Numerical Simulation of Transonic Flow in Steam Turbine Cascades

Abstract In order to study the effect of initial surface roughness on water droplet erosion resistance of last stage blade substrate of steam turbine, eight 17-4PH samples were grounded and velvet polished by different mesh metallographic sandpaper to establish sample with different initial surface roughness. The water droplet erosion experiments were carried out in the highspeed jet water erosion experiment system, and the mass and micro-morphology of each sample were measured by using precision electronic balance and ultra-depth of field microscope respectively at each experimental stage, and the measurement of water erosion trace width and maximum water erosion depth were also completed at the same time. On the basis of experiments, LS-DYNA was used for numerical simulation to verify the reliability of experimental results again. Results show that the smoother the initial surface of sample, then the smaller the mass loss, the stronger its water erosion resistance. On the contrary, the rougher the initial surface of sample, the more severe the surface irregularity, the more times the water droplets concentrated at the lowest point of pit when water droplets flow laterally after impact is completed, thus accelerating the formation of initial crack and lateral expansion, the poorer the water erosion resistance of sample. At same water erosion time, the smoother the sample surface, the later the complete erosion trace appear, the narrower the water erosion trace width. However, the maximum water erosion depth of sample is not affected by the initial surface roughness. The numerical simulation results are in good agreement with the experimental results.

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Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network

10.1115/gt2021-60247 ◽

2021 ◽

Author(s):

Chongyu Wang ◽

Di Zhang ◽

Yonghui Xie

Keyword(s):

Neural Network ◽

Numerical Simulation ◽

Fault Detection ◽

Steam Turbine ◽

Turbine Rotor ◽

Detection Accuracy ◽

Steam Turbine Rotor ◽

Parallel Misalignment ◽

Coupling Fault ◽

The Impact

Abstract The steam turbine rotor is still the main power generation equipment. Affected by the impact of new energy on the power grid, the steam turbine needs to participate in peak load regulation, which will make turbine rotor components more prone to failure. The rotor is an important equipment of a steam turbine. Unbalance and misalignment are the normal state of rotor failure. In recent years, more and more attention has been paid to the fault detection method based on deep learning, which takes rotating machinery as the object. However, there is a lack of research on actual steam turbine rotors. In this paper, a method of rotor unbalance and parallel misalignment fault detection based on residual network is proposed, which realizes the end-to-end fault detection of rotor. Meanwhile, the method is evaluated with numerical simulation data, and the multi task detection of rotor unbalance, parallel misalignment, unbalanced parallel misalignment coupling faults (coupling fault called in this paper) is realized. The influence of signal-to-noise ratio and the number of training samples on the detection performance of neural network is discussed. The detection accuracy of unbalanced position is 93.5%, that of parallel misalignment is 99.1%. The detection accuracy for unbalance and parallel misalignment is 89.1% and 99.1%, respectively. The method can realize the direct mapping between the unbalanced, parallel misalignment, coupling fault vibration signals and the fault detection results. The method has the ability to automatically extract fault features. It overcomes the shortcoming of traditional methods that rely on signal processing experience, and has the characteristics of high precision and strong robustness.

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