Boiling flow estimation for aero-optic phase screen generation

TL;DR

This paper introduces a modified boiling flow algorithm to efficiently generate synthetic aero-optic phase screen data that matches key statistical properties of measured turbulence data.

Contribution

The authors adapt the boiling flow model to produce anisotropic phase screens and demonstrate its effectiveness in replicating specific statistical features of aero-optic turbulence.

Findings

The modified boiling flow can generate data matching the temporal power spectrum.

It can produce anisotropic 2D structure functions.

The model offers a trade-off between different statistical fidelities.

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.