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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 433 records · Page 24

Miscellaneous Electric Loads: Characterization and Energy Savings Potential

Over time, miscellaneous electric loads (MELs) are expected to increase both in magnitude and share of residential and commercial building energy consumption. This trend is most apparent in North America, but it is also occurring in Japan and Europe. However, the contribution of MELs to building energy use is not currently well understood, both because the products in this category are transforming rapidly and the definition and classification of MELs is ambiguous. This study estimated the national energy consumption of 36 MELs using best-available data and found them to comprise 12% of delivered electricity to the U.S. residential and commercial building sectors. If 26 of these MELs were replaced with the most energy-efficient product models available on the market, their energy consumption could be halved to 6% of delivered electricity. National energy models will better account for building energy consumption by incorporating the MELs data collected and analyzed for this study, leading to improved policy decisions.

Miscellaneous electric loads, Plug loads, Taxonomy↗

Methodologies for Design, Characterization and Testing of Electrolytes that Enable Extreme Fast Charging of Lithium-ion Cells

Selection, testing and validation of electrolyte candidates for Li-ion cells are discussed, based on a 10-minute target for extreme fast charge (XFC). A combination of modeling and laboratory measurements create a timely and synergistic approach to identifying candidate electrolyte formulations. Multi-solvent systems provide a balanced set of properties, wherein lower molecular-weight solvents offer reduced viscosity, increased species diffusivity, and mitigation of concentration polarization at high charge rates. Carefully selected formulations can exhibit peak conductivity and usable conductivity range of two to three times that of the baseline EC-EMC (3:7, wt.) + LiPF 6 . Candidates are also chosen based on stability and longevity within the cell environment. Lab testing coincides with property predictions from the Advanced Electrolyte Model (AEM) and a macro-scale cell model. Furthermore, cell testing utilized coin and pouch cells having NMC532 or NMC811 cathodes with graphite electrodes. Results indicate combinations of low-molecular weight solvents are key for fast-charge electrolytes as they extend the useful conductivity range to both low and higher salt concentrations, and possess higher self-diffusivities compared to conventional solvents. This reduces impacts from concentration polarization. The choice of electrolyte also influences the tendency for lithium metal deposition at the anode, as showcased by experimental and modeling results herein.

25 ENERGY STORAGE↗

Performance characterization of scientific workflows for the optimal use of Burst Buffers

Scientific discoveries are significantly rely on the analysis of large volumes of data from observations and simulations of complex phenomena. Scientists compose the complex analyses as workflows and execute them on large-scale HPC systems. The workflow structures are in contrast with monolithic single simulations that have often been the primary use case on HPC systems. At the same time, new storage paradigms such as Burst Buffers are becoming available on HPC platforms. In this paper, we analyze the performance characteristics of a Burst Buffer and two representative scientific workflows with the aim of optimizing the usage of a Burst Buffer, extending our previous analyses (Daley et al., 2016). Our key contributions are (a) developing a performance analysis methodology pertinent to Burst Buffers, (b) improving the use of a Burst Buffer in workflows with bandwidth-sensitive and metadata-sensitive I/O workloads, (c) highlighting the key data management challenges when incorporating a Burst Buffer in the studied scientific workflows.

97 MATHEMATICS AND COMPUTING↗