A deep learning approach for the velocity field prediction in a scramjet isolator

被引:65
|
作者
Kong, Chen [1 ]
Chang, Juntao [1 ]
Li, Yunfei [1 ]
Wang, Ziao [1 ]
机构
[1] Harbin Inst Technol, Harbin 150001, Heilongjiang, Peoples R China
基金
中国国家自然科学基金;
关键词
DIODE-LASER ABSORPTION; SUPERRESOLUTION RECONSTRUCTION; NEURAL-NETWORKS; SHOCK; MODEL; SPECTROSCOPY; VELOCIMETRY; FLOWS;
D O I
10.1063/5.0039537
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
The accurate parameter prediction of a flow field is of practical significance to promote the development of hypersonic flight. Velocity field prediction using deep learning is a promising method to provide an accurate velocity field in a scramjet isolator. A new approach for the velocity field prediction in a scramjet isolator is developed in this study. A data-driven model is proposed for the prediction of the velocity field in a scramjet isolator by convolutional neural networks (CNNs) using measurements of the pressure on the isolator. Numerical simulations of flow in a three-dimensional scramjet isolator at various Mach numbers and backpressures are carried out to establish the dataset capturing the flow mechanism over various operating conditions. A CNN architecture composed of multiple reconstruction modules and feature extraction modules is designed. The CNN is trained using the computational fluid dynamics dataset to establish the mapping relationship between the wall pressure on the isolator and the velocity field in the isolator. The trained model is then tested over various Mach numbers and backpressures. The data-driven model successfully learns the relationship between the velocity field and pressure experienced on the wall of the isolator, i.e., the trained CNN model successfully reconstructed the velocity field based on the wall pressure on the isolator with high accuracy.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] A deep learning approach for velocity field prediction in a scramjet isolator from Schlieren images
    Chen KONG
    Ziao WANG
    Yunfei LI
    Juntao CHANG
    Chinese Journal of Aeronautics, 2023, 36 (11) : 58 - 70
  • [2] A deep learning approach for velocity field prediction in a scramjet isolator from Schlieren images
    Chen KONG
    Ziao WANG
    Yunfei LI
    Juntao CHANG
    Chinese Journal of Aeronautics , 2023, (11) : 58 - 70
  • [3] A deep learning approach for velocity field prediction in a scramjet isolator from Schlieren images
    Kong, Chen
    Wang, Ziao
    Li, Yunfei
    Chang, Juntao
    CHINESE JOURNAL OF AERONAUTICS, 2023, 36 (11) : 58 - 70
  • [4] Flood Velocity Prediction Using Deep Learning Approach
    LUO Shaohua
    DING Linfang
    TEKLE Gebretsadik Mulubirhan
    BRULAND Oddbj?rn
    FAN Hongchao
    Journal of Geodesy and Geoinformation Science, 2024, 7 (01) : 59 - 73
  • [5] An intelligent prediction method for supersonic flow field in scramjet isolator enhanced by feature details
    Tian, Ye
    Zhao, Yitong
    Deng, Xue
    Yang, Maotao
    Chen, Erda
    Xu, Mengqi
    Ren, Hu
    AEROSPACE SCIENCE AND TECHNOLOGY, 2025, 161
  • [6] Deep-learning prediction and uncertainty quantification for scramjet intake flowfields
    Fujio, Chihiro
    Ogawa, Hideaki
    AEROSPACE SCIENCE AND TECHNOLOGY, 2022, 130
  • [7] A deep learning approach for vehicle velocity prediction considering the influence factors of multiple lanes
    Xu, Mingxing
    Lin, Hongy
    Liu, Yang
    ELECTRONIC RESEARCH ARCHIVE, 2022, 31 (01): : 401 - 420
  • [8] Supersonic combustion flow field reconstruction in a scramjet based on deep learning method
    Huang, Shicai
    Tian, Ye
    Deng, Xue
    Yang, Maotao
    Chen, Erda
    Zhang, Hua
    AEROSPACE SCIENCE AND TECHNOLOGY, 2025, 161
  • [9] A machine learning approach for the prediction of settling velocity
    Goldstein, Evan B.
    Coco, Giovanni
    WATER RESOURCES RESEARCH, 2014, 50 (04) : 3595 - 3601
  • [10] Fast flow field prediction over airfoils using deep learning approach
    Sekar, Vinothkumar
    Jiang, Qinghua
    Shu, Chang
    Khoo, Boo Cheong
    PHYSICS OF FLUIDS, 2019, 31 (05)