Cogeneration and waste heat recovery and utilization are important means to improve energy utilization, which opens up a broad application prospect for steam turbines. Digital electro-hydraulic control system is responsible for the speed and load control of the unit, and its control performance directly affects the safety and economy of the unit. The purpose of this paper is to study the heat transfer coefficient of high-power steam turbine impeller rim with deep neural network. In this paper, the surface emission coefficient of high-power turbine rotor blades is measured by power method. Then, the scraping characteristics of the rotor blade and ceramic coating of the high-power turbine box were experimentally studied, focusing on the effects of different scraping depth, scraping rate and disc linear speed on the scraping temperature and scraping force. According to the influence of process and nonlinearity on system stability, a neural network PID controller is designed to replace the traditional PID controller, to reduce the influence of nonlinearity on the control system, and to realize the adaptive adjustment of controller parameters. In this paper, three algorithms are selected to calculate the heat transfer coefficient of turbine wheel rim under different working conditions. The influence of grid number on the average tip temperature of steam turbine is studied and analyzed. The experiment shows that the average blade tip temperature of the impeller tends to be stable after the grid number is more than 4.47 million.

Morozov Sae. Heat Transfer Coefficient of Impeller Rim of High-power Steam Turbine Based on Deep Neural Network. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 1: 1-8. https://doi.org/10.38007/KME.2022.030101.

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