Advancing Particle Track Imaging with Machine Learning Applications for Modern Modular Bubble Chamber
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The Columbia Basin Consulting Group (CBCG) is developing an advanced reactor cooled by Lead-Bismuth (Pb-Bi). The design is being developed for a high level of passive safety. It has the advantages of the classical liquid metal (sodium) reactor, i.e. small size and passive safety, while having the additional advantage of chemically inert coolant.
We will develop a tunable pulsed electron source capable of generating variable intensity highly-focused electron pulses at high repetition rates. Specifically, we need to demonstrate a high-speed variable electron gun capable of generating a variable intensity electron pulse containing between 102 and 105 electrons with an accelerating voltage of 1kV focused into a spot size <100nm in diameter at with a pulse rate >10MHz (with path to 300MHz). This combined performance cannot be achieved by commercially available electron sources. The primary technology path selected for this work will be to use a photocathode approach, where an easily modulated UV laser is used to stimulate electron emission. The approach utilizes the advances made in the development of the Dynamic TEM (DTEM), though this project will be simpler in that the pulse intensity is lower and there is no requirement for coherency. Alternate paths, including using a traditional electron beam modulated using a v-shaped slit and electrostatically deflecting the beam to adjust intensity; will also be considered for future capabilities. The electron source will then be used generate optically stimulated luminescence (OSL) in lithium fluoride (LiF) substrates to demonstrate the potential for OSL to serve as a basis technology for next generation data storage systems.
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.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
EDGE is a bioinformatics platform developed in 2016 by researchers at Los Alamos National Laboratory (LANL) to facilitate the analysis of next-generation sequencing data by researchers with varying levels of experience in bioinformatics (Li et al., 2017). Users with single-end, paired-end or long-read sequencing data can provide their reads as input to EDGE and select the combination of workflows to run that are most useful for their research (e.g., quality control of reads, genome assembly, or the taxonomic classification of input reads). Table 1 summarizes the modules available in EDGE. EDGE is available as a web platform at https://edgebioinformatics.org, as installable source code maintained on GitHub under a GPLv3 license, and as a publicly hosted Docker image.