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
  • [41] A Deep Learning Approach for the Prediction of Retail Store Sales
    Kaneko, Yuta
    Yada, Katsutoshi
    2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW), 2016, : 531 - 537
  • [42] Stock Market Prediction Using a Deep Learning Approach
    Damrongsakmethee, Thitimanan
    Neagoe, Victor-Emil
    PROCEEDINGS OF THE 2020 12TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI-2020), 2020,
  • [43] A Deep Learning Approach for VM Workload Prediction in the Cloud
    Qiu, Feng
    Zhang, Bin
    Guo, Jun
    2016 17TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD), 2016, : 319 - 324
  • [44] A Generalized Deep Learning Approach to Seismic Activity Prediction
    Muhammad, Dost
    Ahmad, Iftikhar
    Khalil, Muhammad Imran
    Khalil, Wajeeha
    Ahmad, Muhammad Ovais
    APPLIED SCIENCES-BASEL, 2023, 13 (03):
  • [45] Deep learning approach for prediction and classification of potable water
    Shri Saroja
    Analytical Sciences, 2023, 39 : 1179 - 1189
  • [46] Hybrid Deep Learning Approach for Traffic Speed Prediction
    Dai, Fei
    Cao, Pengfei
    Huang, Penggui
    Mo, Qi
    Huang, Bi
    BIG DATA, 2024, 12 (05) : 377 - 389
  • [47] MiRTDL: A Deep Learning Approach for miRNA Target Prediction
    Cheng, Shuang
    Guo, Maozu
    Wang, Chunyu
    Liu, Xiaoyan
    Liu, Yang
    Wu, Xuejian
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2016, 13 (06) : 1161 - 1169
  • [48] Telecommunication Services Churn Prediction - Deep Learning Approach
    Karanovic, Mirjana
    Popovac, Milivoje
    Sladojevic, Srdjan
    Arsenovic, Marko
    Stefanovic, Darko
    2018 26TH TELECOMMUNICATIONS FORUM (TELFOR), 2018, : 819 - 822
  • [49] A deep learning approach for port congestion estimation and prediction
    Peng, Wenhao
    Bai, Xiwen
    Yang, Dong
    Yuen, Kum Fai
    Wu, Junfeng
    MARITIME POLICY & MANAGEMENT, 2023, 50 (07) : 835 - 860
  • [50] Automated IT System Failure Prediction: A Deep Learning Approach
    Zhang, Ke
    Xu, Jianwu
    Min, Martin Renqiang
    Jiang, Guofei
    Pelechrinis, Konstantinos
    Zhang, Hui
    2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2016, : 1291 - 1300