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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Recyclability of additively manufactured bio-based composites

As the flexibility, efficiency, and application space of additive manufacturing continues to grow, many have begun to investigate more sustainable feedstocks as well as options for the end of life of additively manufactured parts. This study examines the effects of mechanical recycling on additively manufactured parts from bio-based feedstock. Here, articles were printed on the Big Area Additive Manufacturing (BAAM) system at the Oak Ridge National Laboratory using poly (lactic acid)/wood flour (PLA/WF) pellets. These parts were shredded and granulated, and the granulate was fed directly back into the BAAM system for re-printing, skipping the costly and energy-intensive steps of extrusion and pelletization. The chemical, mechanical, thermal, and rheological changes to PLA/WF before and after recycling were investigated. Additionally, the energy savings from directly printing granulate on the BAAM system without extrusion and pelletization is reported. It is shown that PLA/WF is an excellent candidate for recycling of large format additively manufactured parts, and value of these parts can be reclaimed while saving cost and energy through mechanical recycling.

42 ENGINEERING↗
DOE OSTI2022

Aggregate attack surface management for network discovery of operational technology

Interconnectivity has become a substratum of technology as the benefits of data-driven functionality are being realized in nearly all industries. Increased connectivity of Operational Technology (OT) exacerbates cyber risks because Industrial Control Systems (ICS) are becoming exposed to the Internet. These exposures are often done inadvertently through misconfigurations as additional network devices come online. Attack surface management (ASM) platforms can be used to identify vulnerabilities by performing external network discovery over the Internet using web spiders. These web spiders enable big data analytics of Internet of Things (IoT) devices as identifiable information of Internet-exposed equipment are archived in searchable databases that are made publicly available. There are a multitude of ASM service providers on the market. Here, this study was conducted to evaluate several commonly known tools to determine the aggregate attack surface of control systems. Queries were crafted by targeting commonly known manufacturers and communication protocols found in OT networks. Identified devices were that categorized based on technology types. Each query was replicated between several tools to target identical ICS equipment. Findings in this paper suggested a significant variance in the exposures discovered by each tool, but unique contributions were identified for each tool when a merged attack surface was derived. Therefore, all tools should be used in aggregate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The hazard components of representative key risks. The physical climate perspective

The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II in order to categorize, assess and communicate a wide range of regional and sectoral key risks, expected to become severe as a consequence of the potentially detrimental convergence of changing climate conditions, exposure and vulnerability of human and natural systems, and responses through (mal)adaptation measures. Other papers in this special issue treat each of eight RKRs by holistically assessing their current status and future evolution as a result of this convergence. However, their treatment cannot always organize such assessment according to a systematic gradation of climatic changes, often times having to extrapolate the big-picture evolution of risk from either qualitative effects of “low” medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C). In this study we offer as systematic a representation as possible – given current literature and assessments – of the future evolution of the hazard components of RKRs. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group 1 (WGI) supplemented when needed by more recent literature. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels to the extent possible. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with global average temperature change has been assessed. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - the link to increasing global temperatures can only be described qualitatively. For some processes - like the behavior of ice-sheets, or changes in circulation dynamics - large uncertainties about the effects of different levels of warming remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems when these changes happen in conjunction with these systems’ conditions of exposure and vulnerability, and in lack of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in an often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future possible evolution which represents an important component – if never the only one – of the potential future evolution of risk severity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Stochastic gravitational wave background from global cosmic strings

Global cosmic strings are generically predicted in particle physics beyond the Standard Model, e.g., a post-inflationary global U(1) symmetry breaking which may associate with axion-like dark matter. Here, we demonstrate that although subdominant to Goldstone emission, gravitational waves (GWs) radiated from global strings can be observable with current/future GW detectors. The frequency spectrum of such GWs is also shown to be a powerful tool to probe the Hubble expansion rate of the Universe at times prior to the Big Bang nucleosynthesis where the standard cosmology has yet to be tested.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Synergy between cosmological and laboratory searches in neutrino physics

The intersection of the cosmic and neutrino frontiers is a rich field where much discovery space still remains. Neutrinos play a pivotal role in the hot big bang cosmology, influencing the dynamics of the universe over numerous decades in cosmological history. Recent studies have made tremendous progress in understanding some properties of cosmological neutrinos, primarily their energy density. Upcoming cosmological probes will measure the energy density of relativistic particles with higher precision, but could also start probing other properties of the neutrino spectra. When convolved with results from terrestrial experiments, cosmology can become even more acute at probing new physics related to neutrinos or even Beyond the Standard Model (BSM). Any discordance between laboratory and cosmological data sets may reveal new BSM physics and/or suggest alternative models of cosmology. Here we give examples of the intersection between terrestrial and cosmological probes in the neutrino sector, and briefly discuss the possibilities of what different laboratory experiments may see in conjunction with cosmological observatories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Design analysis of a particle-based thermal energy storage system for concentrating solar power or grid energy storage

Energy storage is becoming indispensable for expanding renewable energy integration, and it is critical to the future low-carbon energy supply. Large-capacity, grid scale energy storage can support the integration of solar and wind power and support grid resilience with the diminishing capacity of baseload fossil power plants. With the development of thermal energy storage (TES) for concentrating solar power systems, standalone TES for grid integration becomes attractive due to the declining renewable generation cost and an increasing need for energy storage. The standalone TES system introduced in this paper can play a big role in the carbon-free energy future with capacity larger than batteries and cost likely lower than other energy storage methods such as pumped storage hydropower and compressed air energy storage, both of which also have geological limitations. To this end, we describe a TES system that uses stable, inexpensive solid particles as a TES media to provide scalable, low cost energy storage. The particle-based TES has the ability to drive various thermal power cycles including conventional steam-Rankine, air Brayton turbine with combined-cycle ability, or the emerging supercritical carbon dioxide Brayton power cycle. This work describes the containment design method including a concrete silo and an internal-insulation layer for the particle-TES system. The economic analysis shows significantly low storage cost when the particle-TES is integrated with Brayton combined-cycle power generation. The paper shows the design approach of the particle-TES system and its economic potential for bulk energy storage. The advantage of the particle-TES system as a promising bulk energy storage method is its ability to economically support dispatchable renewable grid penetration for larger capacity and longer discharging hours than current battery storage technologies.

25 ENERGY STORAGE↗
DOE OSTI2025

A new tubular ironless linear induction motor using non-interrupted winding method

The ironless linear induction motor device can be used for any application which requires a large motion span or rotation restrictedly by the ironless feature due to the strong magnetic field environment. One of the possible applications is ITER diagnostics shutters. The tokamak reactor systems have a severe radiation issue during the operation. The traditional Kapton-insulated wires have significantly lower radiation resistance than an expected large radiation dose at ITER Motion Stark Effect (MSE) shutter location. Furthermore, the traditional single-sided linear induction motor winds the wire to make the N-turn pitch coils independently and then connect them in series to make a one-phase winding. Therefore, it is a big challenge to make so many radiation-protection connectors in such a limited space as inside the vacuum vessel of fusion reactors. The approved mineral-insulated kinds of cables need a large bend radius for the end turns, so the traditional single-sided linear motor with two-layer three-phase windings need to be upgraded to the tubular linear motors. The new proposed tubular ironless linear induction motor can not only overcome the large bend radius issue with the mineral-insulated cables but also allow the motor coils wound continuously around the tubular-shaped stator, forward and backward N times to make a N-turn winding for each phase, in order to resolve the radiation protection issue with the winding connection. The 3D transient Maxwell electromagnetic model with a tubular three-phase linear induction motor has been analyzed. Furthermore, this paper will present the detailed design and winding method of ironless linear induction motor actuators, and results of the 3D transient numerical simulation of the actuator performance.

Ironless↗
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

In-depth analysis on parallel processing patterns for high-performance Dataframes

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bat activity at ecovoltaic solar energy developments in the Midwestern United States

As global photovoltaic (PV) solar electricity generation continues to increase, some PV sites are co-prioritizing electricity generation and ecosystem function (“ecovoltaics”) to align renewable energy development with biodiversity conservation. Thus far, positive responses of plant and insect communities to native habitats at ecovoltaic sites have been observed, but there has been little research on bat responses to ecovoltaic designs in the U.S. We conducted passive ultrasonic monitoring in 2023 and 2024 at 12 solar sites and paired reference sites (agricultural fields) to investigate bat activity responses to ecovoltaic facilities in the Midwestern U.S. We found that average weekly overall bat activity was approximately 50?% higher within ecovoltaic sites than reference sites in the first half of the monitoring season. We also found species-specific differences in bat responses to ecovoltaic sites, with Hoary Bats showing higher activity on ecovoltaic sites throughout most of the monitoring season, Big Brown Bats showing higher activity on ecovoltaic sites during the first one-third of the monitoring season, and Silver-haired Bats showing no difference in activity between ecovoltaic sites and reference sites. There were no weeks in which bat activity was statistically greater on reference sites, suggesting that bats in the Midwestern U.S. do not avoid ecovoltaic solar sites. Rather, our results suggest that ecovoltaic sites in this region may provide early season (May-June) habitat at a time of year when resources may be limited in the surrounding landscape. These findings support a growing body of evidence on the positive ecological outcomes of ecovoltaic designs. Further investigations of the types of bat calls being recorded at PV sites and relationships with insect prey abundance are needed to understand the underlying drivers of species-specific responses to PV developments.

14 SOLAR ENERGY↗
DOE OSTI2022

Context and mitigation of lost circulation during geothermal drilling in diverse geologic settings

Lost circulation is one of the most common and expensive problems facing geothermal energy development, representing up to 30% of drilling costs. We examined drilling records from four geothermal fields—McGinness Hills in central Nevada, Don A. Campbell and Steamboat Hills in western Nevada, and Puna Geothermal Venture on the Big Island of Hawai'i— to identify geologies most prone to lost circulation, as well as common mitigation strategies. Depths of lost circulation events varied, but their frequency often increased in the production interval. Lost circulation commonly occurred near fault intersections, and heavily faulted fields like McGinness Hills and Don A. Campbell showed secondary mineralization within approximately 100 m (328 ft) or less of where circulation was lost. Lost circulation mitigation strategies included using locally available materials (e.g., cotton seed hulls) as well as more expensive proprietary lost circulation materials, cement plugs above the reservoir, and drilling blind with aerated, polymer-based mud in the production zone. Addressing lost circulation using a well thought out decision-making approach and materials above the reservoir will save time and cost, and provide needed well integrity. Mitigation often requires a series of steps, typically applied from perceived least expensive to most, and are dependent on the severity and location in the well where circulation was lost and availability of materials. Placing cement plugs can cure lost circulation events, however these plugs are often expensive, time-consuming, and may not be successful.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Large balancing areas and dispersed renewable investment enhance grid flexibility in a renewable-dominant power system in China

Renewable energy is poised to play a major role in achieving China's carbon neutrality goal by 2060; however, reliability and flexibility is a big concern of a renewable-dominant power system. Various strategies of enhancing flexibility are under discussion to ensure the reliability of such a system, but no detailed quantitative analysis has been reported yet in China. We combine the advantages of a capacity expansion model, SWITCH-China, with a production simulation model, PLEXOS, and analyze flexibility options under different scenarios of a renewable-dominant power system in China. We find that a larger balancing area offers direct flexibility benefits. Regional balancing could reduce the renewable curtailment rate by 5-7%, compared with a provincial balancing strategy. National balancing could further reduce the power cost by about 16%. However, retrofitting coal power plants for flexible operation would only improve the system flexibility marginally.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Response to “how robust are reductions in modeled estimates from GTAP-BIO of the indirect land use change induced by conventional biofuels?”

Malins et al. (2020) recently published "How robust are reductions in modeled estimates from GTAP-BIO of the indirect land use change induced by conventional biofuels?", provided their narrative from the model improvements in GTAP-BIO over time, made several critical points regarding this model, and argued that the implemented improvements in this model tended to decrease Induce Land Use Changes (ILUC) emissions. Furthermore, they also provided several critical points regarding the Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) emissions model. In this response to Malins et al. we address these critiques point-by-point by providing our detailed responses to key issues: 1) the GTAP-BIO model and its improvements over time; 2) the inclusion of cropland pasture in the model; 3) the observed land use changes in the US that have been the bases for improvements in GTAP-BIO model, 4) the time trends in corn price and yield to challenge the concept of the yield to price response; 5) some sources of land intensification in crop production; 6) the FAO notifications with respect to the comparison between harvested area and arable land; and 7) the GTAP-BIO results for multiple cropping. We also provided responses to Malins et al. critical points regarding the CCLUB emissions model. We hope that this response letter will open more constructive discussion among the LUC modeling community to remain focused on the big picture regarding agriculture's role as a very effective GHG mitigation tool that can shape the new policies to govern the production and consumption of biofuels.

09 BIOMASS FUELS↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

An advanced low-cost cathode composed of graphene-coated Na 2.4 Fe 1.8 (SO 4 ) 3 nanograins in a 3D graphene network for ultra-stable sodium storage

Iron-based electrodes have attracted great attention for sodium storage because of the distinct cost effectiveness. However, exploring suitable iron-based electrodes with high power density and long duration remains a big challenge. Herein, a spray-drying strategy is adopted to construct graphene-coated Na 2.4 Fe 1.8 (SO 4 ) 3 nanograins in a 3D graphene microsphere network. The unique structural and compositional advantages endow these electrodes to exhibit outstanding electrochemical properties with remarkable rate performance and long cycle life. Additionally, mechanism analyses further explain the outstanding electrochemical properties from the structural aspect.

25 ENERGY STORAGE↗