Blunt-nosed sphere-cone aeroshell vehicles have played an integral part in space exploration to date and their use is set to continue into the next decade and beyond. While these vehicles have been flight proven with four decades of heritage, design uncertainties in aft body thermal protection systems in the order of 300-400% exist due to the as yet unpredictable flowfield characteristics of the near-wake region of these vehicles at hypersonic speeds. Attempts have been made to reduce this uncertainty but current technology in the field of computational fluid dynamics (CFD) still requires phenomenological models to be developed that accurately predict base heating rates. Expansion tube experimental facilities have the potential for aiding in the reduction of this design uncertainty. However, it is not known whether the X2 or X3 expansion tubes at the University of Queensland can be employed to obtain data for assisting the development of CFD modelling techniques. A survey of the current best practices in CFD modelling techniques is presented. Preliminary CFD models have been developed to resolve the macroscopic features of the wake flows around the Mars Pathfinder aeroshell geometry. A series of experiments have been performed and the duration of time average steady heating is documented. Results of experimentation indicated that the X2 impulse machine operating in expansion tube mode is capable of generating a sufficient length of steady flow for the study of near-wake phenomena. This is documented in flow conditions using CO2 to simulate Martian flight conditions. It was also demonstrated that flow speeds and model dimensions must be matched appropriately to allow for sufficient test time. A method for the sizing of experimental models is also presented for the study of heat transfer in near-wake flow fields. Example model geometries are prescribed for a variety of flow conditions for the X2 expansion tube.
| Identifer | oai:union.ndltd.org:ADTP/279257 |
| Creators | Adriaan Window |
| Source Sets | Australiasian Digital Theses Program |
| Detected Language | English |
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