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Acta Optica Sinica, Volume. 40, Issue 7, 0720001(2020)

Particle Image Velocimetry Based on a Lightweight Deep Learning Model

Changdong Yu1、**, Xiaojun Bi2、*, Yang Han3, Haiyun Li1, and Yunfei Gui3
Author Affiliations
  • 1College of Information and Communication Engineering, Harbin Engineering University,Harbin, Heilongjiang 150001, China
  • 2College of Information and Engineering, Minzu University of China, Beijing 100081, China
  • 3College of Shipbuilding Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (0)
    References (18)
    Cited By (2)
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    Paper Information
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    Figures & Tables(11)
    Structure of the LiteFlowNet

    Fig. 1. Structure of the LiteFlowNet

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    Improved NetE structure

    Fig. 2. Improved NetE structure

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    Depth separable convolution

    Fig. 3. Depth separable convolution

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    Velocity fields and vorticity maps of DNS turbulent flow estimated by different models

    Fig. 4. Velocity fields and vorticity maps of DNS turbulent flow estimated by different models

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    RMSE estimated by different models for turbulent image

    Fig. 5. RMSE estimated by different models for turbulent image

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    Histogram comparison of velocity distributions estimated by different models

    Fig. 6. Histogram comparison of velocity distributions estimated by different models

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    Number of parameters for different models

    Fig. 7. Number of parameters for different models

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    • Table 1. Improved NetC network structure parameters

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      Table 1. Improved NetC network structure parameters

      Layer nameKernelStrideRepeat timesOutput resolution
      conv132132, 256, 256
      conv2_132132, 128, 128
      conv2_231132, 128, 128
      conv2_331132, 128, 128
      conv3_1/dw32132, 64, 64
      conv3_1/sep12164, 64, 64
      conv3_2/dw31164, 64, 64
      conv3_2/sep11164, 64, 64
      conv4_1/dw32164, 32, 32
      conv4_1/sep12196, 32, 32
      conv4_2/dw32196, 32, 32
      conv4_2/sep12196, 32, 32
      conv5/dw32196, 16, 16
      conv5/sep121128, 16, 16

    • Table 2. Types of motion fields included in the PIV dataset

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      Table 2. Types of motion fields included in the PIV dataset

      Case nameDescriptionQuantity
      UniformUniform flow1000
      Back-stepBackward stepping flow3200
      CylinderVortex shedding flow over a circular cylinder2050
      DNS-turbulenceAhomogeneous and isotropic turbulence flow2000
      SQGSeasurface flow driven by a Surface Quasi-Geostrophic model1500
      JHTDB-channelChannel flow provided by Johns Hopkins Turbulence Databases1600
      JHTIDB-mhd1024Forced MHD turbulence provided by JHTIDB800
      JHTIDB-isotropic1024Forced isotropic turbulence provided by JHTIDB2000

    • Table 3. Test errors on DNS

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      Table 3. Test errors on DNS

      ModelRMSE
      TrainTest
      LiteFlowNet0.22500.2300
      LiteFlowNet-en0.07100.0730
      LiteFlowNet-HD0.06800.0682

    • Table 4. Computation time of different models

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      Table 4. Computation time of different models

      ModelTime /msNumber of vectors
      LiteFlowNet24.74256×256
      LiteFlowNet-en46.54256×256
      LiteFlowNet-HD41.98256×256
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    Changdong Yu, Xiaojun Bi, Yang Han, Haiyun Li, Yunfei Gui. Particle Image Velocimetry Based on a Lightweight Deep Learning Model[J]. Acta Optica Sinica, 2020, 40(7): 0720001

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    Paper Information

    Category: Optics in Computing

    Received: Nov. 14, 2019

    Accepted: Dec. 26, 2019

    Published Online: Apr. 15, 2020

    The Author Email: Yu Changdong (heu_yuchangdong@163.com), Bi Xiaojun (bixiaojun@hrbeu.edu.cn)

    DOI:10.3788/AOS202040.0720001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology