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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In-situ optical monitoring of gas turbine blade coatings under operational extreme environments

High temperature material systems consisting of thermal barrier coatings and the underlying superalloys are key to achieving higher gas temperatures which in turn control engine efficiencies and reduce emissions. With engine temperatures in excess of the limits that metallic blades and vanes can endure, advanced monitoring techniques that ensure the integrity and durability of these coatings are paramount to continuous and safe operation. Optical methods have the benefit of being non-invasive and are able to capture data under limited access available for monitoring the hot-section of turbines. Addressing Topic 5 on Advanced Instrumentation, the research and development effort for this project titled “In situ optical monitoring of gas turbine blade coatings under operational extreme environments” was completed by University of Central Florida researchers, including Raghavan (PI) and Ghosh (co-PI). In this project, the research team leveraged intrinsic properties of coatings and rare-earth dopants as active and intelligent “sensors” through their optical properties. The outcomes of this project pave the way for diagnostics of turbine blade coatings, under operating environments, through the development of the sensing thermal barrier coating configurations, calibration and associated instrumentation while ensuring coating integrity and durability goals are concurrently met.

42 ENGINEERING↗
DOE OSTI2023

Maximizing Uptake of Hygroscopic Hydrogels Through Extreme Swelling-Induced Salt Loading

Hygroscopic hydrogels have emerged as scalable and low-cost materials with the potential of high-performance vapor sorption for atmospheric water harvesting, dehumidification, and passive cooling. Despite extensive research interest aimed at improving hygroscopic hydrogels, devices using these materials still exhibit insufficient performance, partly due to limited water uptake of the hydrogels. In this work, we study the swelling dynamics of hydrogels in aqueous lithium chloride solutions and use this to achieve extreme salt loading of hygroscopic hydrogels. By rationally tuning the salt concentration used for swelling we achieve successful synthesis of hygroscopic hydrogels with high water uptake of 1.76 g/g, 2.53 g/g, 3.80 g/g at relative humidites of 30%, 50%, and 70%, respectively, exceeding previous hydrogels by over 19%. These water uptakes bring the performance of hydrogels within ≈91% of the fundamental limit of commonly used hygroscopic salts, while still avoiding leakage problems common in salt solutions. Furthermore, we elucidate via models the maximum uptake achievable without leakage as a function of the operating relative humidity and hydrogel swelling ratio. The insights from this work can be used to design hygroscopic hydrogels with exceptional performance for a wide variety of sorption applications to tackle global challenges such as water scarcity and energy efficiency.

25 ENERGY STORAGE↗
DOE OSTI2023

Dielectrics Under Extreme Electric Fields: In Situ Studies on Nanoscale Mechanisms (Final Technical Report)

The lack of materials that can function reliably under extreme electric fields is a critical roadblock to achieving higher energy efficiency and extended service lifetime of devices in electricity generation, transmission, and consumption. Materials typically fail at two or three orders of magnitude below their theoretical dielectric breakdown strength. Identifying the origins of premature dielectric breakdown using state-of-the-art characterization tools will, therefore, enable the design and discovery of transformational materials capable of approaching their intrinsic limits. The vast improvement in the resolution power of imaging tools, combined with the recently developed in situ electric biasing technique, has made high resolution observations of dielectric breakdown as it happens possible. The overall goal of the project is to directly image the dynamic processes of defect formation, accumulation, and interaction with preexisting defects (e.g., dislocations, domain walls, and grain boundaries in crystalline dielectric films) under electric fields up to 1,000 MV/m with a temporal resolution better than 5 microseconds. Using the newly acquired, custom-made Hysitron PI95 transmission electron microscopy (TEM) specimen holder, in situ TEM observations of breakdown at the sub nanometer resolution will be made on representative dielectric compounds with progressive complexities: the linear dielectric SiO 2 , CuO, and TiO 2 , the ferroelectric BaTiO 3 nanocrystals and nanocubes. Through direct observation of the breakdown event at high spatial and temporal resolutions, the nanoscale mechanisms for the failure of dielectrics under intense electric fields will be identified. The results of the project will be applied to the design and processing of new dielectric materials with higher efficiency, improved reliability, and a prolonged lifetime. Such new dielectrics are urgently needed for modernizing energy infrastructures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Grid Resilience to Extreme Events – Connecting Science to Investments and Policy

A workshop co-hosted by Pacific Northwest National Laboratory and Seattle City Light was held at the Seattle Municipal Tower on November 8–10, 2022. Participants from research organizations, utilities, professional associations, consultants, and government organizations attended. The purpose of the workshop was to develop a vision for addressing climate extremes that include improving forecasting and characterization, infrastructure resilience modeling, and investment planning and decision support. The workshop also aimed to provide a platform for sharing approaches and information and identifying possible collaborations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Oddities in Lightning Measurements from Space Reveal Extreme Flashes and Thunderstorms [Slides]

Space-based sensors are well-suited for detecting intracloud (IC) and Cloud to Ground (CG) lightning over large swaths of the Earth. Current sensors also measure the signals generated by lightning that contain information about the physical discharge process and the radiative transfer effects they experience on their path to orbit. Long records of orbital lightning data reveal rare and exceptional lightning signals push the boundaries of what the sensors are designed to measure and our understanding of what lightning and thunderstorms are capable of. Space-based sensors have revealed long-horizontal “megaflashes,” highly radiant “superbolts,” extremely high flash rate thunderstorms, and more.

58 GEOSCIENCES↗
DOE OSTI2023

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows

This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Cybersecurity Platform and Certification Framework Development for Extreme Fast Charging (XFC)-Integrated Charging Ecosystem (Final Project Report)

This report summarizes a pioneering effort in Electric Vehicle charging infrastructure ecosystem cybersecurity requirements, assessment methodologies, functional verification, as well as embodiment of the key technologies in the form of hardware and software tools being made available to the public. EPRI led a team of experts, as well as a stakeholder coalition encompassing all key actors in the EV charging infrastructure ecosystem that includes eXtreme Fast Charging (XFC) equipment (defined as 200kW or above). EV charging infrastructure in the United States is a patchwork of networks that have continued to grow organically and have been designed to serve the charging needs of the EV owners, who are their customers. In doing so, each network provider, as well as their connected entities such as the cloud Electric Vehicle Service Providers or EVSPs, utility back office, utility AMI networks, payment networks, as well as Original Equipment Manufacturer (EV manufacturer) telematics networks, have designed systems that may work well individually, but no single entity is responsible for the entire ecosystem to be secure in terms of data exchange. Furthermore, there is no uniformity in how each actor has implemented the cybersecurity requirements since no system-wide cybersecurity requirements existed prior to this project. The final project report describes the technical approach guided by the EV charging infrastructure cybersecurity working group, convened specifically for this project. The technical approach included definition of requirements at the ecosystem level, treated as a ‘system of systems’, and then passed down to individual systems (EVSE, EV, cloud EVSP, utility, and the payment networks), followed by developing the cybersecurity risk and vulnerability assessment methods, that were later applied to real-world cyber-physical systems at EPRI, ANL, and NREL laboratories, to validate both the process and the results. Finally, in a spotlight over the most vulnerable equipment, which is the EV charge station (AC or DC), the team developed a multi-layer cybersecurity implementation in the embedded domain embodied by the open-source Secure Network Interface Card (SNIC) demonstrating the various ways in which the infrastructure can be secured protecting against the identified attack surfaces. Finally, the entire process of EV infrastructure cybersecurity assessment was encapsulated in the Electric Vehicle Charging Cybersecurity Management (EVC2M) online GUI-based tool, that is expected to be released to the public. The report presents the objectives, the technical approach, the key results, and recommendations for future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II - Clustering Extremely High-Dimensional Grid-Based Data

Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is to cluster the outputs based on similarity and build a separate surrogate model for each cluster. This clustering is relatively straightforward when the output at each time step is of moderate size. However, when the spatial domain is represented by a large number of grid points, numbering in the millions, the clustering of the data becomes more challenging. In this report, we consider output data from simulations of a jet interacting with high explosives. These data are available on spatial domains of different sizes, at grid points that vary in their spatial coordinates, and in a format that distributes the output across multiple files at each time step of the simulation. We first describe how we bring these data into a consistent format prior to clustering. Borrowing the idea of random projections from data mining, we reduce the dimension of our data by a factor of thousand, making it possible to use the iterative k-means method for clustering. We show how we can use the randomness of both the random projections, and the choice of initial centroids in k-means clustering, to determine the number of clusters in our data set. Our approach makes clustering of extremely high dimensional data tractable, generating meaningful cluster assignments for our problem, despite the approximation introduced in the random projections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Belite Cement, and Concretes; Novel Low-Energy Approaches to Making Concrete Extremely Durable (Final Report)

This award allowed the University of Kentucky to demonstrate a number of key objectives. The focus of the project was on creating a belite cement, and concrete, that demonstrated a greatly reduced CO 2 demand for manufacture and enabling the development of extremely durable concrete. The accomplishments of the developed technology includes the following, as compared to Ordinary Portland cement: 2x greater compressive strength; 10x greater corrosion resistance; 35 – 50% less estimated clinker and mill energy use; 15 – 20% less estimated cement manufacturing cost; 35% more estimated clinker capacity; 25 – 30% less estimated cement CO 2 footprint.

36 MATERIALS SCIENCE↗
DOE OSTI2023

PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems

The objective of this project is to design, develop, and evaluate scalable software to enhance resilience, data checkpointing, program restart, and analysis. The proposed tasks are to 1) develop scalable machine learning techniques to learn temporal change patterns in a scalable and in-situ manner, and to minimize data movement and maximize learning locally closest to data; 2) design a concise data representation and indexing mechanism to capture the distribution of changes in data that can guarantee point-wise user-defined tolerable errors while reducing the data storage requirements by an order of magnitude or more; 3) develop data reduction techniques as library modules; 4) exploit local SSD for minimizing data movement in storage hierarchy; 5) develop anomaly detection algorithms that can predict corruptions based on learning of emerging patterns; 6) develop software libraries to be incorporated within widely used data formats and APIs; and 7) evaluate the proposed software using DOE scientific applications. The outcomes of the proposed work are to satisfy many synergistic data reduction and resilience requirements for large-scale data intensive applications executed on extreme-scale computing systems. The developed mechanism for error-bound data approximation is directly applicable to existing scientific applications. Through machine learning from historical events and change distribution, this work will enable anomaly detection for DOE computer facility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Materials in Extremes [Slides]

Goals: Realization of advanced predictive models that capture aspects of processing structure-properties-performance (PSPP) in ways that build on scale bridging, data science, and transformational physics insights from experiments; Based on a robust and seamless integration of sample manufacturing, characterization (pre- and post-mortem), theory and simulation, and novel experiments; Ultimately we want to be able to reliably predict performance, and control the functionality of materials in extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Testing Instrument Extremes in the TREAT Facility

TREAT is a transient power-shaping reactor whose primary mission is to enhance safety performance by testing nuclear fuels and materials under thermodynamic and neutronic conditions ranging from off-normal to extreme. Transient testing of nuclear fuels is analogous to car crash testing, in that the dramatic changes commonly seen when comparing the test specimens' initial and final state points necessitate in-situ measuring in order to interpret and understand the evolution of the experiment. TREAT’s experiment design strategy utilizes a highly reconfigurable and accessible reactor core that affords flexibility when installing experiment devices, thanks to the lack of a primary coolant boundary and the inclusion of multiple access points in the reactor bioshield. Within the experiment devices, the test specimens (which are integrated with their test environments), desired instrumentation diagnostics, and safety considerations are all contained in a single engineered package. The experiments encompass various types of environments (e.g., water, sodium, and gas at temperature and pressure), as well as a range of control options (e.g., static or flowing coolants). The experimental conditions are uniquely representative of advanced reactors, providing challenges to sensor performance but also affording a unique opportunity for development and qualification of sensors suited to these environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Novel Signatures Beyond the Standard Model: From Extreme Environments to Precision Measurements

Most of our universe is dark. It is dark literally—visible matter makes up only 5% of the energy density—and conceptually, as many outstanding questions remain unsolved. The discovery of particle dark matter would shed light on our dark universe and throw open a window on the highest energy scales. This research made progress toward dark matter and new particle searches in three ways. Extreme compact objects, such as black holes, neutron stars, and stellar cores, can parametrically increase the production rates of feebly interacting particles and yield observable signatures. Specifically, a process known as superradiance leads to exponential growth of the number of ultralight particles gravitationally bound to rotating black holes, and turns astrophysical black holes into particle detectors. This research broadened the scope of applicability of superradiance studies to particles with a wide range of interactions and masses, predicting multi messenger signals. When ultralight particles are produced as dark matter in the early universe, they can be detected directly in laboratory experiments. However, these feebly interacting particles lead to small signals that are inevitably difficult to tease out from backgrounds; their detection requires the theoretical development of unique signatures that can be searched for in low-noise experiments. Some dark matter particles could be detectable with thin film metamaterials combined with low noise single-photon detectors. This research develop the theoretical framework and supported experimental efforts for ambitious searches for a wider class of ultralight particles. Overall, the outcome of this theoretical particle physics program developed novel signatures of new dark matter particles, and thereby expanded the particle parameter space which can be explored with astrophysical observations and precision experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Intelligent, grid-friendly, modular extreme fast charging system with solid-state DC protection

The development of electric vehicle (EV) charging infrastructure is crucial for the widespread adoption of electric transportation. However, implementing such infrastructure is a complex task that requires consideration of factors such as space limitations, adherence to industry standards, grid capacity, and other technical and policy issues. This project seeks to create a framework for the efficient design of compact medium voltage (MV) extreme fast charging (XFC) stations for EVs. The station design involves the use of a solid-state transformer (SST) that connects to the MV distribution network, delivering power to a shared DC bus. This innovative approach eliminates the need for a step-down transformer to provide low-voltage service by connecting directly to the MV distribution network. Eliminating the low-frequency transformer not only reduces the system footprint and losses but also eliminates inrush currents during grid black-start. Additionally, placing power electronics directly on the distribution system allows for high-bandwidth filtering and power factor correction. The inclusion of a shared DC bus enables multiple charging dispensers and DC storage/generation units to connect, forming a DC microgrid. This setup facilitates power sharing with minimal conversion stages. The project showcases a DC distribution network protected by intelligent solid-state (SS) DC circuit breakers (DCCB) capable of isolating the smallest section of the faulted circuit much faster than existing mechanical solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Improved Subseasonal Forecasting of Extreme Polar Vortices Using Machine Learning

Our research was focused on forecasting the position and shape of the winter stratospheric polar vortex at a subseasonal timescale of 15 days in advance. To achieve this, we employed both statistical and neural network machine learning techniques. The analysis was performed on 42 winter seasons of reanalysis data provided by NASA giving us a total of 6,342 days of data. The state of the polar vortex for determined by using geometric moments to calculate the centroid latitude and the aspect ratio of an ellipse fit onto the vortex. Timeseries for thirty additional precursors were calculated to help improve the predictive capabilities of the algorithm. Feature importance of these precursors was performed using random forest to measure the predictive importance and the ideal number of precursors. Then, using the precursors identified as important, various statistical methods were tested for predictive accuracy with random forest and nearest neighbor performing the best. An echo state network, a type of recurrent neural network that features sparsely connected hidden layer and a reduced number of trainable parameters that allows for rapid training and testing, was also implemented for the forecasting problem. Hyperparameter tuning was performed for each methods using a subset of the training data. The algorithms were trained and tuned on the first 41 years of data, then tested for accuracy on the final year. In general, the centroid latitude of the polar vortex proved easier to predict than the aspect ratio across all algorithms. Random forest outperformed other statistical forecasting algorithms overall but struggled to predict extreme values. Forecasting from echo state network suggested a strong predictive capability past 15 days, but further work is required to fully realize the potential of recurrent neural network approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessing Historical Extreme Weather Event Impacts

Resilience planning, particularly energy and water resilience planning, has been a key priority for the federal government for many years, leading federal agencies to develop processes for identifying and addressing critical resilience gaps at their facilities and sites. Furthermore, recent federal policy is driving agencies to prioritize climate change impacts as a more central component of their resilience planning efforts. To achieve this, federal sites must understand their vulnerability to climate change, which involves identifying climate hazards projected to impact the site (known as exposure), as well as understanding the sensitivity (the degree to which a site, including its people and the things they value, could be harmed by that exposure), and adaptive capacity of the site (the degree to which the site could lessen bathe potential for harm by taking action to reduce exposure and sensitivity). To assess and understand sensitivity and adaptive capacity, it is important to first obtain a baseline and understand how a site has been impacted by past events, in addition to considering the potential for unprecedented impacts based on climate projections. This information paper highlights current limitations for developing event history assessments and suggests a framework for more consistently capturing key data points. The purpose of this paper is to help inform how organizations could begin structuring a comprehensive process for recording the impacts of extreme weather events in order to facilitate climate vulnerability assessments, and thus, resilience planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Report to the President: Extreme Weather Rik in a Changing Climate: Enhancing prediction and protecting communities

The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged with making science, technology, and innovation policy recommendations to the President and the White House. Established by Executive Order, it is an independent Federal Advisory Committee comprised of distinguished individuals from industry, academia, and non-profit organizations with a range of perspectives and expertise. On April 24, 2023, PCAST sent a report to the President with recommendations on how science and technology can help inform and empower communities, businesses, and government agencies to prepare, respond, and adapt to extreme weather.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Multilayer Electrodes with Metalized Polymer Current Collector for High-Energy Lithium-Ion Batteries with Extreme-Fast-Charging Capability

The pursuit of batteries capable of extreme fast charging (XFC), that also satisfy high energy and safety criteria, poses a significant challenge to current lithium-ion battery technologies. Additionally, the increasing demand for aluminum (Al) and copper (Cu) in electrification, and vehicle lightweighting is driving these metals towards near-critical status in the medium term. This study introduced metalized polymer films by depositing an Al or Cu thin layer onto two sides of a polyethylene terephthalate (PET) film – named mPET/Al and mPET/Cu, as lightweight, cost-effective alternatives to traditional metal current collectors in LIBs.

25 ENERGY STORAGE↗