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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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Electromagnetic Theory with Practical Applications
Book written by Mary O'Neill on electromagnetic theory for practical applications - for use by students of the author and future NNSS employees in the author's field.
Electromagnetic Theory with Practical Applications
Book written by Mary O'Neill on electromagnetic theory for practical applications for use by students of the author and future NNSS employees in author's field.
Elevating the Practical Application of Sodium-Ion Batteries through Advanced Characterization Studies on Cathodes
Sodium-ion batteries (SIBs) have emerged as promising alternatives to their lithium-ion counterparts due to the abundance of sodium resources and their potential for cost-effective energy storage solutions. The chemistry for SIBs has been investigated since the 1980s, but it went through a slow research and development process. Recently, there has been an acceleration in technology maturation due to a supply chain crisis originating from unequal resource distribution and sustainability and safety concerns regarding lithium-ion batteries. However, the practical application of SIBs has been hindered primarily by challenges related to cathode materials, specifically, surface and structural stabilities in different conditions. Through the integration of advanced techniques such as in situ spectroscopy, operando diffraction, and high-resolution microscopy, a comprehensive understanding of the cathode’s dynamic behavior and degradation mechanisms can be achieved. The identified structural modifications, phase transitions, and degradation pathways offer critical insights into the design of robust cathode materials with prolonged cycling stability, fast charging capability, high energy density, great low-temperature performance, and safety. This review underscores the pivotal role of cutting-edge characterization techniques in guiding the development of high-performance sodium-ion batteries, thereby fostering the realization of sustainable and efficient energy storage solutions for diverse technological applications.
A fast and accurate modeling approach for water and steam thermodynamics with practical applications in district heating system simulation
In U.S. district heating (DH) systems, steam is the most common heat transport medium. Industry demand for new advanced modeling capabilities of complete steam DH systems is increasing; however, the existing models for water/steam thermodynamics are too slow for large system simulations because of computationally expensive algebraic loops that require the solution to nonlinear systems of equations. For practical applications, this work presents a novel split-medium approach that implements numerically efficient liquid water models alongside various water/steam models, breaking costly algebraic loops by decoupling mass and energy balance equations. New component models for steam DH systems are also presented. We implemented the models in the equation based Modelica language and evaluated accuracy and computing speed across multiple scales: from fundamental thermodynamic properties to complete districts featuring 10 to 200 buildings. Compared to district models with the IF97 water/steam model and equipment models from the Modelica Standard Library, the new implementation improves the scaling rate for large districts from cubic to quadratic with negligible compromise to accuracy. Additionally, for an annual simulation with 180 buildings, this translates to a computing time reduction from 33 to 1-1.5 h. These results are critically important for industry practitioners to simulate steam DH systems at large scales.
FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale ® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.
Connecting Minds: AI Use Cases to Bridge Power Systems and Large Language Models for Practical Applications
Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.
Impact of experimental effects on a resolved resonance evaluation for practical applications
Not Available
On the use of oscillating jet flames in a coflow to develop soot models for practical applications
Not Available
Aberration-corrected STEM imaging of 2D materials: Artifacts and practical applications of threefold astigmatism
High-resolution scanning transmission electron microscopy (HR-STEM) with spherical aberration correction enables researchers to peer into two-dimensional (2D) materials and correlate the material properties with those of single atoms. The maximum intensity of corrected electron beam is confined in the area having sub-angstrom size. Meanwhile, the residual threefold astigmatism of the electron probe implies a triangular shape distribution of the intensity, whereas its tails overlap and thus interact with several atomic species simultaneously. The result is the resonant modulation of contrast that interferes the determination of phase transition of 2D materials. Here, we theoretically reveal and experimentally determine the origin of resonant modulation of contrast and its unintended impact on violating the power-law dependence of contrast on coordination modes between transition metal and chalcogenide atoms. The finding illuminates the correlation between atomic contrast, spatially inequivalent chalcogenide orientation, and residual threefold astigmatism on determining the atomic structure of emerging 2D materials.
A Practical Application of Global Sensitivity Analysis for Stochastic Epidemiology Models in Support of Policy Decisions.
Abstract not provided.
Nuclear Data: From Measurement, Evaluation, Processing to Practical Applications [Slides]
Any analysis, design of a nuclear system will be as good as the knowledge of the nuclear data used whether differential and integral. ORNL is engaged in generating high level nuclear data for reactor, shielding, and safety applications. Nuclear data remains a major source of data uncertainties. Uncertainties on nuclear data rely upon measurements and evaluations. Tight connections with experimental facilities around the world have helped ORNL to address issues on ND and uncertainty. It is extremely important for ORNL to retain expertise on nuclear data in support of the research and expertise activities.
Unlocking the Power of Large Language Models: Practical Applications in Scripts and Programs
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Nuclear Data, Evaluation and Practical Applications [Slides]
Abstract not provided.
The Practical Application of Ergonomics in Glovebox Work
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Practical applications of machine-learned flows on gauge fields
Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question how flows can improve lattice QCD at state-of-the-art scales. We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling, aimed at improving topological mixing, which are viable with iterative improvements upon presently available flows.