In order to improve the quality of decision-making on the protection of aerospace materials in land aviation units, the consumption of training aerospace materials in army aviation units is predicted and analyzed. With an army aviation unit training aerospace material consumption as the main research object, the influence factors of helicopter aerospace material consumption are analyzed, partial least squares regression and Markov chain are used to establish a prediction model of helicopter aerospace material consumption, and through the historical data of aerospace material consumption in helicopter training, aerospace material consumption in helicopter training is predicted, and the results show that the partial least squares regression and Markov chain model, which can predict the consumption of helicopter aerial materials more accurately.
Due to the strong nonlinearity of navier stokes equation, it is difficult to solve the hydrodynamics simulation problem. As a century problem, it is still a major difficulty in the academic community. With the improvement of computer ability and the development of data platform, some new changes have taken place in the research direction and content of turbulence model. The data-driven machine learning method is different from the traditional approximate equation solving method in physics, and shows its application potential in highly complex flow fields. In this study, convolution cyclic hybrid neural network is used to predict the complex flow field, and the generated confrontation network is used to generate the simulated flow field.
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