Young-Suk Jung

Helium Quantity Estimation for LOx Tank Pressurization of a Restartable Pressure-fed Propulsion System

In a cryogenic propellant tank the pressurant is contracted due to heat loss and the propellant itself evaporates. On a restartable propulsion system such phenomena are more intensive because the propellant contacts with the pressurant on the larger surface during the coast flight. Such heat and mass transfer phenomena should be considered for estimating the amount of pressurant. On the hypothesis that the heat and mass transfer quasi-equilibrium is achieved during the coast flight, the calculation process of the equilibrium pressure is presented. On the process the amount of loaded helium on the Falcon-1 second stage is calculated.

Thrust and propellant mixture ratio control of open type LPRE using Q-ILC

LPRE(Liquid Propellant Rocket Engine) is one of the important parts to control the motion of rocket. For operation of rocket in error boundary of the set-up trajectory, it is necessary to control the thrust of LPRE according to the required thrust profile and control the mixture ratio of propellants fed into combustor for the constant mixture ratio. It is not easy to control thrust and mixture ratio of propellants, since there are co-interferences among the components of LPRE. In this paper, the dynamic model of LPRE was established, dynamic characteristics were analyzed with control system as PI control and PI+Q-ILC(Iterative Learning Control with Quadratic Criterion) control. From the analysis, it was confirmed that PI+Q-ILC control logic is more useful than simple PI control for control of LPRE.

Iterative learning control for the liquid rocket propulsion systems

This paper addressed mathematical modeling and control of liquid rocket propulsion system (LRPS). Dynamic simulation of LRPS shows that control difficulty is rooted from the slow dynamics and high nonlinearity of the rocket motion. As a consequence a conventional feedback control technique is unsuitable for controlling the rocket motion. In this paper, it is proposed to use both cascade and iterative learning control techniques including feedback controllers. Using the cascade control algorithm it is enabled to overcome the control difficulty to a certain degree caused by the variation of time constants. Iterative learning control strategies improve the tracking performance and guarantee the safety through repetition. The LRPS control system which tracks the changes in the set-point and also diminishes the disturbance under modeling error has been synthesized. Overall performance of proposed control system has been demonstrated by means of thorough numerical simulation of LRPS control system.