Integrated optimization of a three-stage clustered hybrid rocket launcher using neural networks

In this paper, the integrated optimization of a hybrid launch vehicle is analyzed for a nominal mission to a 500 km polar circular orbit. A single hybrid rocket engine unit is reused across the whole launch vehicle, with each stage constituted by a cluster of a specified number of units. Only the nozzle exit diameter of the units is allowed to change across each stage. This clustered approach is aimed at reducing the costs of the launch vehicle and at simplifying the optimization procedure. After a brief mission analysis based on Tsiolkovsky's equation, a three-stage configuration is chosen for the launch vehicle, employing 16, 4, and 1 engine units for, respectively, first, second, and third stage. A neural network-based surrogate model is employed to approximate the complex hybrid rocket internal ballistics, with the aim to reduce the computational cost of the optimization process. The surrogate model is trained to map a reduced number of design parameters to the performance and mass budget of a single engine unit using data from a 0-D hybrid rocket engine model. The accuracy of the trained network in predicting crucial features is then assessed. The trained neural network is then integrated into a multi-disciplinary optimization process. The aim is to identify the optimal unit design and launch vehicle ascent trajectory which maximize the payload capacity to the target orbit.