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The next generation of reusable launch vehicles (RLVs) require significant improvements in guidance methods in order to reduce cost, increase safety and flexibility, whilst allowing for possible autonomous operation. Research has focused on the ascent and hypersonic re-entry flight phases. This thesis presents a new method for trajectory design, optimisation and guidance of RLVs during the terminal area flight phases. The terminal area flight phase is the transitional phase from hypersonic re-entry to the approach and landing phase. The trajectory design, optimisation and guidance methods within this thesis are an evolution of previous work conducted on the ascent and re-entry flight phases of RLVs. The methods are modified to incorporate the terminal area flight phase through the adaption of the problem definition and the inclusion of the speed brake setting as a steering parameter. The methods discussed and developed in this thesis are different to previous methods for the terminal area flight phase as they encompass optimisation, trajectory design and guidance based on the definition of the steering parameters. The NLPQL nonlinear optimiser contained within the International Mathematics Standards Library (IMSL) is utilised for trajectory design and optimisation. Real-time vehicle guidance is achieved using the restoration steps of an accelerated Gradient Projection Algorithm (GPA). The methods used are evaluated in a three degrees of freedom (3DOF) simulation environment. To properly evaluate the programs and gain a better understanding of the terminal area flight phase, two different vehicles are utilised within this study. These vehicles are the German sub-orbital Hopper concept vehicle, a previously proposed replacement for the Ariane series of launch vehicles and the recently cancelled joint National Aeronautics and Space Administration (NASA) and Lockheed Martin sub-orbital test bed vehicle, X-33. The two vehicles each have a terminal area flight phase, but their mission profiles and vehicle characteristics are significantly different. The Hopper vehicle is a winged re-entry vehicle, whereas the X-33 vehicle is a lifting body. The trajectory design method takes into account the initial and final conditions, in-flight restrictions such as dynamic pressure and vehicle loads as well as safety margins. The designed trajectories are evaluated to analyse the terminal area flight phase and to assist in the development of the guidance program. The guidance method is evaluated utilising an program consisting of two parts, a real world simulator with high order models and a representation of the on-board guidance computer, the predictor, which uses low order models for computational efficiency. The guidance method is evaluated against a variety of off-nominal conditions to account for dispersions within the high order real world models and common errors experienced by re-entry vehicles. These off-nominal conditions include atmospheric disturbances, winds, aerodynamic, mass, navigation, steering and initial condition errors. The results of this study include a detailed analysis of the terminal area flight phase highlighting the major influences for vehicle and trajectory design. The study also confirms the applicability of the non-linear programming method utilising the vehicle steering parameters as a viable option for trajectory design and guidance. A comparison to other available results highlights the strengths and weaknesses of the proposed method. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1282342 / Thesis (Ph.D.)--School of Mechanical Engineering, 2007.

During development of a complex system, feasibility initially overshadows other concerns, in some cases leading to a design which may not be viable long-term. In particular for the case of Reusable Launch Vehicles, Operations&Maintenance comprises the majority of the vehicle's LCC, whose stochastic nature precludes direct analysis. Through the use of simulation, probabilistic methods can however provide estimates on the economic behavior of such a system as it evolves over time. Here the problem of operations optimization is examined through the use of discrete event simulation. The resulting tool built from the lessons learned in the literature review simulates a RLV or fleet of vehicles undergoing maintenance and the maintenance sites it/they visit as the campaign evolves over a period of time. The goal of this work is to develop a method for uncovering an optimal operations scheme by investigating the effect of maintenance technician skillset distributions on important metrics such as the achievable annual flight rate and maintenance man hours spent on each vehicle per flight. Using these metrics, the availability of technicians for each subsystem is optimized to levels which produce the greatest revenue from flights and minimum expenditure from maintenance.