Date of Award
Master of Science (MS)
Aerospace, Physics, and Space Sciences
Gnana Bhaskar Tenali
David C. Fleming
Characterizing spray flows has been an issue of interest for years, particularly in regards to fuel injection in engines. Droplet velocity and diameter, among other characteristics, are crucial to understanding spray flows. One approach for determining these quantities in a spray is using a statistical approach that solves for the moments of droplet characteristics as they evolve in space and time. A theoretical probability density function (PDF) can be formulated using various combinations of moments which evolve according to derived moment transport equations (thus evolving the PDF), using the principle of maximum entropy as closure to the system. Building upon previous work completed, this paper performs error analysis on thousands of moment combinations that have been calculated to determine which moments are crucial to reducing the error of a theoretical PDF relative to an experimental PDF. The ultimate goal is to determine which higher-order moments are sufficiently important to be tracked through moment transport equations rather than solving for the evolution of all moments of a given order with such equations. It is determined that most of the third-order moments are equally important, while only 〈𝑈4〉,〈𝐷2𝑉2〉, and 〈𝑈2𝑉2〉 and 〈𝐷4𝑈〉, 〈𝐷4𝑉〉, and 〈𝑉5〉 are the important fourth and fifth-order moments, respectively.
Myers, Amanda Lynn, "Optimization of High-Order Statistical Moment Combinations to Best Represent Spray Flow Particle Probability Density Functions" (2023). Theses and Dissertations. 1356.