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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 1,153 records · Page 64

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DOE OSTI2021

Development of an Extreme Fast Charging Battery

The objective of the project is to research, develop, design, fabricate, and demonstrate extreme fast charging (XFC) Li-ion cells capable of a 10 min charge to deliver 180Wh/kg at the beginning of life (BoL) and sustain >500 XFC cycles with less than 20% capacity loss. The PI developed an asymmetric temperature modulation (ATM) charging strategy that charges a Li-ion cell at an elevated temperature of ~60oC and discharges at the ambient temperature. The elevated charging temperature eliminates Li plating, while the limited exposure time to 60oC effectively controls materials degradation, thereby enabling XFC with remarkable battery life. We developed and demonstrated 209 and 271 Wh/kg cells, both of which exceed the afore-mentioned target of delivering 180Wh/kg at BoL and sustaining 500+ XFC cycles with less than 20% loss.

25 ENERGY STORAGE↗

DOE OSTI2021

A Quantum-AI Framework for Extreme Weather Prediction

The frequency and intensity of extreme weather events in North America will likely increase with a changing climate. High resolution simulations are necessary to advance process-based understanding of weather events at local scales accompanying future changes in global atmospheric circulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

AI for Extreme Volcanic Climate Forcing and Feedback Forecasting in the 21st century

Focal Areas: Our paradigm-shifting framework will apply machine learning and perform physical analysis of contemporary volcanoes to develop sound forecasts of extreme eruptions in the 21st century and their abrupt drying and cooling impacts on the warming climate. We will also bridge climate data driven regression models and earth system model output to trace teleconnections that exacerbate regional impacts such as Arctic amplification and western US droughts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Integrating Models with Real-time Field Data for Extreme Events: From Field Sensors to Models and Back with AI in the Loop

Focal Area(s): This whitepaper is responsive to focal area (1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). We discuss Artificial Intelligence and Machine Learning (AI/ML) enabled integration of real-time data into the extreme event modeling workflow to improve the predictive capabilities of these models, and deliver real-time feedback to remote sensors, including software and data engineering challenges.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Knowledge-Guided Machine Learning (KGML) Platform to Predict Integrated Water Cycle and Associated extremes

Focal Area(s): Predictive modeling through the use of AI techniques and insight gleaned from complex data (both observed and simulated). Science Challenge: Although advanced predictive capabilities of the water cycle are critical to address environmental needs and develop sustainable solutions for energy demands, there is no robust framework, to say the least, that seamlessly integrates local to intermediate to global scales and a gamut of biogeophysical information to enhance understanding of the integrated water cycle and its associated extremes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Learned implicit representations of aerosol chemistry and physics for enhancing the predictability of water cycle extreme events

Focal Area(s): Focal area 2: Predictive modeling through the use of AI-derived model components; and Focal area 3: physics-guided AI. Science Challenge: Comprehensive models of many geophysical processes require a large number of state variables, disqualifying them for inclusion in Earth System Models (ESMs) for the foreseeable future. This white paper proposes the paradigm shift from the use of state variables representing explicit physical quantities to the use of machine-learning to create and integrate a compact, implicit representation of a physical system with a smaller number of state variables. As an initial application, we propose to create parsimonious machine-learned surrogate models of gas- and aerosol-phase chemistry and physics with the purpose of improving the accuracy of the representation of cloud formation and cloud-aerosol interaction in the E3SM model. This type of improved representation of cloud microphysics is critical for enhancing the predictability of water cycle extremes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events

Climate and earth system models have limited skill in predicting water cycle and associated extremes such as intense rain- and wind-producing storms and resulting hazards across all timescales. Water cycle predictions are inherently limited by the predictability of the chaotic system, but large potential for improving predictions remains untapped due to limited use of data-model integration to improve predictive modeling. This white paper proposes a hybrid data-physics paradigm based on physics-informed neural networks (PINNs) to improve the Energy Exascale Earth System Model (E3SM). In particular, we aim to predict and quantify uncertainties of water cycle processes and low probability high impact events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Predictive Understanding of Compound and Cascading Extremes and Their Impacts

We seek to answer the following questions: What interactions across spatial and temporal scales drive multivariate compound and cascading water cycle extremes, and how can we leverage understanding of such interactions to improve predictive understanding of these events and quantification of their impacts?

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Robust data-driven uncertainty quantification in water cycle extreme predictions

Climate experts apply global climate models to predict the future climate, informing decisions and adaptations. Uncertainty plays a fundamental role in this process (particularly for the water cycle simulation, the most relevant but least well-simulated). For many aspects of the water cycle there are disagreements for future climate projections, reflecting the significant uncertainty in model formulation. Also, the chaotic nature of climate dynamics, as well as indeterministic future projection emission scenarios, contribute to the uncertainty in climate predictions. New AI methods that are beginning to be adopted by the climate community have the power to improve predictions of water cycle extremes. The crucial question is how to capture all sources of uncertainty in a systematic way in order for the predictions from the AI models to be maximally leveraged by decision makers

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Transfer Operator Framework for Earth System Predictability and Water Cycle Extremes

For chaotic dynamical systems, nonlinear instabilities lead to exponentially divergent trajectories in the evolution of system states. Unless a simulation is initialized with an infinite-precision snapshot of the state of the true system and all known physical effects that go into its evolution are directly computed, the future state predicted by the simulation will quickly diverge from that of the true system. Moreover, the Earth system is highly structured and contains localized coherent structures that are particularly important to predict. Predicting extreme events associated with coherent structures, like hurricanes and blocking events, is crucial for understanding the effects of global warming on the water cycle.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Transfer Operator Framework for Earth System Predictability and Water Cycle Extremes

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

New High-Energy & Safe Battery Technology with Extreme Fast Charging Capability for Automotive Applications (Final Technical Report)

Being able to refill a cars gas tank when low on fuel is incredibly convenient and is a lacking feature for battery elective vehicles. New High-Energy & Safe Battery Technology with Extreme Fast Charging Capability for Automotive Applications was a research project designed to help correct this lacking feature by developing automotive sized Li-ion battery cells and demonstrating their ability to charge in under 10-minutes. To address the technical challenges Microvast, a cell manufacturer, was joined by Argonne National Labs, battery technology innovator, and BMW, a renown OEM, worked together to define and validate materials and cells for this project. By projects end, Microvast had successfully built and demonstrated a 240 Wh/kg, 35 Ah pouch cell that was capable of over 800 10-minute charge, full discharge cycles. From the outset of the project the plan was to use large amp-hour cells (ie automotive relevant sizes) to ensure the project findings and outcomes were most relevant for electric vehicle applications. The manufacture of cells was done using test manufacturing lines at Microvast and BMW that closely resemble the procedures of a Li-ion battery cell factory.

25 ENERGY STORAGE↗

DOE OSTI2021

EpiCast: Simulating Epidemics with Extreme Detail

In early 2020, COVID-19 swept the globe. Governments attempted to “flatten the curve” through business shutdowns and stay-at-home orders, but the United States was hit hard. By the end of March, mere months after the virus first emerged in humans 7,000 miles away, the U.S. had recorded 192,300 cases and 5,300 deaths. While this unprecedented disaster sent shockwaves through every level of society and clouded an uncertain future, state and local governments turned to computational and mathematical epidemiology researchers to help formulate intervention strategies to limit the spread of the disease. Traditional forecasting models provided a reasonable understanding of how the near future was likely to look, but local policy makers and public health communities still struggled to understand how potential mitigations ought to be implemented. Decision makers needed a way to measure the impact of their policy choices—they needed better technology. EpiCast answered the call, bringing urgently needed answers to policymakers grappling with how to adjust school and business schedules. EpiCast is modeling software that generates a synthetic, representative population to simulate infectious disease transmission in the United States with extreme detail and granularity. The software models human behavior combined with community-specific information to provide a fine-grained preview of the effect of potential mitigation strategies for decision makers.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Sensitivity and Uncertainty Analysis of Generator Failures under Extreme Temperature Scenarios in Power Systems

This report summarizes work done under the Verification, Validation, and Uncertainty Quantification (VVUQ) thrust area of the North American Energy Resilience Model (NAERM) Program. The specific task of interest described in this report is focused on sensitivity analysis of scenarios involving failures of both wind turbines and thermal generators under extreme cold-weather temperature conditions as would be observed in a Polar Vortex event.

17 WIND ENERGY↗

DOE OSTI2021

Highly Ordered Hierarchical Anodes for Extreme Fast Charging Batteries (Final Report)

The goal of this project is to enable extreme fast charging (XFC) of Li-ion batteries, which was accomplished through a combination of 1) rational design and manufacturing of hierarchically structured anode architectures; 2) blending graphite/hard carbon into a bulk hybrid anode; 3) engineering artificial solid-electrolyte interphase (SEI) coatings with reduced interphase impedance; 4) computational modeling of coupled transport, kinetic, and electrochemical phenomena; and 5) improved fundamental understanding of lithium plating through operando analysis. This work integrated structural, compositional, and surface modification of graphite anodes, multi-physics modeling of ion transport, electrochemical activity, and heat transfer, advanced strategies to detect Li plating, and semi-automated roll-to-roll cell assembly. The unique facilities at the University of Michigan (UM) and Sandia National Laboratories (SNL) were leveraged to manufacture, prototype, and characterize commercially relevant >2Ah and >180 Wh/kg cells with a target of <20% capacity fade over 500 XFC cycles.

25 ENERGY STORAGE↗

DOE OSTI2021

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) (Final Report)

This report details the accomplishments from the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows” under the award numbers FWP-66406 and DE-SC0012630, with a focus on the UC San Diego (Award No. DE-SC0012630) part of the accomplishments. We refer to the project as IPPD. The main activities of IPPD were centered on the development and integration of provenance information to capture empirically workflow information and identify the sources of bottlenecks as well as variability, and modeling and simulation to provide insights and predictively explore multiple scenarios for development of advanced techniques for optimization of resources and workflow execution. After a Phase I of the project, a Phase II effort focused 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. The project leveraged and extended our existing tools with new research and demonstrated our work on the Belle II workflow suite as well as on workflows from NSLS-II.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Nanostructured Composite Alloys for Extreme Environments

Accumulative Roll Bonding was used to process nanolayered Cu/Nb and Zr/Nb composites with varying individual layer thickness. Mechanical tests show remarkable strength as the average layer thickness decreases. Relatively high strength is preserved even at elevated temperatures. Despite differences in texture between rolling paths (longitudinally rolled-RL and cross rolled-CR) Cu/Nb, flow stresses between the resultant materials were similar. In addition, thermal conductivity does not change significantly as the interface density increases. The alloys show remarkable resistance to irradiation damage at very high doses due to stability of the interfaces. The results on Zr/Nb indicate that liquid phase chemistry resulting from thermal spikes play an important role and should be taken into account when designing alloys for extreme environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

The Multi-scenario Extreme Weather Simulator: Energy Resilience for Mission Assurance

The Multi-scenario extreme weather simulator (MEWS) is a stochastic weather generation tool. The MEWS algorithm uses 50 or more years of National Oceanic and Atmospheric Association (NOAA) daily summaries [1] for maximum and minimum temperature and NOAA climate norms [2] to calculate historical heat wave and cold snap statistics. The algorithm takes these statistics and shifts them according to multiplication factors provided in the Intergovernmental Panel on Climate Change (IPCC) physical basis technical summary [3] for heat waves.

54 ENVIRONMENTAL SCIENCES↗