ANN-based Chemical Model for Hybrid Rocket Engines

In this paper, the author analyzes the use of a trained artificial neural network to precisely evaluate hybrid rocket engine performance in chemical equilibrium condition, avoiding the use of an high fidelity chemical model. A three-stage small satellite launcher is considered as test case, and the optimization of the engine design and the corresponding ascent trajectory is performed by means of an in-house coupled optimization procedure. The net evaluates specific heat ratio, characteristic velocity and thrust coefficient for given mixture ratio, combustion chamber pressure and nozzle expansion area ratio. The results show that the use of the artificial neural network can improve model accuracy at an affordable computational cost.