Toward automated design of Combined Cycle Propulsion

• Main Author: Mogavero, Alessandro
• Published: University of Strathclyde 2016
• Subjects: 629.47
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690353

One means to reduce both the cost and the risk associated with space missions is to employ a vehicle designed within the re-usable, airliner-like 'space plane' paradigm. Key to the practicality of such vehicles is the further development of Combined Cycle Propulsion technology. In this thesis, a new engineering tool called the HYbrid PRopulsion Optimizer (HyPro) is presented, with the aim of analysing the performance of diverse engine concepts. The tool is conceived to be modular and flexible, and makes use of parsimonious modelling, in order to describe the engine at an high level of abstraction and to be fast in execution. A configurational optimizer has also been developed in order to automatically generate new design concepts, optimizing the engine cycle structure. It is conceived to be used at the beginning of development in order to perform an automatic and objective trade-off of possible propulsion solutions. In this work the model has been implemented for Rocket-Based Combined Cycle, and it has been verified and validated against analytical models, computational fluid dyanamic analyses and experimental data. The design proposed by the optimizer in these conditions was coherent with manually designed Combined Cycle Propulsion engines, demonstrating the HyPro's capability to converge on good solutions. The results, although preliminary, are very promising and represent a novelty in the field, since a configurational optimization, in the field of propulsion concepts, has never been attempted before. The results presented here demonstrate that the configurational optimization of engine design is viable. The next steps to produce a practical optimizer, which delivers robust and innovative engine solutions, are the addition of modelling capabilities beyond the Rocket-Based Combined Cycle and analysis discipline beyond the pure performances.