scholarly journals Theoretical Investigation of Stresses Induced at Blade Mounting Locations in Steam Turbine Rotor System

2019 ◽  
Vol 24 (2) ◽  
pp. 295-307
Author(s):  
D. Kiran Prasad ◽  
K.V. Ramana ◽  
N. Mohan Rao

Abstract One of the most common incipient losses of integrity in mechanical structures is the development and propagation of cracks. Especially in rotating members like steam turbine rotors etc. cracks, because of their potential, cause catastrophic failures and are a grave threat to an uninterrupted operation and performance. A crack may propagate from some small imperfections on the surface of the body or inside of the material and it is most likely to appear in correspondence to high stress concentration. Crack propagation path is generally determined by the direction of maximum stress or by the minimum material strength. Hence determination of stresses induced has been the focus of attention for many researchers. In the present work, development of a mathematical model to determine the stresses induced in a rotating disc of varying thickness is studied. This model is applied to a steam turbine rotor disc to determine the induced stresses and radial deflection. The mathematical modeling results are validated with the results obtained using Ansys package. The results of the present study will be useful in diagnosing the location and magnitude of maximum stress induced in the turbine rotor disc and stress intensity factor due to the presence of crack.

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Vital Kumar Yadav Pillala ◽  
B. V. S. S. S. Prasad ◽  
N. Sitaram ◽  
M. Mahendran ◽  
Debasish Biswas ◽  
...  

AbstractThe paper presents details of a unique experimental facility along with necessary accessories and instrumentation for testing steam turbine cascade blades in wet and nucleating steam. A steam turbine rotor tip cascade is chosen for flow investigations. Cascade inlet flow measurements show uniform conditions with dry air and steam and dry air mixture of different ratios. Exit flow surveys indicate that excellent flow periodicity is obtained. Blade surface static pressure and exit total pressure distributions are also presented with dry air and with steam and dry air mixture of different ratios as the working medium at an exit Mach number of 0.52.


2019 ◽  
Vol 76 ◽  
pp. 263-278 ◽  
Author(s):  
Xuanchen Zhu ◽  
Haofeng Chen ◽  
Fuzhen Xuan ◽  
Xiaohui Chen

2017 ◽  
Vol 46 (3) ◽  
pp. 612-616
Author(s):  
Guo Shirui ◽  
Shang Huichao ◽  
Cui Lujun ◽  
Guo Xiaofeng ◽  
Yao Jianhua

2021 ◽  
Author(s):  
Chongyu Wang ◽  
Di Zhang ◽  
Yonghui Xie

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.


Sign in / Sign up

Export Citation Format

Share Document