Solid Propulsion

On the solid propulsion subteam I worked to optimize the team's rocket nozzle. The previous design (24-25 C) was a conical converging-diverging nozzle with converging and diverging half angles of 40 and 15 degrees respectively, and an expansion ratio of 4.49.

To increase performance, I rederived all the required parameters starting from a chamber pressure of 590 PSI from last year's static hotfire and our known grain geometry. I found the mass flow rate, and used it to calculate the throat area. Then I calculated the exit Mach number based on the chamber pressure and ambient pressure (assuming sea level). I then calculated the expansion ratio, and the exit area.

I found an optimal expansion ratio of 5.81, ~30% higher than last year. To test my calculations, I ran two simulations (Figures 1-1 and 1-2) in Ansys Fluent, one with last year's nozzle and one with my redesign (25-26 C), and compared the resultant exit pressure, exit velocity, and mass flow to calculate thrust and specific impulse for each.

Altough gaining around 20N of thrust and 0.1s of ISP (Table 1-1), I continued research and found a series of journal entries written by an aerospace engineer, Gadicharla Rao, in the 1960s, describing a method to increase performance by curving the walls of the diverging section. Using Rao's method, I designed a new "Rao" nozzle based on the previously calculated expansion ratio (25-26 R), and reran the simulations (Figures 1-3 and 1-4).

This time I gained around 70N of thrust and 2.6s of ISP (Table 1-1) over the optimized conical nozzle. Although there is a larger performance gain, the increase in manufacturing complexity might outweigh the small boost in performance.

Once I had the results of the tests, I created CAD models of both of the two new nozzles. They each consist of a graphite throat with a phenolic structure and aluminum nozzle washer. The converging sections are identical, with only the diverging sections differing between the conical and Rao nozzles.

Liquid Propulsion

For liquid propulsion I developed a simulation in Python to find propellant pressure losses and mass flow rates through our piping prior to being injected into the combustion chamber. We are developing our team's first liquid engine, using kerosene as the fuel and nitrous oxide as the oxidizer. The numbers generated by the simulation can be compared to the water cold flow tests, and can be used to predict the performance when we switch to kerosene.

To run the simulation, relvant parameters, such as the injector specifications, propellant characteristics, and pipe elements and their dimensions, as well as the tank pressure and ambient chamber pressure are input (Figure 2-1). The simulation is then run with a specified maximum number of iterations, and it will iterate until the solution has sufficiently converged. Once solved, the results will print to the console (Figure 2-2).

The simulation works by calculating the pressure losses through each element, and comparing the mass flow through the flowmeter and through the injector. The simulation will iteratively vary the mass flow rate of the propellant until continuity is maintained throughout the system. By default it checks for a mass flow difference of less than 1e-6 kg/s, but a different threshold can be sent as an argument to the solve function.

By tuning the characteristics of the piping elements, the simulation can be made to closely approximate the results of actual testing (Table 2-1).