Computational Fluid Dynamics (CFD) Simulations of Jet Mixing in Tanks of Different Scales

For long-duration in-space storage of cryogenic propellants, an axial jet mixer is one concept for controlling tank pressure and reducing thermal stratification. Extensive ground-test data from the 1960s to the present exist for tank diameters of 10 ft or less. The design of axial jet mixers for tanks on the order of 30 ft diameter, such as those planned for the Ares V Earth Departure Stage (EDS) LH2 tank, will require scaling of available experimental data from much smaller tanks, as well designing for microgravity effects. This study will assess the ability for Computational Fluid Dynamics (CFD) to handle a change of scale of this magnitude by performing simulations of existing ground-based axial jet mixing experiments at two tank sizes differing by a factor of ten. Simulations of several axial jet configurations for an Ares V scale EDS LH2 tank during low Earth orbit (LEO) coast are evaluated and selected results are also presented. Data from jet mixing experiments performed in the 1960s by General Dynamics with water at two tank sizes (1 and 10 ft diameter) are used to evaluate CFD accuracy. Jet nozzle diameters ranged from 0.032 to 0.25 in. for the 1 ft diameter tank experiments and from 0.625 to 0.875 in. for the 10 ft diameter tank experiments. Thermally stratified layers were created in both tanks prior to turning on the jet mixer. Jet mixer efficiency was determined by monitoring the temperatures on thermocouple rakes in the tanks to time when the stratified layer was mixed out. Dye was frequently injected into the stratified tank and its penetration recorded. There were no velocities or turbulence quantities available in the experimental data. A commercially available, time accurate, multi-dimensional CFD code with free surface tracking (FLOW-3D from Flow Science, Inc.) is used for the simulations presented. Comparisons are made between computed temperatures at various axial locations in the tank at different times and those observed experimentally. The affect of various modeling parameters on the agreement obtained are assessed.