The development, testing, and use of liquid propellant and hybrid rocket propulsion systems for spacecraft and their launch vehicles routinely involves the use of cryogenic propellants. These propellants provide high energy densities that enable high propulsive efficiency and high engine thrust to vehicle weight ratios. However, use of cryogenic propellants also introduces technical problems not associated with other types of propellants. One of the major technical problems is the phenomenon of propellant tank pressurant and ullage gas collapse. This collapse is mainly caused by heat transfer from most of the ullage gas to tank walls and interfacing propellant, which are both at temperatures well below those of this gas. Pressurant gas is supplied into cryogenic propellant tanks in order to initially pressurize these tanks and then to maintain required pressures as propellant is expelled from these tanks. The cryogenic propellants expelled from the tanks feed rocket engine assemblies, subassemblies, and components at required interface pressures and mass flow rates. The net effect of pressurant and ullage gas collapse is increased total mass and mass flow rate requirements of pressurant gases. For flight vehicles this leads to significant and undesirable weight penalties. For rocket engine component and subassembly ground test facilities this results in high construction and operational cost impacts. Accurate predictions of pressurant gas mass transfer and flow rate requirements are essential to the proper design of systems used to supply these gases to cryogenic propellant tanks. While much work has been done in the past for predicting these gas requirements at low subcritical tank pressures, very little has been done at supercritical tank pressure conditions and there are selected cases where errors of analytical predictions are high. The objectives of this study are to develop a new generalized and improved computer program to determine pressurant gas requirements at both subcritical and supercritical tank pressure conditions, and then evaluate and validate the consistent accuracy of this program over a wide range of conditions by comparison of program results to empirical data.

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