Date of Award

Fall 12-2021

Access Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Eric Perrell

Committee Advisor

Yechiel J. Crispin

First Committee Member

R.R. Mankbadi

Second Committee Member

Mark Ricklick

Third Committee Member

Richard Prazenica

Fourth Committee Member

William MacKunis

Abstract

In the present work, a genetic algorithm is used to optimize a hybrid rocket engine in order to minimize the propellant required for a specific mission. In a hybrid rocket engine, the mass flow rate of the oxidizer can be throttled to enhance the performance of the rocket. First, an analysis of the internal ballistics and the ascent trajectory has been carried out for different mass flow rates of the oxidizer as a function of time, for a fixed amount of oxidizer, in order to study the effect of throttling. Two equivalent problems are considered: in the first problem the amount of propellant is fixed, and we are seeking the oxidizer mass flow rate as the function of time such as to maximize the altitude. In the second problem, we obtain the mass flow rate of the oxidizer as a function of time in order to minimize the propellant required to reach a specific altitude. A genetic algorithm is used to find the best mass flow rate of the oxidizer. The optimization is carried out for two different regression rate laws, one depending only on the oxidizer mass flux rate and the other one depending on the mass flux rate of the oxidizer and the fuel. The results obtained in both cases are similar and show that the mass flow rate of the oxidizer should be maximized up to about one-third of the burn time and then decreased gradually. Using this mass flow rate of the oxidizer, we obtain the best initial oxidizer to fuel ratio in order to perform an optimal sizing of the rocket.

Download

Share

COinS