Reconstructing shock front of unstable detonations based on multi-layer perceptron

被引:3
|
作者
Zhou, Lin [1 ,2 ]
Teng, Honghui [1 ]
Ng, Hoi Dick [3 ]
Yang, Pengfei [4 ,5 ]
Jiang, Zonglin [4 ,5 ]
机构
[1] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China
[2] Beijing Power Machinery Inst, State Key Lab Laser Prop & Applicat, Beijing 100074, Peoples R China
[3] Concordia Univ, Dept Mech Ind & Aerosp Engn, Montreal, PQ H3G 1M8, Canada
[4] Chinese Acad Sci, Inst Mech, State Key Lab High Temp Gas Dynam, Beijing 100190, Peoples R China
[5] Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
Cellular detonation; Lead shock evolution; Multi-layer perceptron; Numerical simulations; NUMERICAL SIMULATIONS; COMPUTED-TOMOGRAPHY; NEURAL-NETWORK; DYNAMICS; FLAME; MODEL; HYDROGEN; WAVE;
D O I
10.1007/s10409-021-01130-x
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The dynamics of frontal and transverse shocks in gaseous detonation waves is a complex phenomenon bringing many difficulties to both numerical and experimental research. Advanced laser-optical visualization of detonation structure may provide certain information of its reactive front, but the corresponding lead shock needs to be reconstructed building the complete flow field. Using the multi-layer perceptron (MLP) approach, we propose a shock front reconstruction method which can predict evolution of the lead shock wavefront from the state of the reactive front. The method is verified through the numerical results of one- and two-dimensional unstable detonations based on the reactive Euler equations with a one-step irreversible chemical reaction model. Results show that the accuracy of the proposed method depends on the activation energy of the reactive mixture, which influences prominently the cellular detonation instability and hence, the distortion of the lead shock surface. To select the input variables for training and evaluate their influence on the effectiveness of the proposed method, five groups, one with six variables, and the other with four variables, are tested and analyzed in the MLP model. The trained MLP is tested in the cases with different activation energies, demonstrates the inspiring generalization capability. This paper offers a universal framework for predicting detonation frontal evolution and provides a novel way to interpret numerical and experimental results of detonation waves.
引用
收藏
页码:1610 / 1623
页数:14
相关论文
共 50 条
  • [31] Financial Distress Prediction based on Multi-Layer Perceptron with Parameter Optimization
    Bannany, Magdi El
    Khedr, Ahmed M.
    Sreedharan, Meenu
    Kanakkayil, Sakeena
    IAENG International Journal of Computer Science, 2021, 48 (03) : 1 - 12
  • [32] Manufacturing process modeling and optimization based on multi-layer perceptron network
    Louisiana State Univ, Baton Rouge, United States
    J Manuf Sci Eng Trans ASME, 1 (109-119):
  • [33] Multi-layer Perceptron Architecture for Kinect-Based Gait Recognition
    Bari, A. S. M. Hossain
    Gavrilova, Marina L.
    ADVANCES IN COMPUTER GRAPHICS, CGI 2019, 2019, 11542 : 356 - 363
  • [34] MULTI-LAYER PERCEPTRON BASED SPEECH ACTIVITY DETECTION FOR SPEAKER VERIFICATION
    Ganapathy, Sriram
    Rajan, Padmanabhan
    Hermansky, Hynek
    2011 IEEE WORKSHOP ON APPLICATIONS OF SIGNAL PROCESSING TO AUDIO AND ACOUSTICS (WASPAA), 2011, : 321 - 324
  • [35] A Reliable Localization Algorithm Based on Grid Coding and Multi-Layer Perceptron
    Sun, Zhengtang
    Zhang, Yang
    Ren, Qianqian
    IEEE ACCESS, 2020, 8 (60979-60989) : 60979 - 60989
  • [36] Intrinsic Plasticity Based Inference Acceleration for Spiking Multi-Layer Perceptron
    Zhang, Shuxun
    Zhang, Anguo
    Ma, Yupeng
    Zhu, Wei
    IEEE ACCESS, 2019, 7 : 73685 - 73693
  • [37] Multi-Layer Perceptron Based Spectrum Prediction in Cognitive Radio Network
    Singh, Amit Kumar
    Ranjan, Rakesh
    WIRELESS PERSONAL COMMUNICATIONS, 2022, 123 (04) : 3539 - 3553
  • [38] Monotonic multi-layer perceptron networks as universal approximators
    Lang, B
    ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS, 2005, 3697 : 31 - 37
  • [39] Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification
    He, Xin
    Chen, Yushi
    REMOTE SENSING, 2021, 13 (17)
  • [40] Geno-mathematical identification of the multi-layer perceptron
    Ralf Östermark
    Neural Computing and Applications, 2009, 18 : 331 - 344