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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Metabolically diverse microorganisms mediate methylmercury formation under nitrate-reducing conditions in a dynamic hydroelectric reservoir

Brownlee Reservoir is a mercury (Hg)-impaired hydroelectric reservoir that exhibits dynamic hydrological and geochemical conditions and is located within the Hells Canyon Complex in Idaho, USA. Methylmercury (MeHg) contamination in fish is a concern in the reservoir. While MeHg production has historically been attributed to sulfate-reducing bacteria and methanogenic archaea, microorganisms carrying the hgcA gene are taxonomically and metabolically diverse and the major biogeochemical cycles driving mercury (Hg) methylation are not well understood. In this study, Hg speciation and redox-active compounds were measured throughout Brownlee Reservoir across the stratified period in four consecutive years (2016–2019) to identify the location where and redox conditions under which MeHg is produced. Metagenomic sequencing was performed on a subset of samples to characterize the microbial community with hgcA and identify possible links between biogeochemical cycles and MeHg production. Biogeochemical profiles suggested in situ water column Hg methylation was the major source of MeHg. These profiles, combined with genome-resolved metagenomics focused on hgcA-carrying microbes, indicated that MeHg production occurs in this system under nitrate- or manganese-reducing conditions, which were previously thought to preclude Hg-methylation. Using this multidisciplinary approach, we identified the cascading effects of interannual variability in hydrology on the redox status, microbial metabolic strategies, abundance and metabolic diversity of Hg methylators, and ultimately MeHg concentrations throughout the reservoir. This work expands the known conditions conducive to producing MeHg and suggests that the Hg-methylation mitigation efforts by nitrate or manganese amendment may be unsuccessful in some locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Radio frequency sensing and its innovative applications in diverse sectors: A comprehensive study

Recent advancements in radio frequency (RF) sensing technology can be attributed to the development of the Internet of Things (IoT), healthcare, RF-identification, and communication applications. RF sensing is a multidisciplinary research field that requires expertise in computing, electronics, and electromagnetics to cover all system features, including protocol development, antenna design, sensor integration, algorithm formulation, interconnection, data, and analytics. The overarching aim of this work is to present detailed information about RF technologies and their innovations and application diversity from the novel work carried out at CSI Lab1, together in one platform with an extensive survey. This study presents state-of-the art applications and RF sensing that include W-Fi, radar, and SDR and RFID-based sensing. A comprehensive survey and study of the advantages and limitations of each non-contact technology is discussed. Additionally, open research gaps have been identified as well. Decades of knowledge and experience have been put to use to meet new challenges and demands. The development and study of RF systems, IoT, RFID sensing, and research and deployment activities, are briefly discussed. The emerging research projects with industry, institutional research centers, and academic studies are also addressed. Finally, an outline of identified potential future research areas is provided, emphasizing opportunities and challenges.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

BisQue for 3D Materials Science in the Cloud: Microstructure–Property Linkages

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyber-infrastructures which enable effcient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing{structure{property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing{structure{property relationships, can be implemented into the system. Speci cally, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally effcient data-driven model into the BisQue platform. The new module is made available online and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Wind turbine blade design with airfoil shape control using invertible neural networks

Wind turbine blade design is a highly multidisciplinary process that involves aerodynamics, structures, controls, manufacturing, costs, and other considerations. More efficient blade designs can be found by controlling the airfoil cross-sectional shapes simultaneously with the bulk blade twist and chord distributions. Prior work has focused on incorporating panel-based aerodynamic solvers with a blade design framework to allow for airfoil shape control within the design loop in a tractable manner. Including higher fidelity aerodynamic solvers, such as computational fluid dynamics, makes the design problem computationally intractable. In this work, we couple an invertible neural network trained on high-fidelity airfoil aerodynamic data to a turbine design framework to enable the design of airfoil cross sections within a larger blade design problem. We detail the methodology of this coupled framework and showcase its efficacy by aerostructurally redesigning the IEA 15-MW reference wind turbine blade. The coupled approach reduces the cost of energy by 0.9% compared to a more conventional design approach. This work enables the inclusion of high-fidelity aerodynamic data earlier in the design process, reducing cycle time and increasing certainty in the performance of the optimal design.

17 WIND ENERGY↗
DOE OSTI2025

CMCSCFCB—Critical Minerals in Coaly Strata of the Cherokee-Forest City Basin

This report summarizes results of CMCSCFCB—Critical Minerals in Coaly Strata of the Cherokee-Forest City Basin, a multiyear and multidisciplinary study funded through DE-FOA-0002364 as part of the Department of Energy Office of Fossil Energy and Carbon Management (FECM) Carbon Ore, Rare Earth and Critical Minerals (CORE-CM) Initiative for U.S. Basins. Our working group of state and tribal agencies collected data and made interpretations included herein. It also surveyed the regional potential for critical mineral/material production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Energy Materials Chemistry Integrating Theory, Experiment and Data Science (Final Report)

The Energy Materials Chemistry Integrating Theory, Experiment and Data Science (EM-CITED) project is a multidisciplinary research effort focused on accelerating discovery of scientific knowledge via incorporation of data science and artificial intelligence in materials chemistry research. The project aims to advance materials chemistry-aware data science to unify theory and experiment knowledge streams. The work resulted in foundational AI frameworks for materials chemistry – Deep Reasoning Networks (DRNets), Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP), and Material-to-Spectrum (Mat2Spec) prediction – as well as a host of strategies for accelerated scientific discoveries through principled incorporation of data science in computational and experimental research.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploration of Sn 70 Li 30 alloy as possible material for flowing liquid metal plasma facing components

As an advanced alternative to solid materials, Liquid Metals (LM) may offer more resilient and feasible Plasma Facing Components (PFCs). Particularly, regarding the unavoidable material erosion/degradation produced by particle/heat fluxes in future fusion devices where much longer duty cycles are expected. Furthermore, configurations that propose a flowing LM surface can add the advantage of a continuously fresh and clean layer facing the plasma. Although lithium is the most widely tested option, tin-lithium (SnLi) alloys have been proposed to attempt to combine the positive characteristics of both pure elements and ameliorate the specific issues of lithium. In this work, the potential use of Sn70Li30 alloy in such flowing concepts has been explored by addressing several preliminary and mandatory aspects for its utilization. Key issues such as wettability and compatibility of the alloy with relevant substrates have been studied in a multidisciplinary approach. The data obtained from deposited liquid tin-lithium droplets indicates approximate wetting temperatures of 360 °C, 390 °C and 405 °C for the fresh alloy on 316 stainless steel, molybdenum, and tungsten, respectively. However, the alloy contamination appeared to strongly affect the wetting characteristics of materials, increasing their wetting temperature by ~130 °C in the worst observed cases. Interestingly, in some instances, the instability of the liquid alloy surface was observed in the form of sudden gaseous ejection. The deposited droplets were posteriorly characterized in terms of absolute composition and depth profile by Inductively Coupled PlasmaOptical Emission Spectroscopy (ICP-OES) and Secondary Ion Mass Spectrometry (SIMS-ToF). Additionally, the nature and composition of the boundaries between the substrates and alloy microparticles was investigated by Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), and 3D Laser microscopy. The overall results of this post-mortem characterization revealed that first signs of corrosion induced by both alloy elements (lithium-chromium association and iron-tin intermetallic mixing) were present on 316 stainless steel after short exposures (≤3 h) at temperatures lower than 550 °C. Conversely, molybdenum and tungsten showed good compatibility with the alloy in equivalent conditions. The global implications of these results are finally addressed, focusing on the future perspectives and the more viable scenarios for the eventual utilization of these alloys in flowing liquid metal configurations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Finally, findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Electron kinetics in low-temperature plasmas

This work presents an overview of recent advances in the field of electron kinetics in low-temperature plasmas (LTPs). It also provides author's views on where the field is headed and suggests promising strategies for further development. The authors have selected several problems to illustrate multidisciplinary nature of the subject (space and laboratory plasma, collisionless and collisional plasmas, and low-pressure and high-pressure discharges) and to illustrate how cross-disciplinary research efforts could enable further progress. Nonlocal electron kinetics and nonlocal electrodynamics in low-pressure rf plasmas resemble collisionless effects in space plasma and hot plasma effects in fusion science, terahertz technology, and plasmonics. The formation of electron groups in dc and rf discharges has much in common with three groups of electrons (core, strahl, and halo) in solar wind. Runaway electrons in LTPs are responsible for a wide range of physical phenomena from nano- and picoscale breakdown of dielectrics to lightning initiation. Understanding electron kinetics of LTPs could promote scientific advances in a number of topics in plasma physics and accelerate modern plasma technologies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Assessment of Sandia's 2021 Pilot Program for Research Traineeships to Broaden and Diversify Fusion Energy Science: Development and Rapid Screening of Refractory Multi-Principal Elemental Composites for Plasma Facing Components

The Fusion Energy Sciences office supported “A Pilot Program for Research Traineeships to Broaden and Diversify Fusion Energy Sciences” at Sandia National Laboratories during the summer of 2021. This pilot project was motivated in part by the Fusion Energy Sciences Advisory Committee report observation that “The multidisciplinary workforce needed for fusion energy and plasma science requires that the community commit to the creation and maintenance of a healthy climate of diversity, equity, and inclusion, which will benefit the community as a whole and the mission of FES”. The pilot project was designed to work with North Carolina A&T (NCAT) University and leverage SNL efforts in FES to engage underrepresented students in developing and accessing advanced material solutions for plasma facing components in fusion systems. The intent was to create an environment conducive to the development of a sense of belonging amongst participants, foster a strong sense of physics identity among the participants, and provide financial support to enable students to advance academically while earning money. The purpose of this assessment is to review what worked well and lessons that can be learned. We reviewed implementation and execution of the pilot, describe successes and areas for improvement and propose a no-cost extension of the pilot project to apply these lessons and continue engagement activities in the summer of 2022.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Modelling the coupled mercury-halogen-ozone cycle in the central Arctic during spring

Near-surface mercury and ozone depletion events occur in the lowest part of the atmosphere during Arctic spring. Mercury depletion is the first step in a process that transforms long-lived elemental mercury to more reactive forms within the Arctic that are deposited to the cryosphere, ocean, and other surfaces, which can ultimately get integrated into the Arctic food web. Depletion of both mercury and ozone occur due to the presence of reactive halogen radicals that are released from snow, ice, and aerosols. In this work, we added a detailed description of the Arctic atmospheric mercury cycle to our recently published version of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem 4.3.3) that includes Arctic bromine and chlorine chemistry and activation/recycling on snow and aerosols. The major advantage of our modelling approach is the online calculation of bromine concentrations and emission/recycling that is required to simulate the hourly and daily variability of Arctic mercury depletion. We used this model to study coupling between reactive cycling of mercury, ozone, and bromine during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) spring season in 2020 and evaluated results compared to land-based, ship-based, and remote sensing observations. The model predicts that elemental mercury oxidation is driven largely by bromine chemistry and that particulate mercury is the major form of oxidized mercury. The model predicts that the majority (74%) of oxidized mercury deposited to land-based snow is re-emitted to the atmosphere as gaseous elemental mercury, while a minor fraction (4%) of oxidized mercury that is deposited to sea ice is re-emitted during spring. Our work demonstrates that hourly differences in bromine/ozone chemistry in the atmosphere must be considered to capture the springtime Arctic mercury cycle, including its integration into the cryosphere and ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

EMPOWER: Accelerating the Transformation of the Global Energy Economy - 2022 JISEA Annual Report

The 2022 JISEA Annual Report describes accomplishments from the past year at the Joint Institute for Strategic Energy Analysis, which is accelerating the transformation of the global energy economy. 2021 was filled collaboration and high-impact multidisciplinary projects, exploring everything from the energy-water-food nexus, to the cobalt supply chain, to sustainable communities. This year also welcomed the JISEA Catalyzers Initiative that is accelerating research capabilities at the National Renewable Energy Laboratory. The Catalyzers are empowering communities and research partners to by bringing together experts working within one space to discuss capabilities and future needs to continue to stay ahead of the research curve.

clean energy↗
DOE OSTI2021

Crowdsourcing Global Perspectives in Ecology Using Social Media

Transparent, open, and reproducible research is still far from routine, and the full potential of open science has not yet been realized. Crowdsourcing–defined as the usage of a flexible open call to a heterogeneous group of individuals to recruit volunteers for a task –is an emerging scientific model that encourages larger and more outwardly transparent collaborations. While crowdsourcing, particularly through citizen- or community-based science, has been increasing over the last decade in ecological research, it remains infrequently used as a means of generating scientific knowledge in comparison to more traditional approaches. We explored a new implementation of crowdsourcing by using an open call on social media to assess its utility to address fundamental ecological questions. We specifically focused on pervasive challenges in predicting, mitigating, and understanding the consequences of disturbances. In this paper, we briefly review open science concepts and their benefits, and then focus on the new methods we used to generate a scientific publication. We share our approach, lessons learned, and potential pathways forward for expanding open science. Our model is based on the beliefs that social media can be a powerful tool for idea generation and that open collaborative writing processes can enhance scientific outcomes. We structured the project in five phases: (1) draft idea generation, (2) leadership team recruitment and project development, (3) open collaborator recruitment via social media, (4) iterative paper development, and (5) final editing, authorship assignment, and submission by the leadership team. We observed benefits including: facilitating connections between unusual networks of scientists, providing opportunities for early career and underrepresented groups of scientists, and rapid knowledge exchange that generated multidisciplinary ideas. We also identified areas for improvement, highlighting biases in the individuals that self-selected participation and acknowledging remaining barriers to contributing new or incompletely formed ideas into a public document. While shifting scientific paradigms to completely open science is a long-term process, our hope in publishing this work is to encourage others to build upon and improve our efforts in new and creative ways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting Future Disease Burden in a Rapidly Changing Climate

The interplay of a rapidly changing climate and infectious disease occurrence is emerging as a critical topic, requiring investigation of possible direct, as well as indirect, connections between disease processes and climate-related variation and phenomena. First, we introduce and overview three infectious disease exemplars (dengue, influenza, valley fever) representing different transmission classes (insect-vectored, human-to-human, environmentally-transmitted) to illuminate the complex and significant interplay between climate disease processes, as well as to motivate discussion of how Sandia can transform the field, and change our understanding of climate-driven infectious disease spread. We also review state-of-the-art epidemiological and climate modeling approaches, together with data analytics and machine learning methods, potentially relevant to climate and infectious disease studies. We synthesize the modeling and disease exemplars information, suggesting initial avenues for research and development (R&D) in this area, and propose potential sponsors for this work. Whether directly or indirectly, it is certain that a rapidly changing climate will alter global disease burden. The trajectory of climate change is an important control on this burden, from local, to regional and global scales. The efforts proposed herein respond to the National Research Councils call for the creation of a multidisciplinary institute that would address critical aspects of these interlocking, cascading crises.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Water Dynamics in Cancer Cells: Lessons from Quasielastic Neutron Scattering

The severity of the cancer statistics around the globe and the complexity involving the behavior of cancer cells inevitably calls for contributions from multidisciplinary areas of research. As such, materials science became a powerful asset to support biological research in comprehending the macro and microscopic behavior of cancer cells and untangling factors that may contribute to their progression or remission. The contributions of cellular water dynamics in this process have always been debated and, in recent years, experimental works performed with Quasielastic neutron scattering (QENS) brought new perspectives to these discussions. In this review, we address these works and highlight the value of QENS in comprehending the role played by water molecules in tumor cells and their response to external agents, particularly chemotherapy drugs. In addition, this paper provides an overview of QENS intended for scientists with different backgrounds and comments on the possibilities to be explored with the next-generation spectrometers under construction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Framework for Patient-Centered Pathways of Care for Radiopharmaceutical Therapy: An ASTRO Consensus Document

Radiopharmaceutical therapy (RPT) is an area of projected growth and importance with several agents in clinical use, new agents in late-phase clinical trials, and many others under testing and development. This article proposes a framework for developing pathways of care that can be broadly applied to all RPTs, representing the current status of RPT. It suggests foundational elements for many pathways of care for patients with cancer and concludes with areas in active development and the future horizon for RPT treatment centers. Developing a framework for patient-centered pathways of care is a critical step in establishing RPT as standard therapy for patients with a diverse spectrum of cancers. This expected increase in RPT treatment options will affect a much larger population of patients with complex cancer. It will also require enhanced coordination and collaboration among appropriately qualified personnel with diverse expertise in image acquisition, image interpretation, quantitative imaging, dosimetry calculation, radiation quality assurance and safety as well as oncology care and RPT-induced sequelae and response assessment. The essential role of this evolving RPT care team within multidisciplinary oncology care is a cornerstone of this framework for a patient-centered pathway of care for RPT. Given the status of current RPT practice and the horizon for future applications, this patient-centered pathway of care guidance is timely and should help inform future clinical RPT practice paradigms. A task force was recruited from the Theranostic Working Group of the American Society for Radiation Oncology (ASTRO) in May 2019 with equal representation from the nuclear medicine community. The task force expanded on a framework that was originally conceived by the Working Group for patient-centered care. This framework was developed to incorporate the strengths of both radiation oncologists and nuclear medicine physicians. The manuscript was then developed by the task force and posted on the ASTRO website for a 6-week public comment period ending in July 2020. Comments were adjudicated, and the draft was sent to external organizations for potential endorsement. This document was sent to the ASTRO Board of Directors in October 2020 for approval.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Snow thermal conductivity and conductive flux in the Central Arctic: Estimates from observations and implications for models

During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m −1 K −1 to 0.47 W m −1 K −1 with a median from all data of 0.39 W m −1 K −1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m −2 , but the average residual during winter is −6 W m −2 , which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Novel Hot Gas Components for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

Additive Manufacturing (AM), also known as 3D printing, has emerged as a manufacturing method that enables new design freedom for gas turbine engine manufacturers. However, the material selection for AM processable high-temperature super alloys is currently limited. Additionally, the heat transfer performance of AM enabled micro-cooling architectures is not yet well understood. Accordingly, in support of advanced manufacturing and engine performance development, Oak Ridge National Laboratory (ORNL)and Solar Turbines (Solar) conducted a multidisciplinary project to generate both AM super alloy material properties data and micro-channel performance data for two AM super alloys. The data supported the design and analysis of an internally cooled turbine hot section AM tip shoe component. This data was used to analytically predict the reduction in operating temperature of a gas turbine tip shoe. The work concluded that the cooling flow required to cool the tip shoe can be tuned to suit the efficiency improvements desired in an industrial gas turbine.

36 MATERIALS SCIENCE↗