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Boiling flow estimation for aero-optic phase screen generation

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

Aero-optic effects due to turbulence can reduce the effectiveness of transmitting light waves to a distant target. Methods to compensate for turbulence typically rely on realistic turbulence data, which can be generated by i) experiment, ii) high-fidelity CFD, iii) low-fidelity CFD, and iv) autoregressive methods. However, each of these methods has significant drawbacks, including monetary and/or computational expense, limited quantity, inaccurate statistics, and overall complexity. In contrast, the boiling flow algorithm is a simple, computationally efficient model that can generate atmospheric phase screen data with only a handful of parameters. However, boiling flow has not been widely used in aero-optic applications, at least in part because some of these parameters, such as r0, are not clearly defined for aero-optic data. In this paper, we demonstrate a method to use the boiling flow algorithm to generate arbitrary length synthetic data to match the statistics of measured aero-optic data. Importantly, we modify the standard boiling flow method to generate anisotropic phase screens. While this model does not fully capture all statistics, it can be used to generate data that matches the temporal power spectrum or the anisotropic 2D structure function, with the ability to trade fidelity to one for fidelity to the other.

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2026 1

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UNVERDICTED 1

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representative citing papers

ReVAR: A Data-Driven Algorithm for Generating Aero-Optic Phase Screens

eess.SP · 2026-04-02 · unverdicted · novelty 7.0

ReVAR generates synthetic aero-optic data matching measured temporal power spectra and statistics better than conventional phase screen methods or single-lag autoregression by combining long-range AR with spatial re-whitening.

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Showing 1 of 1 citing paper.

  • ReVAR: A Data-Driven Algorithm for Generating Aero-Optic Phase Screens eess.SP · 2026-04-02 · unverdicted · none · ref 32 · internal anchor

    ReVAR generates synthetic aero-optic data matching measured temporal power spectra and statistics better than conventional phase screen methods or single-lag autoregression by combining long-range AR with spatial re-whitening.