Boiling flow parameter estimation from boundary layer data

TL;DR

This paper presents a method to estimate boiling flow simulation parameters from measured aero-optic phase data, enabling more accurate synthetic turbulence modeling for optical systems affected by atmospheric conditions.

Contribution

The authors develop an efficient algorithm to infer boiling flow parameters from boundary layer data, improving the realism of synthetic phase aberration simulations.

Findings

Temporal power spectral density of synthetic data matches measurements within 8-9% error.

Spatial structure function of synthetic phase screens does not match measurements, with errors above 28%.

Parameters fit temporal statistics well but not complex spatial statistics of aero-optic phase aberrations.

Abstract

Atmospheric turbulence and aero-optic effects cause phase aberrations in propagating light waves, thereby reducing effectiveness in transmitting and receiving coherent light from an aircraft. Existing optical sensors can measure the resulting phase aberrations, but the physical experiments required to induce these aberrations are expensive and time-intensive. Simulation methods could provide a less expensive alternative. For example, an existing simulation algorithm called boiling flow, which generalizes the Taylor frozen-flow method, can generate synthetic phase aberration data (i.e., phase screens) induced by atmospheric turbulence. However, boiling flow depends on physical parameters, such as the Fried coherence length r0, which are not well-defined for aero-optic effects. In this paper, we introduce a method to estimate the parameters of boiling flow from measured aero-optic phase aberration data. Our algorithm estimates these parameters to fit the spatial and temporal statistics of the measured data. This method is computationally efficient and our experiments show that the temporal power spectral density of the slopes of the synthetic phase screens reasonably matches that of the measured phase aberrations from two turbulent boundary layer data sets, with errors between 8-9%. However, the Kolmogorov spatial structure function of the phase screens does not match that of the measured phase aberrations, with errors above 28%. This suggests that, while the parameters of boiling flow can reasonably fit the temporal statistics of highly convective data, they cannot fit the complex spatial statistics of aero-optic phase aberrations.