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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Source Attribution of Methane Enhancements at the Southern Great Plains Atmospheric Radiation Measurement Site Field Campaign Report

Tower data from 2017 to 2020 revealed that the U.S. Department of Energy’s Southern Great Plains (SGP) Atmospheric Radiation Measurements (ARM) observatory in Oklahoma exhibited significant CH 4 enhancements compared to other U.S. tower sites. On average, near-surface CH 4 at the ARM site was higher, had larger diurnal and seasonal variations, and had sharper near-surface increases at night compared to other tower sites in the U.S. A field campaign was conducted in June 2024 to identify and quantify the surrounding CH 4 emission sources by two researchers from the University of Oklahoma. During the campaign, a mobile measurement was conducted using a LI-COR 7810 tracer gas analyzer mounted on a vehicle during June 23-26, 2024 near the SGP site (within 7 km distance). We sampled CH 4 along predefined routes surrounding a suspected source, i.e., a large animal feeding operation (AFO) farm 5.8 km northwest of the SGP ARM site. Measurements were collected between 04:30 and 09:00 local time when the planetary boundary layer (PBL) was shallow, increasing sensitivity to surface emissions. Driving speed was kept below 5 mph to minimize pressure-related artifacts. CH 4 concentrations downwind of the AFO exceeded 6000 ppb on the early morning of June 26, corresponding to an emission rate of ~95 kg⋅hr -1 estimated using the mass balance method. Additional sources identified during the campaign include nearby open-range cattle and leaks from a nearby natural gas pipeline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Foreword to special issue: Papers from the 63rd annual meeting of the APS Division of Plasma Physics, November 8–12, 2021

The 63rd annual meeting of the APS Division of Plasma Physics (DPP) was held on November 8–12, 2021 in Pittsburgh at the David Lawrence Convention Center with both a live (in person) component and a virtual component. Following guidance from an APS COVID task force, all in-person attendees were fully vaccinated and masked. More than 800 physicists attended, safely, in-person. With both virtual and on-site participants, discussions were lively, and the research presentations showed unmatched mastery in the modern observation, theory, simulation, and manipulation of plasma. The presentations included four invited review talks, 97 invited talks, four tutorials, and four presentations from this year's prize and award recipients. There were more than 1200 contributed poster presentations and 725 contributed oral presentations. Including both in-person and remote attendees, DPP 2021 had a record of 2232 participants. As a hybrid meeting, in-person presentations of all invited presentations were broadcast live and were accompanied by a Q&A discussion. Contributed oral and poster presentations were prerecorded along with options to schedule in-person discussions on demand. Five mini-conferences were held: “Gatekeeper Workshop: Creating a Diverse, Equitable, and Inclusive Pipeline,” “Collisionless Shocks in Laboratory and Space Plasmas,” “The High Repetition Rate Frontier in High-Energy-Density Physics,” “Measuring and Modeling Plasma Surface Interactions,” and “The Second Mini-Conference on Machine Learning, Data Science and Artificial Intelligence in Plasma Research.” Finally, on the day before the official start of the meeting, an afternoon “for students, by students” included lightning talks, plasma trivia, and an informal occasion to connect with other students, learn how to get the most from the DPP Annual Meeting, and share successful ways to connect with colleagues and advance their professional careers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Optimal economic operation of liquid petroleum products pipeline systems

The majority of overland transport needs for crude petroleum and refined petroleum products are met using pipelines. Numerous studies have developed optimization methods for design of these systems in order to minimize construction costs while meeting capacity requirements. Here, we formulate problems to optimize the operations of existing single liquid commodity pipeline systems subject to physical flow and pump engineering constraints. The objectives are to maximize the economic value created for users of the system and to minimize operating costs. We present a general computational method for this class of continuous, non-convex nonlinear programs, and examine the use of pump operating settings and flow allocations as decision variables. Furthermore, the approach is applied to compute optimal operating regimes and perform engineering economic sensitivity analyses for a case study of a crude oil pipeline developed using publicly available data.

02 PETROLEUM↗
DOE OSTI2022

MIDAS2: Metagenomic Intra-species Diversity Analysis System

The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CO 2 Pipeline Analysis for Existing Coal-Fired Power Plants

Previous studies have examined the needs for CO 2 transport and storage to achieve a net zero emissions economy by 2050 (Abramson and Christensen, 2021; Greig and Pascale, 2021; Chen and Pawar, 2023). This analysis examines the minimum CO 2 pipeline infrastructure necessary to transport and store CO 2 captured from coal-fired power plants. This analysis also examines an additional network configuration considering minimizing the number of state border crossings, reflecting non-economic considerations that may impact pipeline buildout.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Theranostic Radiopnictogens: 71 As, 72 As, and 119 Sb (Final Technical Report)

This project has developed new methods for the cyclotron production of medically relevant radionuclides 71 As and 119 Sb. Arsenic and antimony are chemically homologous elements (group 5A, also known as the pnictogens) that have radionuclides that are of considerable interest within nuclear medicine. Such radiopnictogens include the potentially therapeutic radionuclides 119 Sb (t 1/2 = 38 h) that decays with the emission of 24.5 low energy, high potency electrons per decay with little concomitant photon radiation and 77 As (t 1/2 = 39 h) that decays with average beta energy of 230 keV and diagnostic nuclides 71 As (t 1/2 = 65 h, 28% β+) and 72 As (t 1/2 = 26 h, 80% β+) for positron emission tomography (PET). This work has had major success developing new methods for the cyclotron production and radiochemical isolation of 71 As, supporting parallel developments for 119 Sb, and assessing the chemical similarities between these two homologous radionuclides. This project brought into collaboration two universities with complimentary skill sets, proficiencies, expertise, and facilities: the University of Wisconsin (UWisc) and the University of Missouri (Mizzou). Professors Ellison and Engle have experience in the small cyclotron production and radiochemical isolation of radionuclides, including 72 As and 119 Sb. Their recently developed metallurgic methods for fabricating cyclotron targets have great potential to expand and allow for the biomedical cyclotron production of long- lived, lower positron energy 71 As. Professors Hennkens and Jurisson have significant experience in the reactor production, radiochemical isolation, and biological functionalization of radioarsenic. Recent development of trithiol-based chelator molecules for functionalizing radioarsenic provide a platform for the investigation of the fundamental challenges of the promising low-energy-electron emitter, 119 Sb. Through their positions within their respective University’s graduate schools, the PIs and Co-Is effectively trained of graduate students and postdoctoral researchers in nuclear and radiochemistry, sub-specialties specifically identified in the Department of Energy (DOE) Office of Science Isotope Program long-range plan. Annual laboratory research visits for students between UWisc and Mizzou provided essential broad-field experience and scientific networking that is critical for maintaining their path along the training pipeline to productive careers in isotope production. This research collaboration has provided significant benefits to the DOE University Isotope Network and radionuclide-using researchers around the country.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Learning dynamical systems from data: An introduction to physics-guided deep learning

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The impact of bilateral injuries on the pathophysiology and functional outcomes of volumetric muscle loss

Volumetric muscle loss (VML)—defined as the irrecoverable loss of skeletal muscle tissue with associated persistent functional deficits—is among the most common and highly debilitating combat-related extremity injuries. This is particularly true in cases of severe polytrauma wherein multiple extremities may be involved as a result of high energy wounding mechanisms. As such, significant investment and effort has been made toward developing a clinically viable intervention capable of restoring the form and function of the affected musculature. While these investigations conducted to date have varied with respect to the species, breed, and sex of the chosen pre-clinical in-vivo model system, the majority of these studies have been performed in unilateral injury models, an aspect which may not fully exemplify the clinical representation of the multiply injured patient. Furthermore, while various components of the basal pathophysiology of VML (e.g., fibrosis and inflammation) have been investigated, relatively little effort has focused on how the pathophysiology and efficacy of pro-regenerative technologies is altered when there are multiple VML injuries. Thus, the purpose of this study was two-fold: (1) to investigate if/how the pathophysiology of unilateral VML injuries differs from bilateral VML injuries and (2) to interrogate the effect of bilateral VML injuries on the efficacy of a well-characterized regenerative therapy, minced muscle autograft (MMG). In contrast to our hypothesis, we show that bilateral VML injuries exhibit a similar systemic inflammatory response and improved muscle functional recovery, compared to unilateral injured animals. Furthermore, MMG treatment was found to only be effective at promoting an increase in functional outcomes in unilateral VML injuries. The findings presented herein add to the growing knowledge base of the pathophysiology of VML, and, importantly, reiterate the importance of comprehensively characterizing preclinical models which are utilized for early-stage screening of putative therapies as they can directly influence the translational research pipeline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Outlook for artificial intelligence and machine learning at the NSLS-II

Abstract We describe the current and future plans for using artificial intelligence and machine learning (AI/ML) methods at the National Synchrotron Light Source II (NSLS-II), a scientific user facility at the Brookhaven National Laboratory. We discuss the opportunity for using the AI/ML tools and techniques developed in the data and computational science areas to greatly improve the scientific output of large scale experimental user facilities. We describe our current and future plans in areas including from detecting and recovering from faults, optimizing the source and instrument configurations, streamlining the pipeline from measurement to insight, through data acquisition, processing, analysis. The overall strategy and direction of the NSLS-II facility in relation to AI/ML is presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Early Results from GLASS-JWST. II. NIRCam Extragalactic Imaging and Photometric Catalog

We present the reduced images and multiwavelength catalog of the first JWST NIRCam extragalactic observations from the GLASS Early Release Science Program, obtained as coordinated parallels of the NIRISS observations of the Abell 2744 cluster. Images in seven bands (F090W, F115W, F150W, F200W, F277W, F356W, and F444W) have been reduced using an augmented version of the official JWST pipeline; we discuss the procedures adopted to remove or mitigate defects in the raw images. We obtain a multiband catalog by means of forced aperture photometry on point-spread function (PSF)-matched images at the position of F444W-detected sources. The catalog is intended to enable early scientific investigations, and it is optimized for faint galaxies; it contains 6368 sources, with limiting magnitude 29.7 at 5 σ in F444W. We release both images and catalog in order to allow the community to become familiar with the JWST NIRCam data and evaluate their merit and limitations given the current level of knowledge of the instrument.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Abstract Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
DOE OSTI2024

Influence of Rare Earth Ce Additions on Microstructure and Mechanical Properties of Experimental Pipeline Steels

Herein, the effect of Ce additions ranging from 57 to 263 ppm is evaluated for an experimental pipeline steel. Compared to the Ce-free steel, progressive Ce additions result in a slightly refined microstructure, significantly improve transverse impact properties, and slightly increase strength. All these observations can be attributed to the gradual transformation of Mn sulfide and Mn–Si–Al oxide inclusions to Ce-containing oxide/sulfides. In particular, the inclusions consist exclusively of sub-5 μm spherical Ce 2 O 2 S particles upon near-stoichiometric additions of Ce, considering the oxygen and sulfur impurity level of the steel. In conclusion, the results suggest that Ce is a potentially promising alloying addition for next-generation pipeline steels by replacing overtly deleterious inclusions with potentially beneficial ones.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Advanced genomics identifies growth effectors for proteotoxic ER stress recovery in Arabidopsis thaliana

Abstract Adverse environmental and pathophysiological situations can overwhelm the biosynthetic capacity of the endoplasmic reticulum (ER), igniting a potentially lethal condition known as ER stress. ER stress hampers growth and triggers a conserved cytoprotective signaling cascade, the unfolded protein response (UPR) for ER homeostasis. As ER stress subsides, growth is resumed. Despite the pivotal role of the UPR in growth restoration, the underlying mechanisms for growth resumption are yet unknown. To discover these, we undertook a genomics approach in the model plant species Arabidopsis thaliana and mined the gene reprogramming roles of the UPR modulators, basic leucine zipper28 (bZIP28) and bZIP60, in ER stress resolution. Through a network modeling and experimental validation, we identified key genes downstream of the UPR bZIP-transcription factors (bZIP-TFs), and demonstrated their functional roles. Our analyses have set up a critical pipeline for functional gene discovery in ER stress resolution with broad applicability across multicellular eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Building a custom high-throughput platform at the Joint Genome Institute for DNA construct design and assembly—present and future challenges

Abstract The rapid design and assembly of synthetic DNA constructs have become a crucial component of biological engineering projects via iterative design–build–test–learn cycles. In this perspective, we provide an overview of the workflows used to generate the thousands of constructs and libraries produced each year at the U.S. Department of Energy Joint Genome Institute. Particular attention is paid to describing pipelines, tools used, types of scientific projects enabled by the platform and challenges faced in further scaling output.

36 MATERIALS SCIENCE↗