Aircraft dynamics simulation using a novel physics-based learning method

The fast and accurate prediction of flight trajectories is crucial for the real-time prognostics of the air transportation system. However, the computation costs of simulating aircraft dynamics can be expensive or even prohibitive especially for a large number of aircrafts in the airspace system. This study presents a novel physics-based learning method as a model order reduction (MOR) method for the simulation of aircraft dynamics. The idea of physics-based learning method is to integrate the underlying physics of aircraft dynamical systems into machine learning models to reduce training costs and enhance simulation performances. A recently proposed recurrent neural network (RNN) known as the deep residual RNN (DR-RNN) is used as a tool of physics-based learning. The application of the physics-based learning method is demonstrated on simulating the dynamics of a Boeing 747-100 aircraft. The results show that the DR-RNN can accurately predict aircraft responses and shows excellent extrapolation performances. Furthermore, a purely data-driven approach using the long short-term memory (LSTM) network is also used for the simulation. The comparison demonstrates that incorporating the physics of aircraft dynamics into the learning model can significantly improve prediction performances and effectively reduce training costs compared with using purely data-driven methods. Finally, it is found that the physics-based learning method exhibits superior computation efficiency compared with a classical numerical method since the physics-based learning method can use large time step sizes that violate the numerical stability condition while being explicit in time.