Universality Laws in Pure fluids and Critical point Experiments under Density Gradients

Understanding the behavior of fluids near their critical point is essential for advancing both fundamental physics and fluid dynamics in space. In this project, we analyze image data from microgravity experiments involving oxygen (O2) and sulfur hexafluoride (SF6) to investigate critical phenomena. Two data collection methods were used for the oxygen data set, which was obtained on Earth with magnetic field compensation for gravity to determine the isothermal compressibility and correlation length. The first involves manual sampling of image stacks to measure average light intensity against temperature. We also introduce a novel-to-this-project method of automated sampling using 3D optimization with coefficient of variation thresholds. The methods for the second data set, SF6, involve manually measuring bubble radii of bubbles in both microscopic and macroscopic images of SF6 coalescence behavior in microgravity aboard the International Space Station. We also introduce a promising method of AI-powered image segmentation using an R-CNN (Region-Based Convolutional Neural Network) to automate and make this process more consistent. Finally, data extracted using these processes were used to compute turbidity and measure radii, which, plotted against the changing temperature and time, were used to verify universal power laws. Our results support the viability of using optical imaging to extract thermophysical properties of fluids near their critical point.