Specific launch trajectory inclinations are selected to optimize fuel margins and payload insertion.
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Peer-reviewed literature confirms that launch vehicle trajectory optimization algorithms are specifically designed and selected to guarantee payload orbit insertion while minimizing fuel consumption.
Abstract This paper presents an online trajectory optimization algorithm for launch vehicles based on convex programming to ensure flight safety in case of power system fault. Due to high complexity of the power system, the engine may break down during the flight, causing significant decrease of thrust or energy. In this case, the nominal trajectory will be infeasible as the dynamical model and energy state is different from the normal status, thus the online trajectory optimization and re-planning are considered. For different kinds of engine failures, different terminal orbital constraints are proposed. When the mass flow rate of fuel decreases, the energy loss is little but the dynamical model changes obviously, so the location of the injection point cannot be guaranteed. In this case, the terminal orbital elements are constrained except the true anomaly, so that the payload of launch vehicles can still settle into the nominal orbit, and the true anomaly is optimized for minimum fuel consumption. As for the energy-loss failure, the strategy to change the target orbit is proposed considering the requirement of launch mission and subsequent orbit transfer insertion. The terminal constraints are proposed analytically in this paper. In order to solve the nonconvex trajectory optimization problem accurately and rapidly, the optimization problem is transformed into convex optimization problems by various convexification techniques, including the lossless convexification and successive convexification. Finally, the high efficiency and accuracy of the proposed algorithm is verified by numerical experiments. The algorithm proposed in this paper has potential applications in onboard trajectory optimization and re-planning of launch vehicles in case of power system fault to ensure the accomplishment of the launch mission.
Hybrid rocket engines are a green alternative to solid rocket motors and may represent a low-cost alternative to kerosene fueled rockets, while granting performance and control features similar to that of typical storable liquid rocket engines. In this work, the design of a three-stage hybrid launcher is optimized by means of a coupled procedure: an evolutionary algorithm optimizes the engine design, whereas an indirect optimization method optimizes the corresponding ascent trajectory. The trajectory integration also provides the vertical emission profiles required for the evaluation of the environmental impact of the launch. The propellants are a paraffin-based wax and liquid oxygen. The vehicle is launched from the ground and uses an electric turbo pump feed system. The initial mass is given (5000 kg) and the insertion of the payload into a 600-km circular, and polar orbit is considered as a reference mission. Clusters of similar hybrid rocket engines, with only few differences, are employed in all stages to reduce the development and operational costs of the launcher. Optimization is carried out with the aim of maximizing the payload mass and then minimizing the overall environmental impact of the launch. The results show that satisfactory performance is achievable also considering rocket polluting emissions: the carbon footprint of the launch can be reduced by one fourth at the cost of a 5-kg payload mass reduction.
Agile Earth observation can be achieved with responsiveness in satellite launches, sensor pointing, or orbit reconfiguration. This study presents a framework for designing reconfigurable satellite constellations capable of both regular Earth observation and disaster monitoring. These observation modes are termed global observation mode and regional observation mode, constituting a reconfigurable satellite constellation (ReCon). Systems engineering approaches are employed to formulate this multidisciplinary problem of co-optimizing satellite design and orbits. Two heuristic methods, simulated annealing (SA) and genetic algorithm (GA), are widely used for discrete combinatorial problems and therefore used in this study to benchmark against a gradient-based method. Point-based SA performed similar or slightly better than the gradient-based method, whereas population-based GA outperformed the other two. The resultant ReCon satellite design is physically feasible and offers performance-to-cost(mass) superior to static constellations. Ongoing research on observation scheduling and constellation management will extend the ReCon applications to radar imaging and radio occultation beyond visible wavelengths and nearby spectrums.
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