Jiaming Zhou , Longlei Dong,  Guirong Yan

DOI Number XXX-YYY-ZZZ

Conference Number HiSST 2018-540926

With the purpose of improving the validity and practicality of ground test data for flight vehicle design, a mapping model of the same structure under different boundary conditions is employed to predict the dynamic responses in the time domain. Unfortunately, the mapping model is difficult to solve by traditional mathematical methods. Therefore, a new approach is proposed by using Recurrent Neural Networks (RNNs) combined the Gated Recurrent Unit (GRU). This method is validated on different structures: a three-degree-of-freedom system and a thin plate. The training dataset is generated by numerical computation methods. The predicted results illustrate that this method has good learning and data prediction ability for stationary and non-stationary time sequences with 20% noise. The applications of this method in the aerospace field will be more mature and have bright prospects in the future.

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In Categories: Aircraft Design, High-Speed Aerodynamics and Aerothermodynamics, HiSST 2018, Hypersonic Aerodynamics
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