Dalian University of Technology

Hongyang Zhao

Ximing Sun

Yanhua Ma

Fuxiang Quan

Pan Qin

Zhuocheng Jiang

The present invention relates to a prediction method for stall and surge of an axial compressor based on deep learning. The method comprises the following steps: firstly, preprocessing data with stall and surge of an aeroengine, and partitioning a test data set and a training data set from experimental data. Secondly, constructing an LR branch network module, a WaveNet branch network module and a LR-WaveNet prediction model in sequence. Finally, conducting real-time prediction on the test data: preprocessing test set data in the same manner, and adjusting data dimension according to input requirements of the LR-WaveNet prediction model; giving surge prediction probabilities of all samples by means of the LR-WaveNet prediction model according to time sequence; and giving the probability of surge that data with noise points changes over time by means of the LR-WaveNet prediction model, to test the anti-interference performance of the model.

Prediction method for stall and surge of axial compressor based on deep learning

A vehicle capable of taking off and landing vertically and operating in water, land, air and submarine environments includes a fuselage, two main wings, ailerons, a vertical tail, a rudder, a horizontal tail, elevators, a propeller, rotor wings, rotor wing supports, etc. The vehicle has the advantages of adaptability to various environments, good concealment and strong survivability. Compared with a traditional unmanned rotorcraft, the vehicle has longer endurance time and larger load. Compared with a fixed wing UAV, the vertical take-off and landing function makes the work more convenient. Compared with unmanned diving equipment, the vehicle is applicable to richer environments, and can complete designated missions in air, land, water and submarine environments. Compared with a tilt rotor UAV in water, land, air and submarine environments, the vehicle is rapider in switching of various modes and is higher in stability.

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Liaoning, China

Hongyang Zhao