WeChat Mini Program
Old Version Features

    • Adaption of the LHC Cold Mass Cooling System to the Requirements of the Future Circular Collider (FCC)

    C. Kotnig,L. Tavian,G. Brenn

    IOP Conference Series Materials Science and Engineering（2017）

    CERN

    Cited 2|Views4
    Abstract
    The cooling of the superconducting magnet cold masses with superfluid helium (He II) is a well-established concept successfully in operation for years in the LHC. Consequently, its application for the cooling of FCC magnets is an obvious option. The 12-kW heat loads distributed over 10-km long sectors not only require an adaption of the magnet bayonet heat exchangers but also present new challenges to the cryogenic plants, the distribution system and the control strategy. This paper recalls the basic LHC cooling concept with superfluid helium and defines the main parameters for the adaption to the FCC requirements. Pressure drop and hydrostatic head are developed in the distribution and pumping systems; their impact on the magnet temperature profile and the corresponding cooling efficiency is presented and compared for different distribution and pumping schemes.
    More
    Translated text
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined