Calorimetric Data Labeling And Intelligent Computing Based On Deep Learning

With the continuous change of the world military revolution,the right to control the sky in the future war will play a major role in the future high-tech war,and hypersonic vehicle technology is one of the key technologies for the struggle to control the sky.As the key technology of hypersonic vehicle design,the thermal protection of hypersonic vehicle is very important for the development of hypersonic vehicle.However,the thermal protection of hypersonic vehicles is still facing many severe challenges,one of which is the prediction of aerodynamic thermal environment.In recent years,the rapid development of deep learning has provided new ideas to solve problems in various fields.Based on the method of deep learning,the traditional method of aerodynamic heat prediction is improved in this paper.The main work is as follows:1.This paper investigates the research progress of aerothermal environment prediction of aircraft at home and abroad,summarizes the main research methods of aerothermal environment prediction at present,analyzes the advantages and disadvantages of various research methods,so as to lay a technical foundation for the methods proposed in this paper.2.This paper studies and implements an automatic labeling algorithm for thermal data based on deep learning.As one of the methods to predict the aerodynamic thermal environment,the test data obtained from the ground wind tunnel test need to be post processed.The existing methods rely on manual processing one by one,which is very time-consuming and has random errors.In order to solve this problem,this paper first designs the preprocessing method of the Calorimetric data to preprocess the original data.Then,a convolutional neural network model based on transfer learning is selected to design an automatic labeling algorithm,which improves the efficiency and accuracy of labeling.In the test process,the automatic labeling algorithm was improved by using the idea of online learning,and finally the labeling effect was further improved.3.This paper studies the intelligent calculation method of aerodynamic thermal environment based on neural network.The thermal safety of hypersonic vehicle is mainly reflected in whether the temperature and stress of the hypersonic vehicle structure exceed the limit.It is necessary to load thermal environment information when calculating the thermal response of the structure.However,the mesh of fluid field and solid field is usually inconsistent,and the mesh mismatch problem becomes one of the key problems to solve the numerical calculation of force / heat / structure multi-fields coupling.In order to solve the problem of global and local conservation of data transfer between different grids,this paper adopts the deep neural network prediction model instead of the traditional numerical calculation to solve the Navier Stokes process.The model can predict the aerodynamic heat at any position on the aircraft surface and solve the problem of grid mismatch.In the process of experiment,the intelligent calculation algorithm is optimized,and the better prediction effect is obtained finally.Experiments show that the two algorithm frameworks proposed in this paper can achieve better results in their respective aero-thermal environment prediction problems.