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Study Probability Distribution Method in Turbulent Flow

PROBABILITY DISTRIBUTION METHOD IN TURBULENT FLOW RESEARCH. Study Probability Distribution Method in Turbulent Flow. Author: Michael Stefik , Faculty Advisor: Ryo S. Amano – Professor Computational Fluid Dynamics and Wind Tunnel Lab, University of Wisconsin Milwaukee, Milwaukee, WI, USA.

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Study Probability Distribution Method in Turbulent Flow

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  1. PROBABILITY DISTRIBUTION METHOD IN TURBULENT FLOW RESEARCH Study Probability Distribution Method in Turbulent Flow Author: Michael Stefik, Faculty Advisor: Ryo S. Amano – Professor Computational Fluid Dynamics and Wind Tunnel Lab, University of Wisconsin Milwaukee, Milwaukee, WI, USA Abstract In a high-temperature air flow on an aluminum surface, aluminum is oxidized into alumina (Al2O3), which tends to agglomerate into molten phase and breaks up into droplets. These agglomerates can stick to the inside of the rocket nozzle and damage the chamber wall . The focus of the current research is to study new methods of characterizing the two-phase liquid flow phenomena and how this two-phase flow effects the Al2O3 agglomerates. This would be achieved by using a time average volume contour of the two-phase flow and then converting the volume contour into spatial distribution probability data. The simulation will be run with the STAR-CCM+8.04 program to analyze the probability break up of water particles when the two-phase flow model is tested at different wind velocities. Motivation Probability Distribution Model Pre-hydraulic Jump Angles Alumina (Al2O3) agglomerate on the chamber wall in a high-temperature air flow channel. Simulation Conditions The 1 meter channel in Figure 1, has a .2 mm inlet where a 30mm thick layer of liquid flows. A ramp with a height of 15 mm and an angle of 20 degrees is placed at a distance of .159 m from the inlet. Inlet creates flow separation of liquid. The initial liquid velocity is kept at .67m/s and the initial air velocity is kept at 40 m/s. Conclusions Based on the Probability Distribution Model, Super positioning images of the Two-Phase flow is an accurate way of estimating the probability distribution. Adding multiple frames proves to be the most accurate way of determining the average position of the Two-Phase Boundary. The RSM model provides the most accurate data. Because the RSM model can account for the non-isotropic turbulence , a more complex flow prediction can be determined. Moving forward, experiments will need to be conducted to apply the RSM model’s predictions to find out how the experimental results compare to the computer simulated probability break-up models. Time Averaging Boundary Length Break up droplet size may be similar, but it is hard to determine the average break up size. Superposition is introduced to analyze the time averaged contour. Acknowledgment Thank you to Ryo Amano, Michael Hammen, and Yi Hsin Yen for assisting in obtaining the computational models and data.

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