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
  • [21] A Deep Learning Approach to Gene Function Prediction
    McGuire, Cole
    Strunk, Bethany
    Hibbs, Matthew
    JOURNAL OF BIOLOGICAL CHEMISTRY, 2024, 300 (03) : S9 - S9
  • [22] Deep Learning Approach to Link Weight Prediction
    Hou, Yuchen
    Holder, Lawrence B.
    2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 1855 - 1862
  • [23] A Deep Learning Approach for Next Location Prediction
    Fan, Xiaoliang
    Guo, Lei
    Han, Ning
    Wang, Yujie
    Shi, Jia
    Yuan, Yongna
    PROCEEDINGS OF THE 2018 IEEE 22ND INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN ((CSCWD)), 2018, : 69 - 74
  • [24] A Deep Learning Approach for Molecular Crystallinity Prediction
    Sharma, Akash
    Khungar, Bharti
    INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, 2019, 939 : 219 - 225
  • [25] A Deep Learning Approach for Reflow Profile Prediction
    Lai, Yangyang
    Kataoka, Jun
    Pan, Ke
    Ha, Jonghwan
    Yang, Junbo
    Deo, Karthik A.
    Xu, Jiefeng
    Yin, Pengcheng
    Cai, Chongyang
    Park, Seungbae
    IEEE 72ND ELECTRONIC COMPONENTS AND TECHNOLOGY CONFERENCE (ECTC 2022), 2022, : 2269 - 2274
  • [26] A deep learning approach to quasar continuum prediction
    Liu, Bin
    Bordoloi, Rongmon
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2021, 502 (03) : 3510 - 3532
  • [27] A Deep Learning Approach to Flight Delay Prediction
    Kim, Young Jin
    Choi, Sun
    Briceno, Simon
    Mavris, Dimitri
    2016 IEEE/AIAA 35TH DIGITAL AVIONICS SYSTEMS CONFERENCE (DASC), 2016,
  • [28] ST-LSTM-SA: A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning
    Yuan, Hanxiao
    Liu, Yang
    Tang, Qiuhua
    Li, Jie
    Chen, Guanxu
    Cai, Wuxu
    ADVANCES IN ATMOSPHERIC SCIENCES, 2024, 41 (07) : 1364 - 1378
  • [29] ST-LSTM-SA:A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning
    Hanxiao YUAN
    Yang LIU
    Qiuhua TANG
    Jie LI
    Guanxu CHEN
    Wuxu CAI
    AdvancesinAtmosphericSciences, 2024, 41 (07) : 1364 - 1378
  • [30] Stress field prediction in fiber-reinforced composite materials using a deep learning approach
    Bhaduri, Anindya
    Gupta, Ashwini
    Graham-Brady, Lori
    COMPOSITES PART B-ENGINEERING, 2022, 238