WeChat Mini Program
Old Version Features

    • 某型氢氧火箭发动机频率特性模块化建模计算分析

    ZHANG Wanxuan,ZHANG Jian,ZHANG Nan

    Journal of Aerospace Power（2022）

    Cited 0|Views7
    Abstract
    为分析某型氢氧火箭发动机频率特性,建立了主、副系统耦合的线性化动态模型:通过一维分布参数模型描述管路特性;采用考虑熵波的绝热流动模型描述燃烧组件特性;推导了转速脉动反馈的传递函数,采用考虑气蚀效应的模型描述泵组件;采取有理近似式表达分布式参数模型和热力组件中的超越函数.在Simulink平台上建立模块化仿真模型,采用扫频法获取频率特性,并利用热试车数据进行验证.结果表明,所开发的模块化仿真库对工程应用十分友好,可用于分析发动机系统低频至中频频率特性;该型发动机在燃气脉动激励下将产生主系统10、165 Hz和副系统124、204 Hz的频率响应;考虑转速脉动反馈计算所得特征频率更加准确;增加泵前管路局部流阻和燃气发生器喷注压降,可提高发动机系统稳定性.
    More
    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