Theoretical and experimental studies of gas preparation system for pressurization and reactive control system of launch vehicle

A new gas preparation system(GasPS-RCS)is proposed to solve two tasks:(A)to heat helium gas for tank pressurization;(B)to prepare gas for the Launch Vehicle(LV)Reactive Control System(RCS)at the LV orientation and stabilization sections of the LV on passive parts of the flight trajectory,to provide conditions for launching the Liquid Rocket Engine(LRE).The system includes a gas generator based on hydrogen peroxide,a separator for water separation,heat exchangers independent of the LRE,and gas-jet nozzles.The proposed new system allowed to reduce the length of pressurizing gas lines and reduce the increased helium gas consumption during the heat exchanger warm-up interval of the LRE during its launch.A special advantage of the proposed system is the possibility of ground testing of heat exchangers without an operating LRE.A mathematical model based on the first law of thermodynamics was used to perform a comparative analysis of GasPS-RCS with traditional pressurization and RCS systems.To validate the mathematical model,the experimental studies of helium pressurizing of a liquid nitrogen tank were conducted.The results show that the deviation of experimental and calculated values for pressure is 1.1%and for temperature up to 2%.According to the results of comparative analysis,the GasPS-RCS is 259 kg lighter for the first stage and 80 kg lighter for the second stage of the LV.

Predicting saturated vapor pressure of LNG from density and temperature data with a view to improving tank pressure management

Determining the saturated vapor pressure(SVP)of LNG requires detailed thermodynamic calculations based on compositional data.Yet LNG compositions and SVPs evolve constantly for LNG stored in tanks.Moreover,the SVP of the LNG in a tank influences boil-off rates and tank pressure trends.In order to make improved tank pressure control decisions it would be beneficial for LNG tank operators to be made more constantly aware of the SVP of the LNG in a tank.Machine learning models that accurately estimate LNG SVP from density and temperature inputs offer the potential to provide such information.A dataset of five distinct,internationally traded LNG cargoes is compiled with 305 data records representing a range of temperature and density conditions.This can be used graphically to interpolate LNG SVP.However,two machine learning methods are applied to this dataset to automate the SVP predictions.A simple multi-layer perceptron artificial neural network(MLP-ANN)predicts SVP of the dataset with root mean square error(RMSE)=6.34 kPaA and R^(2)=0.975.The transparent open-box learning network(TOB),a regression-free optimized data matching algorithm predicts SVP of the dataset with RMSE=0.59 kPaA and R^(2)=0.999.When applied to infill unknown LNG compositions the superior TOB method achieves prediction accuracy of RMSE~3kPaA and R^(2)=0.996.Predicting LNG SVP to this level of accuracy is beneficial for tank-pressure management decision making.