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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The importance of recruitment and retention in Heliophysics: it’s not just a pipeline problem

A major obstacle in cultivating a robust Heliophysics (and broader scientific) community is the lack of diversity throughout science, technology, engineering, and mathematics (STEM) fields. For many years, this has been understood as a “leaky pipeline” analogy, in which predominately minority students initially interested in STEM gradually fall (or are pushed) out of the field on their way to a scientific research position. However, this ignores critical structural and policy issues which drive even later career Ph.D.s out of a career in Heliophysics. We identify here several systemic problems that inhibit many from participating fully in the Heliophysics community, including soft money pressure, lack of accessibility and equity, power imbalances, lack of accountability, friction in collaboration, and difficulties in forming mentorship bonds. We present several recommendations to empower research-supporting organizations to help create a culture of inclusion, openness, and innovative science.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Using Radiogenic Noble Gas Nuclides to Identify and Characterize Rock Fracturing

Abstract Fracture‐released radiogenic noble gas nuclides are used to identify locations and constrain the volume of new fracture creation during subsurface detonations. Real‐time, in situ noble gases and reactive gases were monitored using a field‐deployed mass spectrometer and automated sampling system in a multilevel borehole array. Released gases were measured after two different detonations having distinct energy, pressure, and gas volume characteristics. Explosive‐derived gases (N 2 O, CO 2 ) and excess radiogenic 4 He and 40 Ar above atmospheric background are used to identify locations of gas transport and new fracture creation after each detonation. Fracture‐released radiogenic 4 He is used to constrain the volume of newly created fractures with a model of helium release from fracturing. Explosive by‐product gas was observed in multiple locations both near and distal to the shot locations for both detonations. Radiogenic 4 He and 40 Ar release from rock damage was observed in locations near the detonation after the second, more powerful detonation. Observed 4 He response is consistent with a model of diffusive release from newly created fractures. Volume of new fractures estimated from the 4 He release ranges from 1 to 5 m 2 with apertures ranging from 0.1 to 1 m. Our results provide evidence that radiogenic noble gases released during fracture creation can be identified at the field scale in real time and used to identify timing and location of fracture creation during deformation events. This technique could be useful in subsurface science and engineering problems where the location and amount of newly created rock fracturing is of interest including fault rupture, mine safety, subsurface detonation monitoring and reservoir stimulation.

58 GEOSCIENCES↗
DOE OSTI2024

PhILMs: Collaboratory on Mathematics and Physics-Informed Learning Machines for Multiscale and Multiphysics Problems

The landscape of computational science and engineering is continually evolving, with the challenge of high-dimensional regression problems standing as a significant hurdle in numerous scientific endeavors. Addressing this challenge, our research, funded by this award, has led to the development of an innovative computational framework known as Probabilistic Partition of Unity Networks (PPOU-Nets). This initiative represents a collaborative effort to harness the potential of mathematics and physics-informed machine learning in tackling multiscale and multiphysics problems prevalent in high-dimensional spaces. Through this work, we have proposed a novel methodology that seamlessly integrates adaptive dimensionality reduction and a mixture of experts model, thereby facilitating a more efficient and accurate approximation of complex functions. This research effort has not only advanced the state of computational science but also opened new avenues for exploration in quantum computing and beyond. This report outlines the motivation, methodology, key findings, and implications of our work, underscoring our contributions to the broader scientific community and the potential pathways for future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Transforming ENERGY through Computational Excellence

Computational methods underpin advancing the science and engineering of energy efficiency, sustainable transportation, renewable power technologies, and developing a knowledge base to optimize energy systems. Researchers with access to enough computing, and the right type, can focus their ingenuity and creativity on addressing the energy challenges. NREL’s advanced computing influence spans several common themes across the Office of Energy Efficiency and Renewable Energy (EERE), including materials discovery, process modeling, fluid dynamics, resource mapping, and analysis of large-scale systems with real-time optimization.

advanced computing↗
DOE OSTI2024

Crystallization and assembly at interfaces: Celebrating the achievements of a vibrant research community

Crystallization is one of the cornerstones of modern materials science and engineering and plays a critical role in industries ranging from petroleum derivative manufacturing to microstructural engineering of structural materials and the defect-free growth of silicon single crystals for integrated chip technology. Also, in the realm of environmental and biological processes, the mineralization of diverse compounds has shaped the vast array of ecosystems we observe today. Conversely, understanding the crystallization and assembly of building blocks of various sizes at interfaces has broader impacts on materials synthesis, performance of energy storage devices, optimized processing conditions, and more.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Biotechnological solutions for critical mineral recovery from unconventional feedstocks

Secure and sustainable metal recovery from unconventional feedstocks is needed to meet the mineral demands of energy, defense, and electronic technologies. Here, we highlight the potential to leverage nature’s ability to extract and differentiate metal ions in biotechnologies that could become the next generation of mining and refining. We describe bulk and trace processes and then discuss the advances and opportunities of two key bioprocesses: microbially mediated solubilization of metal ions from solid matrices (termed ‘bioleaching’) and bio-based separation of solubilized ions via selective adsorption to proteins. Both biotechnologies have advantages such as reduced energy input for leaching low-grade feedstocks and reduced organic solvent demand for separating ions with similar physiochemical properties but require more development for industrial scale recovery from unconventional feedstocks. Innovation in biological science and engineering may bring timely solutions to key challenges toward recovering critical minerals from unconventional feedstocks.

organic↗
DOE OSTI2020

Recent advances in computational mathematics and applications

We are honored to bring you this special issue dedicated to recent advances in computational mathematics and applications in science and engineering. The papers in this special issue were presented in ”Conference on Computational Mathematics and Applications (CCMA)” held at the University of Nevada Las Vegas (UNLV) during October 25–27, 2019. The conference has attracted about 90 attendees from six countries. The 15 papers in the special issue comprise a diverse collection of theoretical numerical analysis as well as applications in various subjects. Topics cover optimal control of PDEs, discontinuous Galerkin methods, fluid flow in porous media, turbulence flow, static and moving interface problems, complex fluids composed by the mixture of Newtonian fluid and nematic (liquid crystal) flows, modeling of ocean–atmosphere system, image segmentation algorithm, uncertainty quantification problems for turbulence model and Maxwell’s equations, iterative solver for systems resulting from mixed methods, and novel methods for solving systems of nonlinear equations. As the Guest Editors we would like to thank Dr. Leland Jameson at NSF (National Science Foundation) for kindly supporting our conference proposal which made this conference possible. We would also like to thank Darren Sugrue from Elsevier who provided partial support for our conference, and Thennarasu Gunasekaran and his production team from Elsevier who produced this nice special issue. We are grateful to many people (Dr. Zhijian Wu, Lori Ornelas, Elsa Juarez, and many Ph.D. students) at the Department of Mathematical Sciences of UNLV who provided tremendous support for the conference. Finally, we very much appreciate all authors who contributed their precious results to this special issue.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Recent developments in photoresists for extreme-ultraviolet lithography

Here, this report describes recent developments and current needs in the field of high-resolution photopolymers and photomolecules briefly describing prior generation lithographic patterning materials. It subsequently concentrates on recent advances in both inorganic and polymeric materials for extreme ultraviolet (EUV) lithography. It is also part of a series of papers written in celebration of the centenary of the Polymeric Materials: Science and Engineering (PMSE) division of the American Chemical Society (ACS). PMSE has long been home to polymer chemists who have made important contributions to advances in semiconductor manufacturing as a result of PMSE's focus on polymer coatings research. While EUV lithography has been an area of research for several decades, only within the last 5 years has the combination of new light sources, EUV optics, tool advances and resist discoveries come together to transition this area from the patterning method of the future to a leading-edge manufacturing technology.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Neutron Interferometric methods and quantum sensing

Advances in material science and engineering make it possible to access artificial materials or ‘metamaterial’ properties and structures on the length scale comparable to the wavelength of ultracold neutrons ~ 100 nm. Strong neutron scattering effects such as Anderson localization, resonance scattering may be studied in the laboratory according to our recent theoretical studies. UCN interferometry and high-resolution spectroscopy (sub-pico-electronvolt resolution) in neutronic metamaterials are examples of new experimental possibilities that can probe quantum gravitational states of neutrons, and quantum sensing based on ultracold neutrons.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Radioisotope Science and Technology Division FY 2025 Core R&D Summary Report: Competitive Projects, Postdoctoral Researchers, and Student Interns

R&D efforts in support of the Oak Ridge National Laboratory (ORNL) Isotope Program Radioisotope Portfolio are led by the Radioisotope Science and Technology Division (RSTD). In addition to supporting the ORNL Isotope Program Radioisotope Portfolio, RSTD supports a portfolio of research related to fundamental properties of radioisotopes and radioisotope applications, including diagnostic and therapeutic uses of medical radioisotopes, radioisotopes for national security, and the production of 238 Pu for the National Aeronautics and Space Administration (NASA) and US Department of Energy (DOE) Office of Nuclear Energy. RSTD is organized into functional science and engineering groups, with most staff members supporting multiple programs. The goal of this organization is to enable synergy between programs such that R&D advances coming from other programs may provide benefit to the ORNL Isotope Program. R&D within RSTD is focused around addressing five grand challenges, as documented in the strategic plan for the DOE Office of Isotope R&D and Production, or DOE Isotope Program (IP), Radioisotope Production R&D activities at ORNL: 1. Maximizing the scientific output of radioisotope transmutation resources, 2. Maximizing the scientific output of radioisotope processing resources, 3. Minimizing waste and having optimal waste disposition, 4. Focusing on product quality and reliability, and 5. Expanding the use of beneficial isotopes. The ORNL Core R&D program, one of the primary R&D components within the ORNL Isotope Program Radioisotope Portfolio, ranges from benchtop to demonstration activities, with a focus on researching enhanced production techniques, developing emerging isotopes, and developing the talent pipeline for radioisotope science and technology. Projects within the Core R&D Program are led primarily by RSTD staff members. In supporting enhanced production techniques, the Core R&D program presents an opportunity to fund novel R&D that might not be tied to a specific radioisotope product but still presents a high potential for broad applicability in the longer term. In supporting the development of emerging isotopes, the Core R&D program develops high-priority isotopes that are not able to be fully supported through production funds.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Decentralized digital twins of complex dynamical systems

Abstract In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machine learning that promotes knowledge exchange without disclosing actual data. Clients can learn an aggregated model cooperatively using this method while maintaining complete client-specific training data. We use a variety of dynamical systems, which are frequently used as prototypes for simulating complex transport processes in spatiotemporal systems, to show the viability of the DDT framework. Our findings suggest that constructing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems may be made possible by federated machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The confluence of machine learning and multiscale simulations

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. In conclusion, this confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Science Applications of Phased Array Radars

Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near instantaneous sampling of the atmosphere with flexible beam forming, multi-functionality, low operational and maintenance costs, and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors including i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Atomic-Scale Computational and Experimental Investigation of Twinning Mechanisms in HCP Systems

One of the major goals of the Materials Science and Engineering community is to achieve a fundamental understanding of how alloying affects the mechanical properties of structural materials. Titanium (Ti) and its alloys are particularly in demand due to their relatively lightweight, high strength and corrosion resistance. The low-temperature ductility of hexagonal close-packed (hcp) Ti known as α- Ti is linked to twinning. The addition of alloying elements can affect the deformation mechanisms leading to the change in macro-scale mechanical properties. To understand these effects, our objective is to study and quantify the energetics and kinetics of substitutional and interstitial solutes on the deformation mechanisms of hcp alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Editorial: Quantum Information and Quantum Computing for Chemical Systems

Quantum computing has emerged as an exciting inter-disciplinary research topic that cuts across the traditional fields of physics, computer science, and engineering. It is a revolutionary model of computation that has offered new insights into methods for modeling and simulation of chemical systems. Applications of quantum computing to chemistry have demonstrated rapid progress on both theoretical and experimental fronts. Past theoretical efforts have shown how to adapt quantum computation to a variety of problems including electronic structure and molecular dynamics. In addition, the development of quantum algorithms for quantum chemistry has been stimulated by the greater availability of more capable quantum computing devices. Advances in the number and quality of qubits continues to enable remarkable proof-of-concept demonstrations working towards a milestone of quantum computational advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

An overview of visualization and visual analytics applications in water resources management

Recent advances in information, communication, and environmental monitoring technologies have increased the availability, spatiotemporal resolution, and quality of water-related data, thereby leading to the emergence of many innovative big data applications. Among these applications, visualization and visual analytics, also known as the visual computing techniques, empower the synergy of computational methods (e.g., machine learning and statistical models) with human reasoning to improve the understanding and solution toward complex science and engineering problems. These approaches are frequently integrated with geographic information systems and cyberinfrastructure to provide new opportunities and methods for enhancing water resources management. Here, we present a comprehensive review of recent hydroinformatics applications that employ visual computing techniques to (1) support complex data-driven research problems, and (2) support the communication and decision-makings in the water resources management sector. Then, we conduct a technical review of the state-of-the-art web-based visualization technologies and libraries to share our experiences on developing shareable, adaptive, and interactive visualizations and visual interfaces for water resources management applications. We close with a vision that applies the emerging visual computing technologies and paradigms to develop the next generation of hydroinformatics applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Seeing is believing: visualization of antiferromagnetic domains

Abstract Understanding and utilizing novel antiferromagnetic (AFM) materials has been recently one of the central issues in condensed matter physics, as well as in materials science and engineering. The relevant contemporary topics include multiferroicity, topological magnetism and AFM spintronics. The ability to image magnetic domains in AFM materials is of key importance for the success of these exciting fields. While imaging techniques of magnetic domains on the surfaces of ferro-(ferri)magnetic materials with, for example, magneto-optical Kerr microscopy and magnetic force microscopy have been available for a number of decades, AFM domain imaging is a relatively new development. We review various experimental techniques utilizing scanning, optical, and synchrotron X-ray probes to visualize AFM domains and domain walls, and to unveil their physical properties. We also discuss the existing challenges and opportunities in these techniques, especially with further increase of spatial and temporal resolution.

36 MATERIALS SCIENCE↗
DOE OSTI2023

MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

97 MATHEMATICS AND COMPUTING↗