiolate Formation for Ambipolar Organic Semiconductors

Substituted 2,1,3-benzothiadiazole ( BTD ) is a widely used electron acceptor unit for functional organic semiconductors. Difluorination or annulation on the 5,6-position of the benzene ring is among the most adapted chemical modifications to tune the electronic properties, though each sees its own limitations in regulating the frontier orbital levels. Herein, a hitherto unreported 5,6-annulated BTD acceptor, denoted as ssBTD , is designed and synthesized by incorporating an electron-withdrawing 2-(1,3-dithiol-2-ylidene)malononitrile moiety via aromatic nucleophilic substitution of the 5,6-difluoroBTD ( ffBTD ) precursor. Unlike the other reported BTD annulation strategies, this modification leads to the simultaneous decrease in both frontier orbital energies, a welcoming feature for photovoltaic applications. Incorporation of ssBTD into conjugated polymers results in materials boasting broad light absorption, dramatic solvatochromic and thermochromic responses (>100 nm shift and a band gap difference of ~0.28 eV), and improved crystallinity in the solid state. Such physical properties are in accordance with the combined electron-withdrawing effect and significantly increased polarity associated with the ssBTD unit, as revealed by detailed theoretical studies. Furthermore, the thiolated ssBTD imbues the polymer with ambipolar charge transport property, in contrast to the ffBTD -based polymer, which transports holes only. While the low mobilities (10 -4 to 10 -5 cm 2 V -1 s -1 ) could be further optimized, detailed studies validate that the thioannulated BTD is a versatile electron-accepting unit for the design of functional stimuli-responsive optoelectronic materials.

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

nmrrr : A Reproducible Workflow for Binning and Visualizing NMR Spectra From Environmental Samples

Nuclear magnetic resonance (NMR) spectroscopy is a useful tool for detection and identification of molecular structural information, with increasing applications in environmental sciences. NMR instrument outputs are however heterogeneous and require extensive post-processing, creating barriers to their use and application by non-specialists. Here, we report on a new open-source R package, nmrrr, that processes and visualizes spectral data obtained from one-dimensional solution-state and solid-state NMR experiments; the package also performs relevant calculations commonly applied in natural organic matter communities, such as computing the relative abundance of various functional groups. We document the package's installation, dependencies, and functions; and provide a standard workflow for processing NMR data. This package is currently available on CRAN and GitHub, and community contributions are welcome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. In this perspective, we outline how the adoption of key Open Science principles—open data, open source and open methods—is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Early Career Paths at Los Alamos National Laboratory

We represent a selection of early career staff at LANL with backgrounds in mathematics. There are many possible paths to a position at LANL. The lab has very active graduate research and postdoctoral programs, and it is not uncommon for graduate students to become postdocs, who can then be “converted” to staff. Conversion from being a postdoc is not the only route to a position at LANL, nor is it a guarantee, but most of us did postdocs here before becoming staff. Being a postdoc here also gives you an idea of whether you’d like living in northern New Mexico, a rugged, beautiful, and somewhat remote area. It is important to say here that citizenship is not a requirement for many of the positions at the lab. The lab historically and currently welcomes investigators from all over the world. LANL offers a broad spectrum of mathematical challenges that are largely problem driven. Particularly to those who feel that academic mathematics can be stove-piped, with a focus on deep but not necessarily broad expertise, the LANL environment gives the flexibility to move and grow in many different directions. The lab encourages a team-based approach to problems, which not only enhances the breadth of our approach to solving problems, but also gives team members access to new fields. From the outside (academia), however, it can be difficult to see how fields map between academia and the lab. This is one of the reasons that having interaction with the lab as a graduate student or postdoc can be helpful in understanding if the environment would be a good match for you. An important advantage of working at the lab is our high visibility both in academia, as well as in industry and the government. Because of its mission-driven projects, LANL, and more broadly the Department of Energy Laboratories, help set research directions that are picked up by researchers in academia. For this reason, many lab staff have worked at LANL for a number of years, then have transferred into academia after having set the agenda for their field of expertise. Below, we will share some of our experiences, opinions, and reflections in order to give the reader a feel for our own early career experiences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Survival estimates across five life stages of redfin ( Perca fluviatilis ) exposed to simulated pumped-storage hydropower stressors

The global prevalence of pumped-storage hydropower (PSH) is expected to grow exponentially as countries transition to renewable energy sources. Compared to conventional hydropower, little is currently known regarding PSH impacts on aquatic biota. This study estimated the survival of five life stages (egg, two larval stages, juvenile and adult) of redfin (European) perch (Perca fluviatilis) following passage through a PSH facility during the pumping phase. This was achieved by simulating the individual stressors expected to occur during passage through a 2000-MW PSH facility using laboratory-simulated (shear strain and extreme compression) and modelling (blade strike, BS) approaches. Our results indicate that redfin could survive the shear, pressure and BS stressors expected within the PSH facility, but impacts varied among life stages. Juvenile survival was >70% across all shear strain rates, while the survival of eggs and larvae declined markedly as strain rate increased. All life stages had high survival when exposed to rapid compression and BS. The high survival of redfin to the stressors tested suggests the PSH facility could facilitate the passage of redfin during the pumping phase from the lower to the higher elevation reservoir. This outcome would be welcomed in situations where the species is native, but could have adverse implications for the conservation of native biota where the species is considered a pest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep-learning-based image registration for nano-resolution tomographic reconstruction

Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. We report the method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.

deep learning↗