Re-usable Single-Stage-To-Orbit (SSTO) vehicles represent a promising alternative to conventional expendable rocket launchers, since they will be capable of taking off from a conventional runway, delivering a payload to orbit and gliding back to their spaceport before preparing for re-launch. SSTOs are foreseen to reduce drastically the current costs of access to space and to increase the diversity of practical and economic space applications. The design of aircraft-like launchers is hampered by a myriad of design challenges, however. The coupling between the behaviour of their various systems challenges conventional aircraft design practices and requires that a detailed cross-disciplinary and systemic modelling approach be applied early on in their evolution toward a workable prototype. This dissertation focuses on the development of efficient algorithms and modelling strategies for the purpose of the multidisciplinary design and optimization of the next generation of fully reusable aircraft-like launch systems. The approach followed is to represent the vehicle as an interconnected system which can then be discretized into a series of constituent components. The resulting multidisciplinary design environment combines the use of a new reduced-order aerothermodynamic model, specifically conceived to provide a predictive accuracy suitable for preliminary design, with a series of tools that have been developed to model some of the critical components of SSTOs. This modelling environment can be used to predict the overall performance, mass and trajectory of the vehicle, to concurrently size the active and passive thermal shields, organize the internal configuration of the vehicle, and evaluate the performance of the propulsive device. A number of design applications and validations are provided to support the relevance of this approach to the modelling of the characteristics of the next generation of space-access vehicles.
|Date of Award||1 May 2015|
- University Of Strathclyde
|Sponsors||EPSRC (Engineering and Physical Sciences Research Council)|
|Supervisor||Richard Brown (Supervisor) & Qing Xiao (Supervisor)|