Chinese Optics Letters, Volume. 23, Issue 2, 020101(2025)

Atmospheric turbulence time-evolving modeling using spatio-temporal fractal nature [Invited]

Haiyang Fu1,2,3, Fangxiang Wang1,2、*, Wei Chen1,2,3、**, Shuang Wang1,2,3、***, Deyong He1,2,3, Zhenqiang Yin1,2,3, and Zhengfu Han1,2,3
Author Affiliations
  • 1CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China
  • 2CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
  • 3Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
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    Figures & Tables(7)
    Modification of spatial interpolation algorithm; left: origin, right: after modification. (a) PS after 5 iterations of interpolation for Δr = 0.3125 mm and r0 = 0.1 m (64π initial phase has been added in the red box area). (b) Ratio between the mean SF Dϕ (ρ)r at each sampling point of 104 PSs and the theoretical value.
    Schematic illustration of spatial interpolation. {m′, n′} are grid coordinates, and Δr is the grid spacing. (The solid circles are from the initial PS, the shaded circles are interpolated first, and then the dotted circles are interpolated.)
    Bases and coefficients of time-evolving PSs. (a) Complete orthogonal bases Ui of the 8 × 8 grid. (b) Temporal PSDs of different coefficients. (c) Time sequence of different coefficients for the 32 × 32 grid, V = 5 m/s, r0 = 0.1 m, and Δr = 0.01 m.
    Spatial characteristics of PSs. (a) Spatial interpolation process of the PS: (a1) initial PS, (a2)–(a5) 1 iteration, 2 iterations, 3 iterations, and 4 iterations. (b), (c) Ratio between the mean SF Dϕ (ρ)r of 104 PSs and theoretical value for r0 = 0.1 m. (b1)–(b3) Different grids, 16 × 16, 32 × 32, 64 × 64, and L = NΔr = 0.08 m; (c1)–(c3) different iterations of interpolation, 1 iteration, 5 iterations, and 9 iterations. (d), (e) Fluctuations of the ratio of the statistical value to the theoretical value of the SF across all distances in PSs. The error bar represents one standard deviation. (d) Different numbers of 16 × 16 PSs; (e) 104 PSs after different iterations of interpolation.
    Temporal characteristics of PSs, V = 5 m/s, r0 = 0.1 m. (a1)–(a6) Time-evolving PS before temporal interpolation, Δt = 2 ms. (b1)–(b6) Time-evolving PS after temporal interpolation, Δt = 0.5 ms. (c)–(e) Mean temporal PSDs of 103 sets of interpolated time-evolving PS sequences. (c) Different iterations of spatial interpolation. (d) Different iterations of temporal interpolation. (e) Simultaneous temporal and spatial interpolation. Is and It are the iterations of spatial and temporal interpolation, respectively.
    The comparison between our method and existing methods. (a) The comparison of deviations from SF theoretical value. (b) The comparison of the temporal PSD when the crosswind is 5 m/s.
    The AoA temporal power spectrum in the 460 m atmospheric turbulence channel with r0 = 3.7 cm. (a) The field-test results[47]. (b) Our simulation results.
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    Haiyang Fu, Fangxiang Wang, Wei Chen, Shuang Wang, Deyong He, Zhenqiang Yin, Zhengfu Han, "Atmospheric turbulence time-evolving modeling using spatio-temporal fractal nature [Invited]," Chin. Opt. Lett. 23, 020101 (2025)

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    Paper Information

    Category: Atmospheric, Oceanic, Space, and Environmental Optics

    Received: Mar. 30, 2024

    Accepted: Aug. 8, 2024

    Published Online: Mar. 6, 2025

    The Author Email: Fangxiang Wang (fxwung@ustc.edu.cn), Wei Chen (weich@ustc.edu.cn), Shuang Wang (wshuang@ustc.edu.cn)

    DOI:10.3788/COL202523.020101

    CSTR:32184.14.COL202523.020101

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