Abstract: The present invention provides an optimization control method for an aero-engine transient state based on reinforcement learning, and belongs to the technical field of aero-engine transient states. The method comprises: adjusting an existing twin-spool turbo-fan engine model as a model for invoking a reinforcement learning algorithm; to simultaneously satisfy high level state space and continuous action output of a real-time model, designing an Actor-Critic network model; designing a deep deterministic policy gradient (DDPG) algorithm based on an Actor-Critic frame, to simultaneously solve the problems of high-dimensional state space and continuous action output; training the model after combining the Actor-Critic frame with the DDPG algorithm; and obtaining the control law of engine acceleration transition from the above training process, and using the method to control an engine acceleration process.

Abstract: A steady flow prediction method in a plane cascade based on a generative adversarial network is provided. Firstly, CFD simulation experimental data in the plane cascade are preprocessed, and a test dataset and a training dataset are divided from the simulation experimental data. Then, an Encoding-Forecasting network module, a deep convolutional network module and a generative adversarial network prediction model are constructed successively. Finally, prediction is conducted on test set data: the test set data is preprocessed in the same manner, and data dimensions are adjusted according to input requirements of a saved optimal prediction model; and flow field images in the plane cascade at an inlet attack angle of 10° are obtained through the prediction model. The present invention can effectively avoid the problem of limited measurement range of sensors in an axial flow compressor, and the prediction result is highly consistent with the calculation result of CFD.

Abstract: An aero-engine surge active control system based on fuzzy controller switching is provided. The present invention selects a basic controller with the most appropriate current state for switching control according to the operating state of a compressor based on the principle of fuzzy switching, and can realize large-range, adaptive and performance-optimized surge active control. Controllers designed by the present invention realize large-range surge active control through fuzzy switching, so that the effective operating ranges of the controllers are expanded and the reliability of the controllers is improved. The designed controllers can be applied to the active control of surge caused by various causes, so that the adaptability of the controllers is improved and is closer to the actual operating condition of the engine. Some optimization indexes are added in the design process of the controllers, which can realize optimal control under corresponding optimization objectives.

Abstract: A pre-alarming method for rotary stall of compressors based on a temporal dilated convolutional neural network includes firstly, preprocessing dynamic pressure data of an aero-engine, and dividing a test dataset and a training dataset from experimental data; secondly, constructing a temporal convolutional network module, a Resnet-v network module and a temporal dilated convolutional network prediction model in sequence, and saving an optimal prediction model.

Abstract: A method for stability analysis of a combustion chamber of a gas turbine engine based on image sequence analysis belongs to the field of fault prediction and health management of aeroengine. Firstly, flow field data inside a combustion chamber of a gas turbine engine is acquired. Secondly, flow field images of the combustion chamber are preprocessed to respectively obtain a discrimination model data set and a prediction model data set. Then, a 3DWaveNet model is constructed as a generation network of a prediction model. A discrimination network of the module is constructed. The generation network and the discrimination network are combined to form the prediction model. Finally, a discrimination model is constructed according to the discrimination model data set; the training set in the discrimination model data set is used for training, and the test set is used for assessment.

Abstract: 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.