Machine Learning Analysis to Characterize Phase Variations in Laser Propagation Through Deep Turbulence
Abstract
The present dissertation is focused on the analysis of the atmospheric conditions of a turbulent environmental system and its effects on the diffraction of a laser beam that moves through it. The study is based on the optical communication of two labs placed at the summit of two mountains located in Maui, Hawaii. The emitter system is located at the Mauna Loa mountain and the receiver at the Haleakala. The distance between both mountains is 150 km. The emitter system is at a height of 3.1 km and the receiver at 3.4 km. The maritime environment at the location experiences continuous atmospheric of turbulence. The turbulence conditions present a series of rotating systems called Eddies, which act as a kind of lens that diffuses light because they change the refractive index of the air, affecting the phase of the laser beam used in the communication. Thus, the objective of this study has been to find a mathematical relation between the level of turbulence, measured by the Reynolds number, and an optical parameter, represented by the structure constant of refractive index Cn2 for measuring the optical disturbance. The profile of temperatures is required to perform the calculations of the Cn2. I lacked experimental data, so I used a CFD simulation software called ANSYS Fluent. Additionally, neural network algorithms were used to fit the temperature data to an equidistant coordinate network. At the end of the study, the positive correlation between Reynolds and Cn2 was obtained and an equation that shows the positive trend between both parameters.
Subject Area
Fluid mechanics|Mechanical engineering|Mathematics
Recommended Citation
Rodriguez Sanchez, Luis Fernando, "Machine Learning Analysis to Characterize Phase Variations in Laser Propagation Through Deep Turbulence" (2020). ETD Collection for University of Texas, El Paso. AAI28002708.
https://scholarworks.utep.edu/dissertations/AAI28002708